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ISBN print: 978-3-0364-0217-8
ISBN eBook: 978-3-0364-1217-7
DOI book: 10.4028/b-3Zcq2j
© 2026 Trans Tech Publications Ltd, All Rights Reserved

Engineering Innovations Volume 19

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Volume 19 of
Engineering Innovations
ISSN print 2813-0995
ISSN web 2813-1002

Full text available online at https://www.scientific.net/book/engineering-innovations-vol-19/978-3-0364-1217-7

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Table of Contents

Excellent UV-Shielding Performance of PVA Film Using Methanol Extracted Longevity Spinach Leaves (Gynura Procumbens)

Zikri Zikri, Hairul Abral, Meifal Rusli, Melbi Mahardika

Al Tools for Plasma Diagnostics by X-Ray Imaging and Spectroscopy in the PANDORA Project Frame

Bianca Peri, Eugenia Naselli, Giorgio Finocchiaro, Bharat Mishra, Angelo Pidatella, Richárd Rácz, Sandor Biri, David Mascali

Recent Developments of the VOXES Von Hamos X-Ray Spectrometer for Laboratory XES and XAS Studies

Simone Manti, Alessandro Scordo

Beamlines of the EuPRAXIA@SPARC_LAB X-Ray FEL Facility

Federico Nguyen

Laser Shock Peening with Acoustic Emission In-Situ Monitoring for Quality Control

James M. Griffin, Pratik Shukla, Rui Qin, Paul Butler Smith

Improved Performance Assessment of Photovoltaic Systems Using Paraffin-Based Phase Change Materials and Innovative Container Designs

Zainal Arifin, Ubaidillah Ubaidillah, Wibawa Endra Juwana, Rendy Adhi Rachmanto, Eflita Yohana, Denny Widhiyanuriyawan, Noval Fattah Alfaiz, Singgih Dwi Prasetyo, Mohd Afzanizam Mohd Rosli

Numerical Study of Four Hydrokinetic Savonius Turbines in a Staggered Co-Rotating Configuration in the Cooling Water Channel

Mohammad Riko Putra Utama, Triyogi Yuwono

Numerical Study of Four Hydrokinetic Savonius Turbines in an In-Line Square Co-Rotating Configuration in the Cooling Water Channel

Daniel Ardian Setiyanto, Triyogi Yuwono

Numerical Analysis of Defect-Induced Stress and Deformation in a Progressive Cavity Pump Stator Elastomer

Dwi Meilianto, Rachmat Sriwijaya

Bayesian Strategy for the Volume Accuracy of Ink Droplets Continuous Estimation during Printing Based on Prior Knowledge with Earth Mover's Distance

Deyi Kong, Jian Kui Chen, Jia Cong Xiong

The Printing of Highly Uniform Display Device by Graph Convolutional Neural Network-Based Manufacturing

Jia Cong Xiong, Jian Kui Chen, Zi Wei Zhao

Effect of Surface Roughness on the Friction Moment in a Ball Bearing

Harsh Kumar, Mayank Tiwari

A Study on the Effects of Cam Eccentricity and Cylinder Phase Angle on the Output Performance of a Two Cylinder Stirling Engine

Chin Kuei Lin, Shie Chen Yang, Wen Ko Liang, Yi Xiang Wang

Auxiliary Lubrication Effects of Polyolefin Resin and Porous Polymer Materials Used in the Linear Slide Rails

Hsiang Yu Wang, Yuh Ping Chang, Por Cheng Huang, Chun Yi Kuo, Jin Chi Wang

Keywords Index

Authors Index

The original version of this eBook is available on https://www.scientific.net/book/engineering-innovations-vol-19/978-3-0364-1217-7

Engineering Innovations, ISSN: 2813-1002, Vol. 19, pp 1-8

doi: 10.4028/p-JA1TrO

© 2026 The Author(s). Published by Trans Tech Publications Ltd, Switzerland.

Submitted:
2026-02-07

Revised:
2026-02-28

Accepted:
2026-02-28

Online:
2026-06-11

Excellent UV-Shielding Performance of PVA Film Using Methanol Extracted Longevity Spinach Leaves (Gynura Procumbens)

The original version of this paper is available on https://www.scientific.net/EI.19.1.pdf

Zikri 1,2,a, Hairul Abral 1,3, b*, Meifal Rusli 1,c, Melbi Mahardika 4,5, d

¹Department of Mechanical Engineering, Universitas Andalas, Padang 25163, West Sumatera, Indonesia

2 Department of Automotive Engineering, Universitas Muhammadiyah Riau, Pekanbaru, Riau, Indonesia

3 Laboratory of Nanoscience and Technology, Department of Mechanical Engineering, Universitas Andalas, Padang 25163, West Sumatera, Indonesia

4 Research Collaboration Center for Nanocellulose, BRIN and Andalas University, Padang, 25163, Indonesia

5 Research Center for Biomass and Bioproducts, National Research and Innovation Agency of Indonesia (BRIN), Cibinong, 16911, Indonesia

a  zikri@umri.ac.id, b  habral@yahoo.com, c  meifal@eng.unand.ac.id, d  melbi.mahardika@brin.go.id

*Corresponding author: habral@yahoo.com

Keywords: composite films, longevity spinach leaf extract, optical properties, methanol extraction, photosensitive packaging.

Abstract

Developing composite films with UV-absorbing qualities is crucial for protecting lightsensitive products, particularly in food and pharmaceutical packaging. This study created a polyvinyl alcohol (PVA)-based film by integrating LSE to achieve tailored ultraviolet (UV) protection. Extraction of LSE through methanol maceration resulted in high total phenolic ( 38.59±0.15mgGAE/g ) and flavonoid ( 91.26±0.99mEQ/g ) contents, both of which are known to absorb UV radiation. The composite films were prepared using a solution molding method and characterized for UV-visible transmission. Pure PVA films exhibited poor UV blocking ability but high visual transparency, while PVA/LSE films provided perfect 100%UV protection up to 416 nm with reduced clarity. PVA/LSE films showed excellent UV attenuation while maintaining good transparency. Films combining PVA and LSE showed synergistic benefits, facilitating a balance between UV protection and transparency. These results demonstrate the potential of PVA/LSE composites as environmentally friendly functional materials for UV-sensitive food and pharmaceutical packaging applications.

Introduction

The original version of this paper is available on https://www.scientific.net/EI.19.1.pdf

The integration of UV-protective characteristics into polymer-based packaging materials is a current research focus due to the detrimental effects of ultraviolet (UV) radiation on light-sensitive goods, particularly in the food and pharmaceutical industries [1]. Ultraviolet exposure significantly affects the nutritional value and quality of food, degrades active ingredients, reduces shelf life, and produces undesirable changes in color, odor, and characteristics [1,2]. Therefore, there is growing interest in packaging materials made from polymers that can block UV light while maintaining important physical attributes such as safety, flexibility, and transparency.
Recent years have witnessed a transition towards sustainable materials, driving the advancement of biodegradable polymers as replacements for traditional petroleum-based plastics, including polylactic acid (PLA), chitosan, cellulose, and polyvinyl alcohol (PVA) [3,4,5,6]. PVA has become a significant choice among these biopolymers due to its superior film-forming capacity, water solubility, biodegradability, non-toxicity, and excellent optical clarity [7, 8, 9]. The poor UV resistance of PVA limits its direct application in packaging for light-sensitive goods [10, 11]. Ultraviolet radiation can cause photodegradation, reduce mechanical integrity, and compromise the
stability of packaged products, especially in pharmaceutical formulations [1]. Therefore, improving the UV protection effectiveness of PVA films, while maintaining their optical clarity, is an important area of research.
Phenolic and flavonoid bioactive compounds from plants exhibit inherent UV absorption capabilities [12, 13]. The use of natural active components in active packaging has been extensively researched. Extracts from nigella sativa [14], moringa [15], betalain-rich cactus fruits [16], and Hibiscus Sabdariffa L. [17] have been shown to offer UV protection. LSE is a renewable, natural, and inexpensive additive that can function as a nature-based UV absorber. Furthermore, LSE has the ability to interact synergistically with PVA, enhancing UV protection while reducing transparency loss. Despite their potential, previous research on natural additives in PVA films has largely concentrated on their antioxidant or antibacterial properties, without addressing their optical shielding qualities. Furthermore, limited research has examined the combined effects of inorganic UV absorbers and plant-derived extracts in a single film matrix.
This study aims to develop and analyze PVA-based composite films infused with LSE as a natural UV inhibitor, and to evaluate their efficacy. The synergistic potential of PVA and LSE will be investigated to evaluate their effectiveness in enhancing UV-blocking capacity and optical clarity. The integration of these two chemicals is expected to facilitate tailorable optical characteristics, providing a balance between optimal transparency and complete UV protection. This study advances active biodegradable packaging by incorporating a naturally derived, sustainable UV absorber alongside an inorganic UV inhibitor, potentially protecting foods and pharmaceuticals from UVinduced degradation.

Methods

The original version of this paper is available on https://www.scientific.net/EI.19.1.pdf

Materials

The original version of this paper is available on https://www.scientific.net/EI.19.1.pdf

Fresh longevity spinach leaves (Gynura Procumbens) were obtained from Lubug Begalung, Padang, West Sumatera, Indonesia. PVA with a molecular weight of 89,000−98,000 g/mol,99% hydrolysis was obtained from Sigma-Aldrich Pte. Ltd. (Singapore). Methanol ( 96% ) for the extraction process and distilled water were provided by PT. Sari Kimia Semesta Kencana, Padang, Indonesia.

Preparation of LSE

The original version of this paper is available on https://www.scientific.net/EI.19.1.pdf

Extraction of longevity spinach leaf was carried out using the maceration method with 96% methanol as the solvent. This method was chosen because of its simplicity, cost efficiency, and proven effectiveness in extracting active compounds in leaves including phenolics and flavonoids [18, 19]. Fresh leaves were first cleaned to remove surface dirt, then dried by the wind and pureed with a blender. The ratio of leaf material to solvent was 1:10 (w/v). The mixture is placed in a dark-colored bottle to minimize light-induced degradation, sealed tightly, and stored at room temperature for 7 days, with periodic stirring to enhance solvent-solute interaction. Afterwards, the mixture was filtered with filtration paper to separate the extract liquid from the leaf residue. Finally, all the macerated liquid fractions concentrated using a rotary evaporator (IKA RV8) at 50∘C and 120 rpm to remove the methanol and obtain the LSE.

Determination of Total Phenolics and Flavonoids Content of LSE

The original version of this paper is available on https://www.scientific.net/EI.19.1.pdf

The total phenolics content (TPC) of the LSE was determined based on the Folin-Ciocalteu (FC) method [19]. Each sample ( 1mg/mL ), 0.2 mL was mixed with 0.5 mL of FC reagent (diluted 1:10 with distilled water) and 0.8 mL of sodium bicarbonate solution ( 7.5%w/v ). The mixture was allowed to rest at room temperature for 30 min , with intermittent agitation. The absorbance of the mixture was subsequently quantified at a wavelength of 765 nm utilizing a UV-Vis spectrophotometer (Shimadzu UV 1800, Japan). A standard gallic acid solution was used, and the TPC value on LSE was obtained based on the calibration curve of gallic acid. Results are expressed as GAE (gallic acid equivalents) or the equivalent amount in mg gallic acid per gram of extract. The total flavonoid content (TFC) of the LSE was assessed utilizing the aluminum chloride (AlCl3) colorimetric method [19]. In summary,

5 mL of 2% aluminum trichloride in methanol was combined with an equivalent volume of the sample (500μ g/mL). The mixture was allowed to rest at room temperature for 10 min with intermittent agitation. The absorbance of the mixture was recorded at 415 nm relative to the blank sample devoid of aluminum chloride, utilizing a UV-Vis spectrophotometer (Shimadzu UV 1800, Japan). The total flavonoid concentration was quantified and represented as milli quercetin equivalents (mQE) per gram of extract.

Film Preparation

The original version of this paper is available on https://www.scientific.net/EI.19.1.pdf

The composite films were prepared using a solution molding technique. Initially, 100 mL of distilled water was added to 0.05 g of LSE and stirred using a magnetic hot plate stirrer (Daihan Scientific MSH-280) at 50∘C and 500 rpm for 30 minutes, followed by centrifugation at 2,000rpm for 30 minutes to ensure no insoluble residue remained.
The centrifugation process did not yield any insoluble residue, but stirring with the hot plate resulted in a reduction in the solution. The resulting LSE solution was adjusted to 100 mL and added to 10 g of PVA. The entire mixture was continuously stirred using a magnetic hot plate stirrer at 70∘C and 500 rpm for 2 hours to ensure complete dissolution of the PVA and homogeneous dispersion of the additives.
The solution was then molded into petri dishes and dried in a hot air oven (Memmert UN-55) at 50 ∘C for 20 hours. The dried film was stored in a desiccator until further use. The composition of the film-forming solution is presented in Table 1, and the resulting films were labeled with the codes P/L 0 (pure PVA film), P/L 0.05 (PVA film with 0.05 g LSE), and P/L 1 (PVA film with 1 g LSE).

Table 1. The composition of film forming solutions.

Sampel Code | PVA (g) | LSE (g) | Distilled Water (mL)
P/L 0 | 10 | 0 | 100
P/L 0.05 | 10 | 0.05 | 100
P/L 1 | 10 | 1 | 100

UV-Vis Transmittance

The original version of this paper is available on https://www.scientific.net/EI.19.1.pdf

For the optical properties UV-Visible (UV-Vis) spectroscopy was conducted using a Shimadzu UV 1800 spectrophotometer (Shimadzu, Japan) to measure the optical absorbance and transmittance of the composite films, following the ASTM D1003-00 standard method for haze and luminous transmittance of transparent plastics. The absorbance spectra were recorded in the range of 280−800 nm , the composite films were cut into 10 mm×25 mm rectangles, and UV light transmittance measurements were performed three times.

Result and Discussion

The original version of this paper is available on https://www.scientific.net/EI.19.1.pdf

Total Phenolics and Flavonoids Content

The original version of this paper is available on https://www.scientific.net/EI.19.1.pdf

The total phenolic and flavonoid contents (TPC and TFC) of LSE extracted with methanol were quantified as 38.59±0.15mgGAE/g and 91.26±0.99mEQ/g extract, respectively, indicating a significantly higher concentration of flavonoid bioactive compounds compared to extracts from other natural sources (Table 2). The high TFC values obtained in this study can be attributed to the efficiency of methanol as an extraction solvent [20]. Methanol, due to its significant polarity, is known to increase the solubility of various bioactive compounds, resulting in superior extraction yields compared to aqueous or less polar organic solvents [20, 21]. In addition, the maceration technique used, along with multiple extraction cycles, likely optimized the mass transfer of phenolics and flavonoids from the plant matrix into the solvent, thereby maximizing extraction efficiency. The phenolic and flavonoid compounds used in this study were still in the form of complex mixtures from the leaf extracts. Phenolic and flavonoid compounds are widely known for their ability to absorb UV radiation due to their conjugated aromatic structures [12, 13]. These intrinsic UV absorption characteristics support the rationale for incorporating LSE into PVA films as a natural UV protective agent.

Table 2. Results of total phenolics and flavonoids content of LSE and comparison with other plant extracts.

Plant Extract Sources | Total Phenolics (mgGAE/g) | Total Flavonoids (mEQ/g) | References
Longevity spinach leaf (Gynura procumbens) | 38.59 ± 0.15 | 91.26 ± 0.99 | This Study
Bay leaf extract | 41.61 ± 0.02 | 3.87 ± 0.00 | [10]
Nigella sativa seeds | 7.05 ± 0.00 | 3.05 ± 0.00 | [14]
Mulberry leaf extract | 49.54 ± 1.22 | - |
Brassica leaf extract | 48.06 ± 1.14 | - | [22]
Sorghum leaf extract | 47.04 ± 1.33 | - |
Moringa leaf extract | 61.29 ± 1.42 | - |

UV-Barrier Properties

The original version of this paper is available on https://www.scientific.net/EI.19.1.pdf

The UV protection ability of polymer films is crucial for their applications in pharmaceutical and food packaging [10, 23]. Photographs of composite films and their UV-Vis transmission spectra are presented in Figures 1 and 2. As shown in Figure 2, the wavelength range of 280−400 nm is in the UV radiation region, specifically the UV-B ( 280−315 nm ) and UV-A ( 315−400 nm ) regions, while 400−800 nm is in the visible light region [24, 25, 26]. UV-Vis spectroscopic investigations revealed unique optical properties among the synthesized PVA-based films, influenced by the addition of LSE or their combination.
PVA films exhibited poor UV protection in both the UV-A and UV-B spectra, but maintained high visible light transparency ( >90% transmission). This is predictable because PVA lacks functional groups or chromophores that absorb UV radiation, resulting in nearly full transmission [16]. The addition of a small amount of LSE (total 0.05 g ) to the PVA matrix resulted in a slight increase in UV-A protection effectiveness at 341 nm of 94.8%, while also achieving greater visible light transmission than PVA (>91%).
A significant increase in UV protection effectiveness occurred with the addition of LSE ( 1 g ) to the PVA matrix. A 1 gP/L film completely blocked 100% of UV radiation across the UV-A, UV-B, and UV-C spectrum, while maintaining good transparency in the visible spectrum (Figure 2). The P/L1 film allowed light to pass through up to a wavelength of 416 nm . This indicates that the P/L1 film is capable of blocking UV A-C radiation in the 280−400 nm wavelength range. The presence of LSE reduced visible light transmission by approximately 89%, indicating a decrease in optical clarity likely due to particle agglomeration and scattering. The balance between protection and clarity is attributed to the UV absorption characteristics of the phenolic and flavonoid compounds in LSE, as confirmed by TPC and TFC results [27].
The P/L film exhibits strong absorption in the red light region, likely due to the presence of chlorophyll, as indicated by a distinct absorption valley near 671 nm in the transmission spectrum, consistent with chlorophyll's characteristic absorption wavelength [28, 29, 30]. Chlorophyll plays a role in the formation and control of Reactive Oxygen Species (ROS) through light absorption mechanisms, particularly in the red spectrum ( ±660−680 nm ), which is the absorption peak of chlorophyll a [31]. Decreasing LSE concentration in the film composition correlates with decreasing red light absorption intensity. Interestingly, the P/L film exhibits excellent optical clarity, with a light transmittance of 89% at 800 nm .
The results indicate that modifying the BLE ratio can tailor the UV protection and transparency characteristics of the film for specific applications. This is particularly true for pharmaceutical packaging, where maximum UV protection is crucial to prevent photodegradation of active pharmaceutical ingredients. In certain food packaging applications requiring UV protection and product visibility, a lower BLE formulation would be more appropriate. This tunable performance, combined with the biodegradability of the PVA matrix, makes this film a viable option for sustainable UV-protected packaging in the pharmaceutical and food sectors.

[picture]

Fig. 1. Photographs of films a) P/L0, b) P/L0.05, and c) P/L1.

Fig. 1. Photographs of films a) P / L 0 , b) P / L 0.05 , and c) P / L 1 .

[picture]

Fig. 2. The film's light transmittance.

Fig. 2. The film's light transmittance.

Conclusion

The original version of this paper is available on https://www.scientific.net/EI.19.1.pdf

This study demonstrated that the incorporation of PVA with 1 g of long-lived spinach extract into a PVA/LSE film was able to block 100% of UV radiation across the UV-A, UV-B, and UV-C spectrum, while maintaining good transparency in the visible spectrum. The UV protection performance can be tailored to the desired transparency level, depending on the formulation. LSE contributes to the molecular adsorption of phenolic and flavonoid compounds.
UV radiation exposure can cause degradation of some pharmaceutical and food materials. With this excellent UV blocking ability, PVA/LSE1 film could be a promising candidate for future sustainable pharmaceutical and food packaging applications.

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The original version of this paper is available on https://www.scientific.net/EI.19.1.pdf

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The original version of this eBook is available on https://www.scientific.net/book/engineering-innovations-vol-19/978-3-0364-1217-7

Engineering Innovations, ISSN: 2813-1002, Vol. 19, pp 9-21

doi: 10.4028/p-lS9Zoj

© 2026 The Author(s). Published by Trans Tech Publications Ltd, Switzerland.

Submitted:
2025-11-29

Revised:
2026-03-05

Accepted:
2026-03-20

Online:
2026-06-11

Al Tools for Plasma Diagnostics by X-Ray Imaging and Spectroscopy in the PANDORA Project Frame

The original version of this paper is available on https://www.scientific.net/EI.19.9.pdf

Bianca Peri 1,2,a*, Eugenia Naselli 1, b, Giorgio Finocchiaro 1,3,c, Bharat Mishra 1, d, Angelo Pidatella 1,e, Richard Rácz 4,f, Sandor Biri 4, g, David Mascali 1, h

1 INFN, Laboratori Nazionali del Sud LNS, Catania, Italy

2 University "La Sapienza", Rome, Italy

3 University of Catania UniCt, Catania, Italy

4 Institute for Nuclear Research (Atomki), Bem square 18/c, 4026 Debrecen, Hungary

a*  bianca.peri@Ins.infn.it, b  eugenia.naselli@Ins.infn.it, c  giorgio.finocchiaro@Ins.infn.it, dbharat.mishra@Ins.infn.it, epidatella@Ins.infn.it,  frracz@atomki.hu, g  biri@atomki.hu, h  davidmascali@Ins.infn.it

Keywords: Plasma Diagnostic, X-ray Spectroscopy, X-ray Imaging, AI tools, Neural Networks

Abstract

Magnetized plasmas in compact traps offer a unique environment for fundamental research. PANDORA (Plasma for Astrophysics Nuclear Decay Observations and Radiation for Archeometry) is a multidisciplinary project focused on the study of β decays in plasmas, using a novel facility that replicates stellar-like conditions. A plasma diagnostics system based on a soft X-ray pinhole camera has been designed and implemented, with an innovative algorithm for Single-Photon Counting (SPhC) and High Dynamic Range (HDR) analysis. This enables space-resolved X-ray spectroscopy and the determination of magneto-plasma properties such as local thermodynamic parameters (in terms of electron density and temperature) and confinement dynamics. This work presents results from an AI-based model in MATLAB designed to optimize the abovementioned algorithm. Using k-means clustering, events with similar features were grouped to identify those that differentiate real event from spurious ones. A labeled dataset is then used to train a neural network to minimize pile-up, accelerating the recovery of high-resolution spectra and improving soft X-ray emission analysis. This contribution details the current neural network development stage and its first applications to experimental data acquired during an experimental campaign carried out at the ATOMKI Laboratory.

Introduction

The original version of this paper is available on https://www.scientific.net/EI.19.9.pdf

The PANDORA project, currently under development at INFN-LNS in Catania, is designed to explore β-decay properties from a pool of radioisotopes for the first time in a plasma environment, employing an ECR (Electron Cyclotron Resonance) plasma trap capable of recreating stellar-like conditions [1]. PANDORA will also be an experimental platform for further studies on plasma optical features and astrophysical processes relevant to multi-messenger observations, including effects like opacity [2]. Additionally, it will enable a wide range of applications, including materials science, accelerator physics and ion source technology. Since plasmas inherently radiate across the full electromagnetic spectrum, a comprehensive multi-diagnostic system will be integrated into PANDORA to map these emissions in detail and retrieve information on plasma properties [3].
Among other techniques, X-ray plasma diagnostics were developed over the years, allowing for the volumetric characterization of ECR plasmas. The volume-integrated X-ray diagnostics in the soft-X domain (from 0.5 keV to 30 keV ) were adopted to estimate the spectral temperatures in different plasma conditions and various ECR setups versus RF power, gas pressure, and the magnetic field profile [4,5,6].
Particular attention is devoted to the soft X-ray domain, as it is linked to warm electron populations essential for driving ionization dynamics and shaping the plasma charge state distribution [1,7].

The work on X-ray imaging techniques and space-resolved spectroscopy was initiated by our team around 20 years ago [8]. The same techniques have also been used in other plasma-based facilities, enabling the morphological study of different kinds of magnetically confined plasmas, evidencing their overall structure and the spatial distribution of the warmer electrons. This plays a main role in leading to X-ray emission via characteristic lines and bremsstrahlung radiation [9,10].
With a view to data evaluation, a novel algorithm for X-ray imaging in a Single-Photon Counting mode has been developed, enabling highly effective, space-resolved plasma spectroscopy in the soft X-ray range and providing new insight into magnetized plasma properties, including local thermodynamic conditions, confinement dynamics, and structural features. The diagnostic setup is based on an X-ray pinhole camera system [7, 11, 12]. Subsequent work focuses, for the first time, on the design and refinement of an AI-assisted diagnostic pipeline, including the optimization of clustering and neural network models, and on its implementation [13].
Using multiple datasets, each photon detection event (i.e., a photon striking the CCD) was analysed with respect to its geometrical and intensity-related characteristics. By applying a k-means clusteringbased AI tool, groups of events with shared features were identified, revealing the parameters that distinguish genuine signals from spurious ones (i.e., pile-up events). For further details on k-means please refer to the section: "Unsupervised Learning: k-means Clustering". This process allowed the creation of a labeled dataset for training a neural network capable of detecting and reducing pile-up artifacts.
The goal of this approach is to extract plasma emission spectra in a fraction of the usual time, while enhancing both energy and spatial resolution, maximizing the signal-to-noise ratio, and delivering unprecedented precision in characterizing soft X-ray fluorescence and bremsstrahlung emissions from such plasmas. The following sections detail the architecture of the AI-assisted diagnostic pipeline, the optimization of clustering and neural network models, and their application to experimental measurements for validation and first analysis on new plasma emitted X-rays datasets. This work contributes to the growing integration of AI-based analysis methods across various research fields. These approaches are becoming increasingly relevant not only in plasma physics and diagnostics, but also in accelerator physics and ion-source development and medical applications. These techniques can enable rapid plasma diagnostics, as demonstrated here, continuous ion source tuning through machine-learning-based monitoring and corrective adjustments for long-term stability, and early failure detection [14, 15]. Significant contributions are also made by the comprehensive review by Dopp et al. [16] on data-driven methodologies, as well as the more detailed study by Kirchen et al. [17], focused on shot-to-shot instability analysis proving the potential of data-driven techniques for the optimization of beam-loading conditions in high-power plasma systems.

Experimental Set-up

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The experimental campaign was conducted on the 2nd  generation ECR Ion Source (ECRIS) at Atomki Laboratory (Debrecen, Hungary) [18, 19], in November 2024. A sketch and a picture of the plasma diagnostic experimental setup are respectively shown in Fig. 1.a) and Fig.1.b).

[picture]

Fig. 1. a) Sketch of the experimental plasma diagnostic setup based on the pinhole CCD camera system. The Sophia Princeton CCD camera, coupled to a pinhole system, is currently integrated into the PYN-HO prototype. It includes a 400−μm-diameter lead pinhole, a multi-disk assembly of lead collimators, and an aluminum window used to shield the camera from UV-VIS radiation. The plasma chamber is also shown, featuring an argon plasma, the magnetic branch lines (where electrons are deconfined and impact on the plasma chamber walls), and the injection and extraction end plates. b) Picture of the experimental setup showing the CCD camera aligned with the pinhole system used to observe plasma emission within the ECRIS at ATOMKI laboratories (Debrecen, Hungary).

Fig. 1. a) Sketch of the experimental plasma diagnostic setup based on the pinhole CCD camera system. The Sophia Princeton CCD camera, coupled to a pinhole system, is currently integrated into the PYN-HO prototype. It includes a 400 − μ m -diameter lead pinhole, a multi-disk assembly of lead collimators, and an aluminum window used to shield the camera from UV-VIS radiation. The plasma chamber is also shown, featuring an argon plasma, the magnetic branch lines (where electrons are deconfined and impact on the plasma chamber walls), and the injection and extraction end plates. b) Picture of the experimental setup showing the CCD camera aligned with the pinhole system used to observe plasma emission within the ECRIS at ATOMKI laboratories (Debrecen, Hungary).

It is based on a pinhole X-ray camera system [20] properly designed for spatially, spectrally, and temporally resolved measurements of the plasma emission [21,11,22]. The setup consists of a multidisc lead collimator, a 400μ m lead pinhole, an 800 nm thick aluminum window to shield the camera from UV-VIS radiation and a back-illuminated CCD detector ( 2048×2048 pixels with pixel size 13.5μ m, model Sophia by Princeton Instruments) with the quantum efficiency optimized in the window of soft X-rays, 0.6−30keV energy range [11, 12].
To enable time-resolved studies, a dedicated X-ray PtIr shuttering system ( 4 ms of opening/closing time) was integrated, allowing millisecond-resolved acquisitions of fast-evolving plasma phenomena. This configuration enables simultaneous monitoring of plasma dynamics in energy, space, and time domains, providing crucial insights into confinement behaviour and instability evolution.
The overall system performance currently achieves an energy resolution of ~236eV at 8.1 keV , a spatial resolution of about 500μ m, and a temporal resolution in the 5 ms range, depending on the exposure mode [11].
Complementary diagnostics-including HPGe and SDD spectrometers, scintillators, RF probes, spectrum analyzers, and fast photodiodes-were employed to perform multi-scale correlation studies between X-ray bursts, beam current fluctuations, and instability repetition rates [1].
The flexibility of the diagnostic platform allows systematic investigations of key plasma parameters under different operating conditions, including variations in microwave power, excitation frequency, magnetic field topology, and gas composition. In particular, the setup has been used to explore, among
others, the gas-mixing effect in ECR plasmas, a well-known phenomenon that enhances the production of high charge states [23]. By controlling the ratio between buffer and working gases, it was possible to reproduce the conditions under which this effect arises and to analyse the associated changes in X-ray emissivity and confinement dynamics.

Method: Hybrid Approach for Photon Event Classification

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A hybrid analytical method that combines the efficiency of an advanced imaging algorithmdeveloped within the SPhC framework [7, 11, 12]-with both unsupervised and supervised machine learning models has been proposed.
The goal is to build an automated workflow capable of distinguishing valid photon events from spurious detections on a CCD sensor, thereby improving the reliability of event characterization. Our method integrates three key stages: i) feature extraction via a connectivity-based imaging tool, ii) clustering through k-means for preliminary data labelling procedures, and iii) supervised classification via a feed-forward neural network [13].

Feature Extraction via MATLAB Imaging Tool

The original version of this paper is available on https://www.scientific.net/EI.19.9.pdf

The first step employs the MATLAB function bwconncomp [24], which identifies connected components on the CCD matrix and extracts a set of geometric and intensity-related features.
Each photon impinging on the CCD camera releases its energy in the silicon, generating a characteristic number of photoelectrons, proportional to its own energy. The Analogue Digital Unit (ADU), corresponding to the information read by the CCD in each pixel, is, therefore, proportional to the product of the number of incident photons by their energies. The Single-Photon-Counting mode is obtained by fixing a very short exposure time texp  for each of the acquired image frames (several tens of milliseconds), minimizing the probability that two (or more) photons hit the same pixel or its nearest neighbours. SPhC texp was empirically set up in such a way that only a few pixels were illuminated on the full CCD matrix during a single-frame acquisition; a sequential acquisition of thousands of SPhC frames then allows us to gain the statistics necessary to obtain high-quality X-ray fluorescence spectra and images.
Fig. 2 shows a typical image-frame acquired with an exposure time of 5 ms . The detected events on the CCD are highlighted and visually validated.

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Fig. 2. CCD sensor matrix with detected events highlighted. (a) Binary map of events detected on a single CCD frame, showing the pixel-level detection. (b) Magnified view of the data matrix illustrating individual events and their spatial extent; this close-up facilitates extraction of features such as cluster size (number of pixels per event), local morphology.

Fig. 2. CCD sensor matrix with detected events highlighted. (a) Binary map of events detected on a single CCD frame, showing the pixel-level detection. (b) Magnified view of the data matrix illustrating individual events and their spatial extent; this close-up facilitates extraction of features such as cluster size (number of pixels per event), local morphology.

Each detected event (i.e., the photon impinging on the CCD which releases its energy in a group of pixels labelled as cluster) is described by a group of parameters forming the basis of the structured dataset used for analysis. Among all extracted quantities that define the properties of each cluster, three features play a dominant role:

Cluster size S - the total area occupied by the event, expressed as the number of connected pixels where the charge of the photon is released.

Number of local maxima NLM - the number of local maxima present in each cluster, which is a binary indicator of single versus multi-photon occurrence.

Eccentricity e - the ratio between the major and minor axes of a given cluster, capturing the shape of the detected event.
These features were chosen due to their direct physical interpretability and their ability to effectively disentangle genuine photon detections and background noise.
In the Fig. 2.b) can be observed an example of two events/clusters. In the first case, it is a SPhC event because the charge is released in a single cluster with a single absolute maximum. In the second case, it is a spurious pile-up event because two or more photons release their energy in a cluster characterized by multiple local maxima. The main goal is to recognize and discriminate such event populations in order to optimize the performance of the analysis code by AI-based tools.
Consequently, a dataset is constructed that contains the information for each event, in particular referring to the three main parameters described above: S,NLM,e.

Unsupervised Learning: K-means Clustering

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K -mean is an unsupervised clustering algorithm that partitions a dataset into k populations by minimizing the within-cluster sum of squared distances [25]. The algorithm starts by initializing k centroids, either randomly or using a heuristic method. Each data point is then assigned to the nearest centroid based on the Euclidean distance metric. The centroids are updated by computing the mean of the points assigned to each cluster. This assignment-update process is repeated iteratively until convergence, i.e. the centroids no longer change significantly or a maximum number of iterations is reached.
The number of populations k was determined heuristically using the Silhouette Method [26], which evaluates how well each point fits within its assigned clusters compared to others.
The configuration that maximizes the Mean Silhouette Score (MSS) is k=2, as can be observed in Fig. 3, meaning k-means will identify two different populations within the given dataset.

[picture]

Fig. 3. Plot of the Mean Silhouette Score (MSS) against the number of Population k that k-means uses to group the data into.

Fig. 3. Plot of the Mean Silhouette Score (MSS) against the number of Population k that k -means uses to group the data into.

Through k-means, two main populations were identified: one corresponding to small and compact events, and another mainly composed of bigger connected regions with a pronounced elliptical shape. This binary distinction provides the initial labeling needed for the subsequent supervised training stage.
Initially, k-means served as a valuable benchmark by rapidly identifying the cluster size threshold that had previously been established through a time-consuming fine-tuning process, involving numerous parameter trials. Subsequently, when provided solely with the parameters considered by the single-photon counting algorithm, k-means was able to reproduce the optimal spectrum with
remarkable fidelity, matching the previously achieved resolution ( 0.231 keV versus the 0.235 keV reference at the tantalum peak) and signal-to-noise ratio ( 12.05 compare to the 10.79 reference for titanium), but in a fraction of the time, generally under a minute.
As shown in Fig. 4, the application of the k-means algorithm was a fundamental benchmark result; in fact, it was able to determine a threshold value for the cluster size at 7 pixels for the CCD camera. In a matter of seconds, the model was able to match the threshold results obtained heuristically in previous studies [7]. Moreover, it's possible to see the statistical distribution of the events into the two categories: "Valid" with 61% events and 39% "Spurious" events.

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Fig. 4. Plot of counts versus cluster size after applying the k-means algorithm. This benchmark result shows that larger events (i.e. the higher the cluster size value) correspond to a multi-photon events occurring within the same region of the CCD.

Fig. 4. Plot of counts versus cluster size after applying the k -means algorithm. This benchmark result shows that larger events (i.e. the higher the cluster size value) correspond to a multi-photon events occurring within the same region of the CCD.

Supervised Learning: Neural Network Design

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With labeled data available, a feed-forward neural network for binary classification was designed and developed.
Before training, every feature was normalized to the range [ 0,1 ] using MATLAB's mapminmax [27] function to improve convergence and numerical stability.Additionally, class balance was verified to confirm that both categories were adequately represented, avoiding biased learning.
The dataset was randomly split into training (70%), validation (15%), and test (15%) subsets to prevent overfitting.

Network Architecture

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The network was implemented using MATLAB's patternnet [28]. This feed-forward architecture processes data in a single direction, without feedback loops, and is well-suited for binary classification tasks.
The architecture includes, as shown in Fig. 5:

Input layer: neurons equal to the number of selected features.

Hidden layer: 10 neurons with a hyperbolic tangent (tanh) activation function, introducing non-linearity and enabling the network to learn complex relationships.

Output layer: a single neuron with a sigmoid activation function that produces a probability score for event classification.

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Fig. 5. Architecture of the feed-forward neural network: patternnet encoded in MATLAB with input, hidden, and output layers.

Fig. 5. Architecture of the feed-forward neural network: patternnet encoded in MATLAB with input, hidden, and output layers.

This structure offers an optimal balance between simplicity, computational efficiency, and learning capability.

Results

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The following section presents the first obtained results. It begins with the training performance, then moves on to the benchmark results used to ensure the reliability of the network, and finally introduces the first results on a completely new dataset collected during an experimental campaign carried out at Atomki Laboratories in November 2024.

Training Performance

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The training process relies on minimizing of the cross-entropy loss function, which measures the distance between predicted and actual class memberships.
Training was conducted over 300 epochs, during which the network demonstrated stable and rapid convergence, as shown in Fig. 6. Training was halted at the 128th epoch based on a validation checkpoint to prevent overfitting.
The cross-entropy loss consistently decreased, and the confusion matrix at the end of training revealed a high accuracy with consistent results across the training, validation, and test subsets.

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Fig. 6. Loss function plot during training, validation and test phases.

Fig. 6. Loss function plot during training, validation and test phases.

The convergence of the algorithm is further supported by the confusion matrix, which tracks the number of true positives and true negatives throughout the training, validation and test subsets. It provides a direct measure of the network's ability to correctly classify the events. In Fig. 7 the overall confusion matrix is shown.

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Fig. 7. Confusion matrix showing the distribution of true positives, true negatives, false positives, and false negatives obtained during the classification task. The matrix illustrates the model's overall performance and its ability to correctly identify each event class. The Output Class represents the guess made by the net, while the target is related to the real class the event belongs to.

Fig. 7. Confusion matrix showing the distribution of true positives, true negatives, false positives, and false negatives obtained during the classification task. The matrix illustrates the model's overall performance and its ability to correctly identify each event class. The Output Class represents the guess made by the net, while the target is related to the real class the event belongs to.

The selected parameters for the training process are summarized in Table 1.
The number of neurons in the hidden layer was selected through a trial-and-error procedure, given that an excessively large hidden layer may lead to memorization and overfitting, whereas an insufficient number of neurons prevents the network from learning effectively.
The hyperbolic tangent activation function was adopted for the dense layer as a standard choice for the classification task, while a sigmoid function was used in the output layer to allow the estimation of the probability that each event belongs to one class or the other. Finally, the Scaled Conjugate Gradient (SCG) algorithm was employed for training, as it accelerates and stabilizes convergence by avoiding line search and removing the need for learning-rate tuning. The cross-entropy loss function was used to measure the divergence between predicted and true class distributions, thereby directly optimizing classification performance.

Table 1. Parameters used for the training phase.

Parameters | Value
Neurons in the Hidden Layer | 10
Training Algorithm | Scale Conjugate Gradient
Number of Epochs | 300
Number of Events | 7767611
Activation Function | Hyperbolic Tangent
Activation Function Output | Sigmoid
Minimum Gradient | 10^-6
Maximum Fails | 10
Loss Function | Cross Entropy

These outcomes confirm both the reliability of the feature selection and the effectiveness of the hybrid workflow. In particular, the unsupervised stage provided robust initial labeling, which allowed the supervised model to learn efficiently even with a relatively small dataset.
The presented hybrid methodology successfully integrates physical feature extraction, unsupervised clustering, and supervised learning within a single pipeline. This approach achieves high classification accuracy while remaining computationally efficient and scalable.
By coupling interpretable physical features with data-driven machine learning models, the method establishes a foundation for future extensions, including multi-class event characterization or online, real-time photon event analysis. The framework demonstrates how a combination of traditional imaging analysis and modern machine learning can advance automated, reliable event detection in experimental physics.

Benchmark Results

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The benchmark analysis aimed to reproduce the same results obtained with the Single Photon Counting Algorithm developed at LNS [4, 5, 10], to prove the reliability of the new AI-based approach. The analysis was conducted on the experimental data coming from the campaign carried out at the Atomki Laboratories in 2018. The measurements were conducted with the 2nd  generation ECRIS using a basic operation pumping frequency of 13.9 GHz at a power of 200 W . A special design of the plasma chamber (length ~210 mm, diameter ~58 mm ) was implemented. The chamber lateral wall was covered by a liner of tantalum and made of titanium and aluminum (extraction and injection endplate, respectively). In this way fluxes of electrons escaping the plasma and impinging on the chamber walls can be detected by the fluorescence induced on the metals, which can be easily distinguished from the Ar emission in plasma.
Within the former algorithm, the presence of a threshold in the cluster size was estimated via a semiempirical approach which was hugely computational time consuming, properly balancing statistics and resolutions [7]. The optimize value was S=5.
The data were then analysed using the approach described in the section: "Unsupervised Learning: k -means Clustering" employing of k-means. In this case, the determination of the cluster size threshold value is optimized and fully automated, requiring significantly less computational time than the previous semi-empirical approach, while still reaching the same final value: S=5, as shown in Fig. 8.a). The k-means algorithm was able to identify two different populations among the photon events, considering the commonalities between the events features. The results produced the same threshold identification in a matter of minutes. Encouraged by the compatibility between the two algorithms, the spectra obtained within the two were compared.
To evaluate the effectiveness of the cluster-size threshold in distinguishing true SPhC events from spurious ones, the events can be processed to reconstruct the corresponding experimental spectra. In the case where the population corresponds effectively to single-photon events, so that energy information is indeed conserved, one should obtain a well-resolved spectrum in which it is possible to distinguish the characteristic peaks of the system's constituent elements. Referring to the dataset
analysed, the fluorescence lines can be originated from the plasma (Ar) emissions and from the plasma chamber walls (Al, Ti, Ta) ones, due to electrons impacting them and emitting intense radiation.
The benchmark results are displayed in Fig.8.b). The spectra related to the population with cluster size S<=5, analysed by the traditional SPhC algorithm and the k-means based one are respectively showed in blue and in green. It is possible to highlight the agreement between the two approaches and that the result shows well-resolved spectra attributable to good single photo-counting events, where each characteristic fluorescence peak is well distinguishable ( Ar−Ka,Ti−Ka,Ti−Kb,Ta−La, etc.). Instead, the other population with cluster-size S>5 is mainly affected by spurious events since the spectrum is not well resolved and the characteristic fluorescence lines cannot be distinguishable. The red spectrum shows the spectrum for the entire dataset, without separating the two populations. It is important to highlight that the spectrum analysed with the new approach is fully compatible with the one obtained with the previous method, which serves as a validation of benchmark measurements.

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Fig. 8. Panel displaying the benchmark results obtained. In a) the plot features the Area, in terms of number of pixels, against the cluster ID. The two different populations are marked in red and in blue. The green dashed line represents the threshold value of S=5 pixels. In b) plot shows the comparison between different spectra. In red, the spectrum of the entire dataset, without any processing. The green curve represents the best spectrum obtained with the Single Photon Counting algorithm, while the blue plot is the spectrum obtained via application of the k-means discrimination algorithm, reducing the space of the parameters to the one employed with the former algorithm. Lastly in c) plot displays the comparison of the spectrum obtained with the k-means tool considering multiple features, while the green and the red define in b).

Fig. 8. Panel displaying the benchmark results obtained. In a) the plot features the Area, in terms of number of pixels, against the cluster ID. The two different populations are marked in red and in blue. The green dashed line represents the threshold value of S = 5 pixels. In b) plot shows the comparison between different spectra. In red, the spectrum of the entire dataset, without any processing. The green curve represents the best spectrum obtained with the Single Photon Counting algorithm, while the blue plot is the spectrum obtained via application of the k -means discrimination algorithm, reducing the space of the parameters to the one employed with the former algorithm. Lastly in c) plot displays the comparison of the spectrum obtained with the k -means tool considering multiple features, while the green and the red define in b).

Lastly, multiple parameters in the k-means step were added to consider not only the cluster size but also the number of local maxima and the eccentricity.
The spectra are shown in Fig. 8.c) respectively in blue and green for the one analysed by the SPhC algorithm and for the k-means based one. The results compared show how the new results are indeed
coherent, showing a spectrum with slightly less statistic, but comparable FWHM and signal to noise ratio, in the tantalum peak.
The main spectral parameters considered in this analysis provide essential information on the physical conditions of the emitting plasma and results are summarized in Table 2. The FWHM (in keV) reflects the broadening of the fluorescence line. The line intensity (in counts) quantifies the emission strength, while the IMP/IDP and IMP/IEP ratios represent, respectively, the ratio between the intensity of the main peak of the Ti ( @ 4,51keV ) and of its dimer peak ( @9,2keV ) and the ratio between the intensity of the main peak of the Ta ( @ 8,1 keV) and of its escape peak (@ 6,4keV) [7]. Together with the total counts, these parameters form a consistent set of indicators for characterizing the emission spectrum and its underlying dynamics.

Table 2. Summary of the comparison parameters between the two spectra, the one obtained via the SPhC algorithm and the other via k-means.

Spectra

FWHM Ta

(keV)

Intensity Ti

(Counts)

mathematical formula
Ti
mathematical formula

SPhC Algorithm | 0,25 | superscript reference | 10,79 | 3,31 | superscript reference
K-means | 0,25 | superscript reference | 12,05 | 3,42 | superscript reference

The above-mentioned results were indeed promising, laying the foundation for further development of the technique.

Spectra Retrieval

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Once validated, the method was applied to new datasets for more effective and rapid data processing. The two datasets were collected at the ATOMKI Laboratories with the same setup used for the training purposes and described in the experimental set-up section. A basic operation pumping frequency of 14.25 GHz at a power of 205 W was used. Different gas mixtures were tested: i) pure krypton gas and ii) gas mixing set with krypton and neon.

For each configuration, 2000 frames with an exposure time of 160 ms each were acquired and elaborated to obtain the SPhC image and spectrum.
The parameters set for both datasets are summarized in Table 3.

Table 3. Camera and plasma parameters for each configuration. The first refers to the training dataset, where a pure krypton gas was employed, while the second refers to the gas mixing set featuring a krypton and neon plasma.

Dataset | RF Pumping | Net Power | COILS % | Plasma | Pressure
| Freq (GHz) | W | Inj | Mid | Ext | Type of gas | Mbar
Training | 14,25 | 205 | 98 | 70 | 98 | Kr | superscript reference
Test | 14,25 | 204 | 98 | 70 | 98 | mathematical formula | superscript reference

Once trained, the new dataset is feed into the net. The net itself acts as a classifier, meaning that the incoming data are classified into two categories: "Valid events" and "Spurious events", based on their features (i.e. cluster size, number of local maxima, etc).
Computationally, the implementation operates efficiently even on non-specialized hardware. Once the dataset events are correctly classified, the spectra of the valid events can be retrieved in under 15 s .
The results were benchmarked by means of the SPhC algorithm currently in use, validating those obtained so far.
The spectra coming from the training dataset and the gas mixing one are shown in Fig. 9. As expected, can be observed a well distinguished peak at low energy coming from the neon presents. Note that a linear energy calibration was performed for the time being.

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Fig. 9. X-ray spectra evaluated for a) the training set, only krypton plasma and b) gas mixing configuration combining neon and krypton. In both spectra a red curve it's shown that refers to the spectrum of the total events (valid and discarded, before the net discrimination)

Fig. 9. X-ray spectra evaluated for a) the training set, only krypton plasma and b) gas mixing configuration combining neon and krypton. In both spectra a red curve it's shown that refers to the spectrum of the total events (valid and discarded, before the net discrimination)

The spectra are well resolved if compared to the spectra of the whole dataset events, as expected. The FWHM were evaluated at the krypton peak, and they resulted to be respectively: 0.1967 keV and 0.1983 keV @ 12.4 keV .

Conclusions

The original version of this paper is available on https://www.scientific.net/EI.19.9.pdf

In conclusion, a hybrid approach was employed to analyse data acquired from the CCD diagnostic system operating in Single Photon Counting (SPhC) mode. The workflow combines an AI-based unsupervised clustering algorithm k-means to generate a labeled dataset, which was then used to train a supervised model. A feed-forward neural network encoded in MATLAB, patternnet, was subsequently developed and trained on these labeled data. This achievement served as a benchmark for the integration of machine learning techniques into the analysis pipeline for X-ray spectroscopy in magnetically confined plasmas.
Nevertheless, the current neural network was trained on a specific set-up, which limits its generalization capability, and while the feature extraction system provides a solid foundation for event characterization, the selection and weighting of features could be further optimized. To enhance performance and robustness, further training with diversified and previously unexplored datasets is ongoing. This step represents a crucial milestone toward creating an adaptive data analysis system capable of real-time event classification and pattern recognition in complex plasma environments. Moreover, although the current implementation focuses on offline data analysis, this framework lays the foundation for future adaptations toward online, real-time event monitoring, an objective that could further enhance experimental feedback and efficiency.

Acknowledgments

The original version of this paper is available on https://www.scientific.net/EI.19.9.pdf

The authors gratefully acknowledge the support of Italian Ministry of University and Research (MUR) under the grant agreement PRINS2022THRKMK, CUP I53C24002400006.
The authors wish to thank INFN for the support through the project PANDORA_Gr3 funded by third Nat. Sci. Comm. This work has been partially funded by European Union (NextGeneration EU), through the MUR-PNRR project SAMOTHRACE (ECS00000022) and through the MUR-PNRR Project PE0000023-NQSTI.
This project has also received funding from the European Union's Horizon Europe Research and Innovation programme under Grant Agreement No. 101057511.

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[bwconncomp] (Version [R2009a]). Natick, Massachusetts: The MathWorks Inc., [2025]

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Engineering Innovations, ISSN: 2813-1002, Vol. 19, pp 23-29

doi: 10.4028/p-rQpe9Z

© 2026 The Author(s). Published by Trans Tech Publications Ltd, Switzerland.

Submitted:
2025-11-28

Revised:
2026-04-09

Accepted:
2026-04-09

Online:
2026-06-11

Recent Developments of the VOXES Von Hamos X-Ray Spectrometer for Laboratory XES and XAS Studies

The original version of this paper is available on https://www.scientific.net/EI.19.23.pdf

Simone Manti 1,a* and Alessandro Scordo 1,b

Laboratori Nazionali di Frascati INFN, Frascati, Italy

a simone.manti@Inf.infn.it, b alessandro.scordo@Inf.infn.it

Keywords: VOXES; XES; XAS; Von-Hamos

Abstract

VOXES is a Von Hamos X-ray spectrometer developed at the INFN National Laboratories of Frascati for high-resolution laboratory X-ray spectroscopy in the 5−20keV range. It uses curved mosaic crystals and motorized positioning stages to perform wavelength-dispersive X-ray fluorescence (WD-XRF) with sub-10 eV tunable resolution for extended and dilute samples. Recent developments include the integration of an energy-dispersive X-ray fluorescence (ED-XRF) line based on a silicon pin-diode detector, which enables flux monitoring and simultaneous ED and WD measurements. In addition, a dedicated liquid-sample holder has been introduced, and a Y-shaped support geometry, crucial for switching to a transmission layout, provides mechanical compatibility with laboratory XAS, now under implementation. These upgrades expand the versatility and automation of VOXES, strengthening its role as a table-top platform for laboratory X-ray spectroscopy.

Introduction

The original version of this paper is available on https://www.scientific.net/EI.19.23.pdf

X-ray spectroscopy [1], in its various forms, offers a well-established method for probing the state of matter under different physical and chemical conditions. Different X-ray techniques offer specific capabilities and applications across diverse fields such as materials science [2], chemistry and catalysis [3] with applications for battery research [4, 5], gemmology [6], cultural heritage [7], environmental analysis [8], and industrial quality control [9].
Among the techniques that detect X-rays, core-hole spectroscopies [10], such as X-ray Absorption Spectroscopy (XAS) and X-ray Emission Spectroscopy (XES), play a central role in probing the local electronic environment of atoms. The creation of a deep-core vacancy and its subsequent relaxation encode detailed information about oxidation state, spin configuration, ligand environment, and bonding characteristics [11].
Traditionally, these measurements mostly have been performed at large-scale facilities, such as synchrotrons and X-ray free-electron lasers, which provide highly brilliant and tunable X-ray beams [12]. Despite their remarkable performance, access to these infrastructures remains limited due to high demand, restricted beamtime availability, and the logistical constraints associated with conducting experiments in a facility. This has driven a growing interest in developing laboratory-scale X-ray spectroscopy setups [13,14] capable of delivering high-resolution measurements using compact and cost-effective X-ray sources like X-ray tubes [15]. For instance, several table-top setups have been demonstrated for XAS and XES measurements [16], including configurations capable of performing both simultaneously [17, 18].
In this scenario, the VOXES [19, 20] spectrometer at the INFN National Laboratories of Frascati represents an example of how optimized optical design, advanced detectors, and precise motorized positioning systems can significantly enhance the performance of laboratory XES for large sources. Originally developed for application of kaonic atom X-ray spectroscopy at DAΦNE [21] within the SIDDHARTA-2 experiment [22], VOXES has evolved into a versatile platform capable of measuring emission spectra from extended or dilute samples, with a tunable sub-10 eV energy resolution in the 5−20keV range [23].
In this article, we present the recent developments of the VOXES spectrometer. We first describe the main features of the setup and its operation in emission geometry for extended sources. We then introduce the design upgrades implemented to improve sensitivity and versatility. Finally, we show
how the system can be reconfigured between emission mode (XES) and transmission mode (XAS), enabling both types of measurements within the same compact platform. The paper is structured as follows. Section 2 describes the operating principle of the VOXES setup. Section 3 presents the updated configuration, which allows switching between XES and XAS modes. Section 4 concludes the paper.

VOXES Experimental Setup

The original version of this paper is available on https://www.scientific.net/EI.19.23.pdf

The VOXES experimental setup is shown in Fig. 1. Compared to previous publications [23], the setup now consists of two complementary detection lines: a Wavelength-Dispersive (WD)-XRF line based on a Von Hamos geometry with a mosaic crystal, and an Energy-Dispersive (ED)-XRF line using a compact silicon detector. The entire spectrometer is mounted on a motorized rail system to ensure reproducible positioning and remote operation.
The X-ray source is a MicroXray WindChill tube with a molybdenum anode. The Mo K α line at

[picture]

Fig. 1: The VOXES experimental setup. The X-ray tube on the left directs the beam onto the sample holder. After emission, the beam is conditioned by adjustable slits and motorized stages to ensure precise alignment. The resulting X-rays are diffracted by the mosaic crystal and subsequently detected by the position detector placed on the right.

Fig. 1. The VOXES experimental setup. The X-ray tube on the left directs the beam onto the sample holder. After emission, the beam is conditioned by adjustable slits and motorized stages to ensure precise alignment. The resulting X-rays are diffracted by the mosaic crystal and subsequently detected by the position detector placed on the right.

17 keV results in a peak-free continuum in the 5−12keV range, that is ideal for studying 3d transition metals without source-line contamination. The WD-XRF line consists of a slit system, a curved mosaic crystal, and a position-sensitive detector. Two motorized STANDA slits ( S1 and S2 ) shape the beam emerging from the source box and define the effective source size and angular acceptance. Typical settings are an effective source width of S0′=1 mm and an angular acceptance Δθ′=0.4∘ [20], allowing a controlled trade-off between spectral resolution and photon flux. The mosaic crystals used in VOXES are highly annealed pyrolytic graphite (HAPG) crystals, all oriented along the (002) direction with a lattice spacing of d=3.354\AA. They share the typical properties of HAPG, namely high reflectivity, low absorption in the 5−20keV range, and a controlled mosaic spread that enables efficient diffraction of divergent beams, with a declared mosaicity of 0.10∘±0.01∘. The specific crystals available for VOXES differ in their bending radius and thickness, allowing the instrument to be optimized for different applications. A set of cylindrical crystals is available with radii ρc=10.3 mm,
77.5 mm,103.4 mm, and 206.7 mm , as well as thicknesses of 20μ m,40μ m, and 100μ m. The crystal mount is actuated by a five-axis STANDA positioning system, enabling fine control of its position and orientation with respect to the Bragg angle θB.
The diffracted spectrum is recorded with a position-sensitive detector. Several detectors are available depending on the target energy range and efficiency requirements. For one-dimensional detection, the setup can employ a MYTHEN2 strip detector (DECTRIS) with a 50μ m strip pitch and an active area of 32×8 mm2. When two-dimensional imaging is required, particularly for applications where the vertical alignment of the dispersed spectrum is critical, an ANDOR iKon-M CCD camera can be used, featuring a 1024×1024 pixel sensor with a 13×13μ m pixel pitch and an active area of approximately 13.3×13.3 mm2. Alternatively, a Timepix detector with a 2×2 module configuration is available, providing a total active area of 512×512 pixels with a 55μ m pixel size. All position-sensitive detectors are mounted on a motorized linear stage combined with a fine rotation axis, enabling precise positioning and optimal focusing of the Bragg-diffracted spectrum along the dispersion direction.
FESTO electrical stage motor and STANDA stepper motors provide full motorization of the slit sys-

[picture]

Fig. 2: VOXES spectrometer in transmission (left) and emission (right) modes. In transmission mode, the beam passes through the sample for XAS measurements. In emission mode, the emitted X-rays are detected for XES analysis. The setup allows switching between the two modes by rotating the source and detector assemblies.

Fig. 2. VOXES spectrometer in transmission (left) and emission (right) modes. In transmission mode, the beam passes through the sample for XAS measurements. In emission mode, the emitted X-rays are detected for XES analysis. The setup allows switching between the two modes by rotating the source and detector assemblies.

tem, crystal mount, and detector stage. This ensures stable, repeatable alignment during scans and allows remote operation during long measurements. Fine adjustments of the Bragg angle and detector position are controlled through the motorized five-axis stages, which also provide pitch and roll corrections to maintain optimal focusing.
The ED-XRF line can operate in parallel with the WD-XRF system and employs an Amptek X-123 silicon pin-diode with integrated preamplifier and digital pulse processing. The detector uses a 25 mm2 active area with approximately 500μ m silicon thickness and a thin Be window (≈12.5μ m) for optimal transmission of low-energy X-rays. It delivers an energy resolution of 125−140eV FWHM at 5.9 keV . The sample holder is mounted on a motorized rotation stage, allowing the sample to be oriented toward either the WD-XRF or ED-XRF line. It includes a Y-shaped aperture, which is used for optical alignment in emission geometry and also enables transmission of the X-ray beam when

Table 1: Position-sensitive detectors available in the VOXES spectrometer and their main characteristics and intended use cases.

Detector | Type | Advantages | Limitations | Applications
MYTHEN2 | 1D Strip | High count-rate and fast readout | No vertical information | Routine WD-XRF, calibration, high-statistics studies
ANDOR iKon-M | 2D CCD | High spatial resolution, low noise | Limited count rate, slow readout | High-resolution XES, alignment and imaging
Timepix | 2D Pixel | Single-photon sensitivity, flexible 2D imaging | Lower count rate | Low-flux measurements

the setup is reconfigured for XAS for future developments. The holder accepts custom mounts for different sample types; dedicated Teflon cells with thin Kapton windows were fabricated for liquid samples, enabling measurements of aqueous or organic solutions without realigning the spectrometer.

VOXES Operating Modes

The original version of this paper is available on https://www.scientific.net/EI.19.23.pdf

The VOXES spectrometer can operate in multiple configurations depending on the measurement requirements. Its fully motorized geometry allows switching between WD-XRF and ED-XRF, with minimal manual intervention like shown in Fig. 2. The modular design also enables a transmission configuration that provides compatibility with XAS measurement, currently under development. In emission mode, the sample is positioned on the sample-holder at 45∘ so that it is directly irradiated by the X-ray tube. The emitted X-rays then impinge on the mosaic crystal, where their distance depends on the specific Bragg angle for the emission line under study. The mosaic crystal diffracts the photons, and the dispersed spectrum is recorded by the position-sensitive detector, one-dimensional with a strip detector or two-dimensional with a CCD or pixel detector. Motorized slits ( S1 and S2 ) allow adjustment of the effective source size and angular acceptance, allowing a balance of energy resolution versus counting rate, depending on the target requirements. This geometry is used for high-resolution studies of emission lines and for laboratory WD-XRF measurements.
An example of the VOXES WD-XRF operating modes is shown in Fig. 3 obtained with the MYTHEN2 detector. The left panel displays a spectrum acquired from a FeCoNi metallic foil. This foil is used during alignment and energy calibration, taking advantage of the well-separated CoKα1,2 and FeKβ lines. Once the setup is aligned and calibrated, the same geometry can be used to investigate the electronic configuration of iron-containing samples. The right panel shows two preliminary XES measurements on iron compounds in different spin states: high-spin for FeSO4 and low-spin for K4FeCN6. Spectra were acquired using the X-ray tube at 25 kV and 2 mA , with a total acquisition time of 4 hours. Due to the different spin configuration of iron, high and low respectively, the Kβ′ satellite is strongly suppressed in the low-spin compound, while it is clearly visible in the high-spin case [24] and a few eV shift. This demonstrates the ability of the VOXES spectrometer to resolve chemical and spin-state dependent features in the FeKβ1,3 emission line.
The ED-XRF line can operate independently and in parallel with the WD-XRF system. The silicon pin-diode detector is mounted on a motorized stage that allows it to be positioned closer to the sample to increase the detected signal, or retracted to avoid excessive dead time when the emitted flux is high. It collects energy-dispersive fluorescence spectra in the 2−25keV range. This mode is typically used to monitor the emitted flux, particularly from dilute samples such as liquids, or as a complementary detection channel for quantitative analysis. The sample holder includes mounts for both solid and liquid samples (see Fig. 4). Liquid measurements are carried out using dedicated Teflon pouches with external dimensions of 25×80×2.5 mm3 and an exposed active area of 15×60 mm2, encapsulated with 7μ m thin Kapton windows. This thickness results in a transmission of approximately 40% at 7

[picture]

Fig. 3: WD-XRF Spectrum of a FeCoNi foil (left) used to align and calibrate with cobalt and iron reference line. Spectra (right) of high-spin K4FeCN6 (blue) and low-spin FeSO4 (orange). The FeKβ′ suppression in low-spin compounds is clearly resolved, indicating sufficient sensitivity for chemicalstate analysis.

Fig. 3. WD-XRF Spectrum of a FeCoNi foil (left) used to align and calibrate with cobalt and iron reference line. Spectra (right) of high-spin K 4 F e C N 6 (blue) and low-spin F e S O 4 (orange). The F e K β ′ suppression in low-spin compounds is clearly resolved, indicating sufficient sensitivity for chemicalstate analysis.

keV for water-like liquid solutions, which is close to the optimal regime for transmission-mode XAS measurements [13]. Other path lengths are available and can be selected depending on the photon energy and sample composition. The cells can be installed without modifying the optical alignment, allowing the system to switch rapidly between solid and liquid samples while preserving the same measurement geometry. The VOXES setup has been mechanically upgraded to allow operation in a transmission geometry suitable for laboratory XAS. This configuration is enabled by a Y-shaped sample holder and a reversible X-ray tube mount, which allows the beam to illuminate the sample from behind. Although XAS measurements are still under development, the hardware required to perform absorption studies is already in place and will be validated in future work.

[picture]

Fig. 4: Teflon liquid-sample holder used for solution measurements. The liquid is contained in a flexible pouch fixed with Teflon screws to prevent metal contamination, and sealed with a Kapton window to ensure X-ray transmission.

Fig. 4. Teflon liquid-sample holder used for solution measurements. The liquid is contained in a flexible pouch fixed with Teflon screws to prevent metal contamination, and sealed with a Kapton window to ensure X-ray transmission.

Conclusion and Outlook

The original version of this paper is available on https://www.scientific.net/EI.19.23.pdf

In this work, we presented the recent developments of the VOXES spectrometer at the INFN National Laboratories of Frascati. The setup now features full motorization of all key components, including slits, crystal and detector, ensuring stable, reproducible, and remotely controlled operation for laboratory X-ray spectroscopy. The implementation of a dedicated sample holder, with the possibility to perform measurements on liquids, expands the range of measurable materials to include solutions and non-solid samples. In addition, the integration of a complementary ED-XRF line based on a silicon pin-diode enables fast alignment, flux monitoring, and simultaneous WD+ED acquisition, significantly improving workflow efficiency. The mechanical layout has also been upgraded to allow operation in a transmission mode geometry. Through the introduction of a Y-shaped sample holder and a reversible tube mount, the system is now compatible with transmission mode measurements for acquiring XAS spectra. These improvements increase the flexibility and usability of the VOXES spectrometer, reinforcing its role as a compact and versatile platform for laboratory-scale XRF measurements. Future work will focus on completing the XAS implementation and further optimizing the automation and data-acquisition framework.

Acknowledgments

The original version of this paper is available on https://www.scientific.net/EI.19.23.pdf

Project 963530 - RCLIB, "Recycling of the cathodes, based on carbon nanotubes and conducting polymers, from spent rechargeable Li batteries" - call LEAP-RE 2022"

References

The original version of this paper is available on https://www.scientific.net/EI.19.23.pdf

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Engineering Innovations, ISSN: 2813-1002, Vol. 19, pp 31-38

doi: 10.4028/p-1astY2

© 2026 The Author(s). Published by Trans Tech Publications Ltd, Switzerland.

Submitted:
2025-11-29

Revised:
2026-01-12

Accepted:
2026-03-20

Online:
2026-06-11

Beamlines of the EuPRAXIA@SPARC_LAB X-Ray FEL Facility

The original version of this paper is available on https://www.scientific.net/EI.19.31.pdf

Federico Nguyen 1,2

1 ENEA, Centro Ricerche Frascati, Via E. Fermi 45, 00044 Frascati, Italy

2 INFN-LNF, Via E. Fermi 54, 00044 Frascati, Italy

e-mail address: federico.nguyen@enea.it

Keywords: Free-Electron Laser, Magnet Undulators, Beam Dynamics

Abstract

We present the design and performance analysis of AQUA and ARIA, the two free-electron laser beamlines designed to operate at the EuPRAXIA@SPARC_LAB facility. AQUA is optimized for the water window, leveraging APPLE-X undulators for selectable polarization and fine spectral tuning, driven by a 1 GeV linac featuring a plasma accelerating stage. Start-to-end simulations, including realistic beam distributions and jitter effects, predict stable output of about 1011 photons per pulse. ARIA is a seeded source aimed at covering the extreme ultraviolet range with polarization control and unique characteristics of the light output pulse, for user applications in chemistry and life sciences.

Introduction

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Conventional particle accelerators face inherent limitations stemming from both technical and practical constraints. The performance of these machines is restricted by phenomena such as electrical breakdown at the metallic walls of radiofrequency (RF) cavities or the maximum achievable magnetic field in superconducting magnets. Furthermore, the pursuit of higher particle energies necessitates larger machine dimensions and escalating construction and operational costs, rendering conventional accelerator designs increasingly non-sustainable. To overcome these barriers, plasma-based accelerators [1] introduce a transformative approach by replacing the solid metallic structures of conventional RF systems with an ionized gas. This replacement eliminates damage issues associated with high gradient operation and enables the generation of extremely strong accelerating fields. In such systems, collective plasma oscillations are excited either by an intense laser pulse (Laser Wakefield Acceleration, LWFA) or by a relativistic charged particle beam (Particle Wakefield Acceleration, PWFA). The driving pulse or beam displaces the plasma electrons while the heavier ions remain stationary, leading to the formation of strong longitudinal electric fields, namely the wakefields, that can accelerate charged particles to relativistic energies over centimeter-scale distances.

Unlike conventional accelerators - limited by material breakdown thresholds - plasma-based accelerators exploit the self-organizing, dynamic properties of plasma to sustain much higher field gradients, offering a promising path toward compact and cost-effective high energy accelerator facilities.

Within these premises, the European Plasma Research Accelerator with eXcellence In Applications (EuPRAXIA) project conceived the design [2] of the first international plasma-based accelerator facility able to provide compact and efficient acceleration of electrons up to 5 GeV beam energy, driving a free-electron laser (FEL) operating in the X-rays. The proposed FEL facility is included in the 2021 ESFRI roadmap, which identifies those infrastructures of pan-European importance that correspond to the long-term needs of European research communities.

Early demonstrations of free-electron lasing driven by electron beams generated from plasmabased accelerators have been achieved using both LWFA [3, 6] and PWFA [4, 5] schemes. These experiments have been conducted in self-amplified spontaneous emission (SASE) mode as well as with an external laser seeding: in the PWFA case, both modes have been realized at the INFN-LNF laboratories. Most notably, the PWFA scheme represents the approach selected for implementation at one of the sites of the EuPRAXIA distributed research infrastructure: the EuPRAXIA@SPARC_LAB facility [7], which is going to be built at the INFN-LNF laboratories. This facility is going to host two separate FEL beamlines, with different tasks:

[picture]

Fig. 1: Layout scheme of the FEL sources at the EuPRAXIA@SPARC_LAB facility, with emphasis on the undulator and photon beamlines. Adapted figure from Mario Del Franco.

Fig. 1. Layout scheme of the FEL sources at the EuPRAXIA@SPARC_LAB facility, with emphasis on the undulator and photon beamlines. Adapted figure from Mario Del Franco.

AQUA. A soft X-ray SASE FEL source for experiments around 4 nm−10 nm wavelengths, namely photon energies of 120eV−310eV, a range where water is almost transparent to radiation the so-called water window - and where coherent diffraction imaging techniques allow to reconstruct images of viruses or cells in their native environment [8];

ARIA. A high gain harmonics generation seeded FEL source for the gas phase, operating in the 50 nm−180 nm wavelength range, able to provide 10μ J−100μ J a pulse energy with continuous tunability, selectable polarization and high degree of longitudinal coherence [9].

Figure 1 shows the layout of the FEL beamlines foreseen at the EuPRAXIA@SPARC_LAB facility.

The AQUA Beamline

The original version of this paper is available on https://www.scientific.net/EI.19.31.pdf

The undulator technology selected for AQUA is a variable polarization, out-of-vacuum permanent magnet APPLE-X device, belonging to the Advanced Planar Polarized Light Emitter (APPLE) family. This choice ensures less complex a system architecture and reduced operational costs compared with superconducting undulators. The open mechanical layout further allows the magnetic arrays to be retracted from the beam pipe, thereby mitigating radiation damage arising from driver or witness beam losses in PWFA operations. In addition, the APPLE-X configuration provides a superior on-axis magnetic field relative to APPLE-II devices for the same undulator period and aperture gap, while

Table 1: APPLE-X undulator parameters for the AQUA beamline.

Parameter | Value
Remanent field Br | 1.35 T
Period λu | 18 mm
Blocks per period | 4
Aperture diameter φ | 6 mm
Minimum gap | 1.5 mm
Kmax (linear polarization) | 1.57
Tuning range (at 1 GeV) | 3.5-5.2 nm
Module length | 2 m

also delivering a maximum deflection parameter K that is independent of the selected polarization mode and so offering an extended tuning range. Table 1 shows the main undulator parameters of the APPLE-X design for AQUA. The undulator consists of four octagonal NdFeB magnet blocks with a remanent field Br=1.35 T. The magnets are arranged radially at equal distances around the electron beam axis. The resulting square aperture at the center of the structure, with a diameter ϕ=6 mm, accommodates a vacuum pipe with an external diameter of 5.5 mm for electron propagation.

The minimum achievable gap is set to 1.5 mm , which represents a good compromise between the desired maximum magnetic field - thus the maximum value Kmax  - and practical constraints stemming from magnet mounting tolerances, and from the supports for the vacuum chamber. The undulator module length is about 2 m , that is equivalent to 110 undulator periods.

The wavelength is tuned by moving the four magnet arrays radially. Linear polarization (LP), rather than circular polarization (CP), is obtained by introducing a longitudinal shift between the two diagonally opposite magnet arrays with respect to the other two. The intrinsic symmetry of the APPLEX layout guarantees that each polarization mode can achieve the same maximum deflection parameter, although each mode naturally corresponds to a different resonant wavelength.

Operating AQUA under CP configuration

Table 2: Electron beam parameters assumed for the AQUA FEL performance studies.

Parameter | Value
Electron beam energy | 1 GeV
Peak current | 1.5 kA
Resonant wavelength | 4 nm
FWHM bunch duration | 15 fs
Twiss 〈βx〉 = 〈βy〉 | 10 m

allows to exploit a wider undulator gap tunability with respect to LP working points: water window wavelengths will be probed with higher photon yields and shorter saturation lengths. By increasing the electron beam energy, there is the chance to efficiently reach for the 3 nm target wavelength. In any case, the AQUA FEL performance demands for high quality electron beam parameters. Table 2 shows the values used to investigate the relative energy spread and normalized transverse emittance values in terms of the photon yield evaluated by means of the modified Ming-Xie semi-analytical model [10,11]. Distributions in current and energy profiles, transverse momenta and spatial coordinates are assumed Gaussian. Normalized emittance is considered equal in transverse horizontal and vertical planes. Figure 2 shows

[picture]

Fig. 2: FEL performance in terms of the photon number per pulse, as a function of relative energy spread and normalized transverse emittance, under the specified polarization mode. The white dashed ellipse indicates the AQUA working region. Adapted figure from Luca Giannessi.

Fig. 2. FEL performance in terms of the photon number per pulse, as a function of relative energy spread and normalized transverse emittance, under the specified polarization mode. The white dashed ellipse indicates the AQUA working region. Adapted figure from Luca Giannessi.

the number of photons per pulse as a function of relative energy spread and normalized transverse emittance, for both CP and LP working modes. The dashed ellipse indicates the parameter values for the AQUA electron beam. The increased acceptance region - well within the reach of 𝒪(1011) photons per pulse - is remarkable in the CP working mode: in this case, even non-optimal parameter values have marginal impact on the FEL performance.

Results from the same model show that the active magnetic length needed to reach for saturation of the FEL gain growth is about 20 m . Thus, the AQUA beamline consists of ten APPLE-X modules, interleaved with about 60 cm long intra-undulator cell space for quadrupoles and diagnostics.

The effects of non-optimal values can be partially compensated by decreasing the average betatron β-function. However, the periodicity conditions imposed by the lattice - comprising the undulators together with the embedded quadrupoles and magnetic correctors - fix the betatron Twiss parameter β as a function of the quadrupole strength. Figure 3 shows this behavior at increasing integral quadrupole gradient, assuming the quadrupole magnetic length is 10 cm . As a result, there exists a lower bound on
the achievable average β-function that cannot be reduced further, even by increasing the quadrupole strength. For the present lattice, this minimum value is βmin =4.3 m.

On the other hand, taking also
the focusing power of the undulator modules into account, the effective quadrupole strength depends on the deflection parameter, namely on the resonant wavelength, and on the operating polarization mode. Most notably, for the AQUA beamline driven by an electron beam energy of 1 GeV for working at a resonant wavelength of 4 nm , the requested quadrupole integral strength values are fairly lower than 1 T in both polarization modes.

The behavior of the quadrupoles strength is investigated at increasing the electron beam energy, while keeping constant all other parameters. In order to accommodate the same focusing factors, the integral gradient has to be increased. Figure 4 shows the quadrupole strengths required to focus the electron beam along the undulator line at average beta functions of ⟨βx,y⟩=8 m (left panel) and 5 m (right panel), as a function of the electron beam energy, for both circular and linear polarization modes. The corresponding resonant wavelength at each beam energy is calculated for undulator period λu=18 mm and deflection parameter KRMS=0.84, and shown on the same plots. For ⟨βx,y⟩=8 m, the strength values stay on the order of about 1 T

[picture]

Fig. 4: Integral quadrupole strength as a function of the electron beam energy for both LP and CP configurations, superimposed to the achievable resonant wavelength calculated for ⟨βx,y⟩=5 m and Q≃30pC, focusing at ϵx=0.69 mm×mrad (left) and ϵy=0.66 mm×mrad (right).

Fig. 4. Integral quadrupole strength as a function of the electron beam energy for both LP and CP configurations, superimposed to the achievable resonant wavelength calculated for ⟨ β x , y ⟩ = 5 m and Q ≃ 30 p C , focusing at ϵ x = 0.69 m m × mrad (left) and ϵ y = 0.66 m m × mrad (right).

or even lower. These estimates imply that the same undulator beamline under design can operate at higher energies, in such a way to cover shorter resonant FEL wavelengths in a more efficient way. For 3×1011, the increase of the quadrupole strength is steeper, but the integral field values are
required to be around 2 T only if the beamline has to operate at electron beam energies larger than 1.4 GeV , a scenario that is not foreseen for the baseline linac at EuPRAXIA@SPARC_LAB.

The start-to-end electron bunch distribution, realistically transported through every stage of the linac up to the undulator entrance, is analyzed with the Genesis1.3 simulation code [12] to assess the resulting FEL performance. In addition to the quantities listed in Table 2, the start-to-end electron beam under study has the following properties: bunch charge of 1011, slice normalized emittances of 3.80 nm<λ<4.06 nm and 10μ J−100μ J, and a slice relative energy spread of 0.2 %.

The FEL performance of the AQUA facility operated in circular polarization is summarized in Table 3, where a properly tailored undulator tapering [13] is taken into account. Owing to the short electron bunch length, applying a taper to the magnetic field of the undulator modules enhances the emitted power and leads to a temporal shortening of the radiation pulse. The results in the table indicate that the AQUA source delivers pulses of about 50 nm−180 nm photons, with a relatively narrow bandwidth. The transverse size and divergence values demonstrate that the system provides a well-collimated, high brightness X-ray beam suitable for applications requiring good spatial coherence and spectral purity.

Extensive tolerance studies have been performed accounting for jitter sources in the RF

Table 3: AQUA FEL output parameters in circular polarization with undulator tapering, as obtained from time-dependent Genesis1.3 simulations.

Parameter | Value
Pulse energy | 15.8 μJ
Photons per pulse Nγ | 3.2 × 10¹¹
Saturation length | 20.5 m
Mean wavelength λ | 4.01 nm
Bandwidth | 0.44 %
Transverse size | 230 μm
Divergence | 34 μrad
Rel. deviation on Nγ | 48 %
Rel. deviation on λ | 2 %

accelerating sections, in the photocathode laser system and in the plasma density. In particular, RF voltage amplitude and phase introduce shot-toshot fluctuations in the energy gain within each section thereby altering both the mean energy and the correlated energy spread. Variations in the cathode laser pulse energy translate directly into charge jitter, while changes in spot size induce transverse emittance fluctuations. Moreover, the temporal characteristics of the laser pulse determine the initial bunch length, and consequently the peak current. Finally, the beam emerging from the PWFA stage exhibits shot-to-shot variations in energy gain, energy spread, and longitudinal phase-space which arise from fluctuations in the plasma density.

A Monte Carlo procedure is employed in which 100 independent linac replicas are generated by randomly sampling the distributions associated with the jitter sources described above. The resulting 100 start-to-end electron bunches are tracked through all sections of the linac, and their corresponding FEL output is computed to assess the impact of machine fluctuations on performance. Last two rows of Table 3 show the fractional deviations both on the photon number per pulse and on the mean wavelength, resulting from the analysis of the 100 electron bunches. Figure 5 shows the photon number per pulse as a function of the mean electron beam energy (left) and of the resonant wavelength (right), arising from the analysis of the different 100 linac replicas accounting for the fluctuations discussed above. As a result of these stability studies, about 84 electron bunches are capable to provide with at least ϵx=1.24 mm×mrad photons per pulse, and they have mean wavelength ϵy=0.98 mm×mrad.

The ARIA Beamline

The original version of this paper is available on https://www.scientific.net/EI.19.31.pdf

This beamline is conceived to operate as a seeded FEL source working in high gain harmonics generation, able to provide fully coherent pulses with energy in the 0.4% range, continuously tunable in the nth  wavelength range. As sketched in Figure 1, this beamline foresees a modulator, a dispersive section and four undulator modules acting as the main radiator, for a total physical length of about 20 m . The choice of the undulator technology is not yet finalized, as variable selectable

[picture]

Fig. 5: Results from simulations of 100 electron bunches including jitter effects for the linac driving the FEL source. Photon number per pulse at the undulator exit as a function of the mean electron beam energy (left) and of the resonant wavelength (right). Adapted figure from Vittoria Petrillo.

Fig. 5. Results from simulations of 100 electron bunches including jitter effects for the linac driving the FEL source. Photon number per pulse at the undulator exit as a function of the mean electron beam energy (left) and of the resonant wavelength (right). Adapted figure from Vittoria Petrillo.

polarization is an asset also in this case, and comparison studies between APPLE-II and APPLE-X solutions are still ongoing. The bonus value to run such a facility within EuPRAXIA@SPARC_LAB is the flexibility on the beam longitudinal phase space: the PWFA scheme has the peculiar characteristics to deliver short electron bunches. Most notably, a seeded FEL source such as ARIA might in principle be tuned to operate according to the following schemes, with the corresponding considerations.

Short electron bunch. When a high peak current short electron bunch interacts with a long seed laser pulse, the FEL pulse duration is primarily determined by the bunch length. In this regime, the possibly large FEL gain broadens the effective gain bandwidth, so that even with a long seed pulse the amplified spectrum may become slightly wider due to the strong gain. The central wavelength, however, remains locked to the seed, provided that the energy chirp along the short bunch is sufficiently small to preserve the resonance condition.

Long electron bunch. For a seeded FEL operated with a low peak current long electron bunch and a long seed pulse, the duration of the emitted FEL pulse is ultimately limited by the longitudinal energy-position correlation of the electron bunch, since only the resonant slice remains phasematched to the seed. In this regime, the wavelength linewidth stability is instead governed by the coherence properties of the external seed laser.

Both schemes are complementary with the FEL sources foreseen to operate at the FERMI facility [14], where a long electron bunch with low peak current interacts with a short seed laser pulse. No other seeded FEL facility with the option of a short electron bunch covers the same ARIA wavelength window. Furthermore, such an FEL facility extends over high harmonics in gas (HHG) sources without limits in polarization, wavelength tuning and intensity: ARIA can actually be synchronized with HHG sources or external lasers for multicolor multi-pulse pump and probe operations. Accounting for fluctuations in the linac sections in a way similar to the above discussions, the FEL performance of operating ARIA with four APPLE-X radiator modules in circular polarization is evaluated on 10 replicas of start-to-end 1 GeV energy electron bunches with the following average values: bunch charge of 50 pC , slice normalized emittances of 1011 and mathematical formula, and RMS relative energy spread of mathematical formula. Table 4 shows the FEL light yield results obtained by running simulations with the Genesis1.3 code, working at the indicated mathematical formula harmonic and achieved after the specified number of modules. The results in Table 4 reflect the reduced gain and longer amplification distance required at higher harmonic number and shorter wavelength, as the FEL pulse energy decreases and

Table 4: ARIA FEL output parameters at the λu=18 mm and KRMS=0.84 harmonics, as obtained from time-dependent Genesis 1.3 simulations in circular polarization, after the number of specified undulator modules.

Quantity [unit] | n = 3, λ = 153 nm after 3 modules | n = 9, λ = 51 nm after 4 modules
pulse energy [μJ] | 127 | 53
bandwidth [%] | 1.5 | 0.6
FWHM duration [fs] | 18 | 17
trans. size [mm] | 0.8 | 0.4
divergence [mrad] | 0.3 | 0.08
saturation length [m] | 2.9 | 5.5

the saturation length increases. Nevertheless, the spectral and transverse beam properties improve significantly at shorter wavelength. In particular, the relative bandwidth is reduced, and the transverse source size and divergence shrink from 0.8 mm and 0.3 mrad to 0.4 mm and 0.08 mrad , respectively, indicating better transverse coherence and beam collimation. The pulse duration remains essentially unchanged, demonstrating that the temporal structure of the radiation is preserved across the harmonic orders.

Conclusions

The original version of this paper is available on https://www.scientific.net/EI.19.31.pdf

In this work we have presented the conceptual design and performance assessment of the AQUA beamline, a flexible and cost-effective FEL facility optimized for operation in the water window. The line is based on APPLE-X undulators, a well-established technology that enables selectable polarization and fine tuning over the spectral range of interest. This configuration, combined with a linac capable of sustaining electron beam energies above 1 GeV , ensures that the target FEL performance is robust with respect to variations in energy spread and transverse emittance. Comprehensive time-dependent simulations have been performed, using realistic electron beam distributions tracked from the cathode to the undulator entrance and including the impact of machine jitters around nominal operating conditions. These studies demonstrate that AQUA will be able to deliver of the order of superscript number photons per pulse with narrow bandwidth and small deviations from the target output parameters. The results confirm the stability and reliability of the proposed configuration.

The feasibility and expected performance of ARIA as a versatile extreme ultraviolet (EUV) user facility, delivering FEL pulses of about 20 fs close to the Fourier transform limit, have been investigated in detail, also making use of plasma-beam driven electron bunch distributions. The selectable polarization capability in the EUV range will be a key asset for future experiments, enabling the exploration of chirality, circular dichroism, and related phenomena in biotic and soft-matter systems with unprecedented temporal and spectral characteristics.

In summary, the EuPRAXIA@SPARC_LAB beamlines design combines mature undulator technology, high quality electron beams, and advanced start-to-end modelling to demonstrate a realistic path towards next generation water window and EUV FEL sources. The projected performance in terms of pulse energy, duration, spectral purity, and polarization control provides the foundations for a broad user program in physics, chemistry, and life sciences.

Acknowledgments

The original version of this paper is available on https://www.scientific.net/EI.19.31.pdf

The author wishes to thank Luca Giannessi for leading the coordinated efforts on FEL performance and undulator concepts within the EuPRAXIA@SPARC_LAB Collaboration, and Alberto Petralia for continuous support and insightful discussions on the undulator design of the FEL beamlines of the project. The author extends appreciation to Vittoria Petrillo and Michele Opromolla for providing

AQUA and ARIA simulation results, respectively. The author also thanks Mario Del Franco, Lucia Sabbatini and Andrea Selce for their contributions on the beamline layout and magnetic components. Finally, the author acknowledges Alessio Del Dotto, Anna Giribono, Stefano Romeo, Andrea Renato Rossi, Gilles Jacopo Silvi, and Cristina Vaccarezza for their essential work on the electron beam dynamics simulations, without which the FEL results presented here would not have been possible.

References

The original version of this paper is available on https://www.scientific.net/EI.19.31.pdf

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This ePub file was created using machine-generated methods and automated software to convert the source text into an ePub format and may contain formatting inconsistencies from the original version. Formatting may vary depending on the device used.

The original version of this eBook is available on https://www.scientific.net/book/engineering-innovations-vol-19/978-3-0364-1217-7

Engineering Innovations, ISSN: 2813-1002, Vol. 19, pp 39-49

doi: 10.4028/p-DCq8VK

© 2026 The Author(s). Published by Trans Tech Publications Ltd, Switzerland.

Submitted:
2025-09-11

Accepted:
2025-09-28

Online:
2026-06-11

Laser Shock Peening with Acoustic Emission In-Situ Monitoring for Quality Control

The original version of this paper is available on https://www.scientific.net/EI.19.39.pdf

James M Griffin 1,a*, Pratik Shukla 2, b, Rui Qin 3,c and Paul Butler Smith 2, d

¹College for Clean Growth and Future Mobility, Centre for Manufacturing and Materials, Coventry University, Coventry, CV1 5FB, UK

2 Ansty Park, The Manufacturing Technology Centre, Ansty Park, Pilot Way, Coventry CV7 9JU

3 Institute for Infrastructure and Environment, School of Engineering, The University of Edinburgh, Edinburgh, EH9 3FG, UK

a*ac0393@coventry.ac.uk,b Pratik.Shukla@the-mtc.org, c v1rqin@ed.ac.uk, dPaul.Butler-Smith@the-mtc.org

Keywords: Laser Shock Peening, acoustic emission, FLIR images, time series, time-frequency.

Abstract

Laser Shock Peening (LSP) is increasingly employed across various industrial sectors, particularly in safety-critical applications where extending structural integrity and lifespan is paramount. This work investigates the influence of varying laser energies and confinement layers on the LSP process. Understanding these parameter variations is crucial for precise process control. To gain deeper insights into the process dynamics from an in-situ, non-destructive testing (NDT) perspective, acoustic bursts will be considered and measured. Acoustic Emission (AE) is a method that uses piezo-electric material to determine the level of emitted elastic waves from the source where transients in the form of displacement within piezo-electric crystalline structure (either in a tensile or compressive manner) provide a quantitative voltage for decerning different measurements. Most current testing methods use destructive tests with coupon parts; this process however directly measures the material state in question and is non-destructive in nature. Post-LSP, additional NDT techniques are utilised to characterise material modifications in terms of residual stress and hardness. This research aims to enhance material understanding, complemented by destructive testing for residual stress and microstructure analysis, and to lay the foundation for advanced algorithms and digital modelling for robust real-time, quality control in LSP.

Introduction

The original version of this paper is available on https://www.scientific.net/EI.19.39.pdf

Laser shock peening (LSP) has emerged as a promising surface strengthening technology, offering superior residual stresses and hardening depths compared to conventional methods like shot peening and rolling, ultimately enhancing fatigue resistance [1]. Meng et al. [1], further discusses, the quality of a machined material surface is often quantitatively assessed by surface hardness and residual stress, which respectively indicate resistance to foreign object damage and the ability to withstand high cycle fatigue. However, ensuring consistent LSP surface quality presents a challenge due to the process's inherent variability and complexity. Traditionally, the evaluation of LSP quality has relied on offline and destructive techniques, such as X-ray diffraction and the hole-drilling method, which cannot provide real-time feedback on the material surface condition. These methods also often require specific detection parameters for different objects, making the process cumbersome and unsuitable for industrial manufacturing efficiency, reliability, and stability [1]. This necessitates the development of trustworthy, real-time, and non-destructive quality assessment techniques to guarantee the accuracy and dependability of producing critical components.

In this context, acoustic emission (AE) has gained significant attention as a dynamic and non-destructive method for evaluating material performance changes in real-time, overcoming many limitations of traditional non-destructive testing (NDT) methods and demonstrating successful application in on-line monitoring of LSP[2]. This manuscript will compare the capabilities of traditional material surface analysis techniques with (AE) methods for assessing the quality of laser shock peening processes, drawing on recent research and developments in the field.

Conventional methods for assessing the quality of LSP-treated surfaces primarily involve post-process analysis to determine the resulting surface characteristics. Such as preparing the surface through polishing, applying etching and performing micro-structure analysis. Such work is time consuming and destructive in nature. Therefore, this cannot be applied to safety-critical structures and requires another approach. The machining quality after LSP is often evaluated by measuring surface hardness, which indicates resistance to damage, and residual stress, which correlates with fatigue resistance [1]. Techniques like microhardness and nano hardness measurements are used to quantify surface hardening resulting from the plastic deformation induced by LSP[3]. These are certainly quantitative processes however again, they are destructive in nature. Meng et al. [1] further discusses, offline and destructive methods such as X-ray diffraction and the hole-drilling method are commonly employed to measure the surface residual stress induced by LSP. X-ray diffraction analyses the crystallographic structure of the material to determine strain and subsequently stress [6]. The hole-drilling method involves incrementally drilling a small hole in the material and measuring the relieved strain to calculate the residual stress. Meng et al. [1] continues, while these traditional techniques provide valuable quantitative data about the final state of the material surface, they suffer from several drawbacks: most of these methods require the treated part to be removed from the production line and often involve destructive procedures, preventing their use for real-time monitoring on every produced part. The processes for setting up and conducting these tests, along with the subsequent data analysis, can be time-consuming and may not meet the demands of high-efficiency manufacturing. In addition, they have the inability to provide immediate feedback during the LSP process means that any inconsistencies or defects are only detected after the treatment is complete, potentially leading to wasted resources and production delays. Another aspect for consideration, different materials and desired surface quality characteristics may necessitate specific test parameters for these offline methods, adding to the complexity and cumbersomeness of the inspection process [1]. Such complexities may force the need for intelligent databases where real-time comparisons can be made both accurate and repeatable.

AE, however, has emerged as a promising alternative for on-line and non-destructive monitoring of LSP quality [2]. LSP involves the interaction of a high-energy pulsed laser with the material surface, leading to rapid vaporisation and plasma formation [1]. The rapid expansion of this plasma, confined by a transparent overlay, generates high-pressure plasma shock waves that propagate into the material. These shock waves induce plastic deformation and create compressive residual stresses [1]. This process generates elastic waves within the material, which can be detected by AE sensors placed on the workpiece [3]. AE is a passive NDT method that detects the transient elastic waves generated by the rapid release of energy within a material due to phenomena like plastic deformation and crack initiation/propagation [2]. In the context of LSP, AE allows for real-time monitoring of the material's response to the laser-induced shock waves without causing any damage to the component [2]. AE sensors can capture changes in the material's stress waves during the LSP process, providing valuable information about the stability and consistency of the treatment [5]. Variations in the AE signals can indicate fluctuations in laser parameters, plasma formation, or the material's response [5].

AE monitoring presents a promising alternative for in-situ quality assessment of LSP [3]. AE involves the detection and analysis of transient elastic waves generated within a material during deformation or damage processes [2]. These elastic waves, or acoustic signals, can provide dynamic information about the LSP process stability and potentially correlate with the resulting surface quality and residual stress state [1]. Qiu et al. [3] suggested that the correlation between adjacent data sampling points of the acoustic signal has characteristics of identifying residual stress. In addition, the same work established an empirical formula between acoustic signal energy and residual stress for online monitoring of LSP using polynomial fitting. However, noisy processing environments can hinder the sensing ability of acoustic signals. Qiu et al. [3] also mentioned, plasma acoustic waves collected in the air are also a good area of research because they are simpler to attach and acquire, nevertheless, they also suffer the most in terms of noise.

Research has shown that AE signals and their characteristic parameters are related to the energy of the plasma shock waves and the resulting changes in the material [2]. For instance, the AE energy of
the material, calculated from detected waveforms and characteristic parameters, has been shown to increase with pulse laser energy, reflecting the intensity of the shock waves [2]. This suggests a direct link between AE signals and the effectiveness of the LSP process in inducing the desired compressive residual stresses [7]. The acquired AE signals can be analysed using advanced signal processing techniques to extract meaningful information about the LSP process [5]. This can involve analysing the frequency content, amplitude, duration, and other characteristics of the AE signals to identify patterns indicative of process quality [5]. Studies have explored the propagation characteristics of AE signals and planar positioning functions to calculate the AE energy of the material. This approach aims to provide a standardised method for evaluating LSP quality, potentially overcoming the challenge of signal variation due to sensor position [2]. Besides direct AE from the material, the formation of laser-induced plasma also generates acoustic feedback in the form of optoacoustic signals, which can be captured by microphones and piezo transducers [8]. Analysing the spectra of these optoacoustic signals can provide supplementary information regarding plasma propagation dynamics, which is inherently linked to the LSP process [8]. A comparison between current methods and using acoustic emission is displayed in Table 1.

Table 1 Comparative analysis of traditional techniques vs. advanced non-destructive techniques

Feature | Traditional Material Surface Analysis | Acoustic Emission (AE)
Timing of Analysis | Offline, post-process | On-line, real-time
Nature of Analysis | Often destructive | Non-destructive
Feedback Capability | No real-time feedback | Potential for real-time process adjustment
Process Efficiency | Cumbersome, time-consuming | High potential for efficiency
Scope of Information | Primarily final surface condition | Dynamic information about the process itself
Industrial Applicability | Challenges for on-line integration | Well-suited for on-line monitoring

AE offers significant advantages over traditional material surface analysis techniques for quality assessment in laser shock peening. Its real-time and non-destructive nature allows for continuous monitoring of the process and early detection of potential issues, which is crucial for maintaining manufacturing efficiency and product reliability. While traditional methods provide accurate measurements of the final surface state, they lack the ability to offer immediate feedback for process control [1].

However, AE-based monitoring also presents certain challenges. The characteristics of AE signals can be influenced by factors such as sensor position and coupling, potentially requiring the establishment of databases for different experimental conditions to ensure standardised quality evaluation [2]. Furthermore, correlating AE signals directly with quantitative measures of residual stress and hardness might require sophisticated signal processing and robust calibration against traditional methods.

The integration of advanced signal processing techniques and machine learning algorithms holds significant promise for enhancing the capabilities of AE in LSP quality assessment [5]. By analysing complex patterns in AE signals and correlating them with process parameters and resulting surface quality, more accurate and reliable on-line monitoring and control systems can be developed.

This work demonstrates the potential of a multispectral in-situ monitoring approach, integrating AE measurements with other sensory information, to enhance the understanding and quality control of the LSP process. The study highlights the correlation between LSP parameters, in-situ signals, and post-LSP material characteristics such as residual stress and hardness. While traditional offline analysis techniques remain essential for characterising the final material state, the real-time and non-destructive nature of in-situ multispectral monitoring, particularly AE, offers a compelling
alternative for online quality assessment [1]. Meng et al. [1] discusses further that ongoing research focusing on advanced signal processing of multispectral data, optimised sensor placement, and the application of machine learning algorithms will further improve the reliability and applicability of this approach for ensuring the quality and consistency of LSP in industrial manufacturing. The ability to obtain immediate feedback during the LSP process paves the way for more efficient, reliable, and cost-effective production of high-performance components treated with LSP. Future work will focus on validating these correlations with destructive testing and developing robust digital models for real-time quality prediction.

Method of Experimentation

The original version of this paper is available on https://www.scientific.net/EI.19.39.pdf

To look at different systems, and different effects, Experiment 1 will focus on different confinement layers and Experiment 2, different power densities. The laser system parameters for both Experiments are seen below in Tables 2 and 3 respectively. Experiment 1 was also chosen to evaluate the sensitivity of contact acoustic emission (AE) signal detection when faced with different confinement layers namely, air, gel and water. For these tests the amplifier gain will be explored to see which is the best gain when carrying out LSP. Experiment 2 will look at a higher power laser system where power setting will be looked at the effect of the LSP on AE. The power settings will be 15%,50% and 98%. Qiu et al. [3] mentions in their work, that there are still many problems in monitoring LSP that still need to be solved especially for more quantitative measurements used in improving consistent quality.

Table 2. LSP Parameters for LSP Experiment 1

Laser Energy: 300 mJ
Laser Wavelength: 1064 nm
Spot size: 1.4 mm
Pulse Repetition Rate (PRR): 10 Hz
Laser Speed Feed Rate: 300 mm/min
Water layer = 8.5 mm (conventionally 1 to 2 mm for a 10ns pulse duration)
Pulse duration = 51ns (commonly used is 10ns)

Table 3. LSP Parameters for LSP Experiment 2

Laser Energy: 1000 mJ
Laser Wavelength: 1064 nm
Spot size: 1.4 mm
PRR: 8 Hz
Laser Speed Feed Rate: 300 mm/min
Water layer = 8.5 mm (conventionally 1 to 2 mm for a 10ns pulse duration)
Pulse duration = 10ns (commonly used is 10ns)

The present work investigates the effects of varying laser energies and confinement layers during LSP. The experimental setup involves applying controlled laser pulses to the material surface under different conditions [9]. In-situ monitoring is performed, capturing AE generated during the LSP process [1]. AE signals can reveal information about the material's dynamic response to the laser-induced shock waves [6]. Tables 4 and 5 display the LSP output parameters used for the two Experiments, 1 and 2. Table 4 does not display Power Density as for Experiment 1, the % remained constant at 100%. To mitigate against uncertainty of measurement, Tables 2 and 3 display the LSP parameters, where constants are maintained and for the phenomena of interest, parameters varied. Not just the LSP parameters, but also the sensors are kept at equal distances where both are added to each other and divided by two to normalise measurement from the doppler shift effect. The sensors parameters are also kept constant such as previously mentioned with the amplifier gain fixed at 20 dB . Pencil break calibration tests and automatic sensor tests, provide individual sensor measurement
discrepancies to also be added to the sensor's measurement in terms of a gain factor and further proof, of an acceptable measurement setup.

Table 4. LSP Settings for Experiment 1

Test

Number

Confinement

Layer

Gain dB

Test 1 | air | 60
Test 2 | air | 20
Test 3 | air | 20
Test 4 | air | 20
Test 5 | air | 40
Test 6 | air | 40
Test 7 | air | 40
Test 8 | gel | 40
Test 9 | water | 40
Test 10 | water | 40

Table 5. LSP Settings for Experiment 2

Test Number | Confinement Layer | Power Density (%) | Gain dB
Test 1 | air | 50 | 20
Test 2 | air | 50 | 20
Test 3 | air | 50 | 20
Test 4 | air | 50 | 20
Test 5 | air | 50 | 20
Test 6 | gel | 96 | 20
Test 7 | gel | 96 | 20
Test 8 | water | 98 | 20
Test 9 | water | 98 | 20
Test 10 | water | 98 | 20
Test 11 | water | 98 | 20
Test 12 | water | 98 | 20
Test 13 | water | 50 | 20
Test 14 | water | 50 | 20
Test 15 | water | 50 | 20
Test 16 | water | 15 | 20

Results

The original version of this paper is available on https://www.scientific.net/EI.19.39.pdf

This section looks at the two main focused studies, LSP confinement layer differences based on AE measurements and different power densities used in LSP also using AE as a measurement differentiator. These two focuses are important as the very nature in obtaining measurement is in regard to repeatable, accurate and trustworthy results. If this is not carried out, the results obtained for further work will not be fit for purpose and render the work and understanding not met beyond reasonable doubt. These results will consider if the correct amplifier gain was used, the correct attachment of the sensor and, the effects of a moving actuator laser system.

Experiment 1 Investigating LSP and confinement layers through acoustic emission.

The original version of this paper is available on https://www.scientific.net/EI.19.39.pdf

[picture]

Fig. 1. Acoustic Emission Output Parameters Duration (s) and Counts (n) for Tests 5, Test 8 and Test 9 .

Fig. 1. Acoustic Emission Output Parameters Duration (s) and Counts (n) for Tests 5, Test 8 and Test 9 .

[picture]

Fig. 2. Acoustic Emission Output Parameters Amplitude (dB) and Rise Time (s) for Tests 5, Test 8 and Test 9.

Fig. 2. Acoustic Emission Output Parameters Amplitude (dB) and Rise Time (s) for Tests 5, Test 8 and Test 9.

Figures 1 and 2 display AE parameters, which are recorded as cyclic data quantities as opposed to continuous waveforms which are very much computationally intensive. The data quantities that were considered the most significant AE parameters were AE duration, AE counts, AE Amplitude and AE Rise Time. Cyclic data quantities refer to summary statistics calculated for a specific AE attribute of the continuous waveform. Such data, if sensitive, can be very useful for control requirement purposes of LSP however the trends need to be sensitive to change and provide a good level of resolution otherwise, they will not be useful for the requirement in real-time control.

[picture]

Fig. 3. Acoustic Emission single pulse for LSP (A): Time series pulse and Bottom (B): Short-time Fourier Transform of Test 5. (C): Time series pulse and Bottom (D): Short-time Fourier Transform of Test 8 and (E): Time series pulse and Bottom (F): Short-time Fourier Transform of Test 9.

Fig. 3. Acoustic Emission single pulse for LSP (A): Time series pulse and Bottom (B): Short-time Fourier Transform of Test 5. (C): Time series pulse and Bottom (D): Short-time Fourier Transform of Test 8 and (E): Time series pulse and Bottom (F): Short-time Fourier Transform of Test 9.

Figure 3 displays the continuous time series waveform (A) and Short-time Fourier Transform (STFT) of the same waveform (B). The amplifier gain was set to 40 dB and the response obtained is well within the limit of the AE sensor. As the response of Figure 3 (A) is mid-way of the AE sensor range (full range 10 V ), if all the other signals in the experiment were well within this threshold, this would be a good amplifier gain for all tests. In addition, the size window choice for STFT in Figure 3 (B) is good, as all the frequency feature in terms of the amplitude and duration are present. Such continuous signal data could be very useful for distinguishing between different LSP parameter settings. The AE sensor has an operating range from 70 to 1000 kHz and anything outside of this should be ignored. Nevertheless, we can see strong peak amplitude harmonics from the 100kHz,200kHz,275kHz,330kHz,430kHz,550kHz right up to 700 kHz .

Figure 3 displays the continuous time series waveform (C) and STFT of the same waveform (D). The amplifier gain set to 40 dB and again, the response obtained is well within the limit of the AE sensor. The AE STFT Figure 3 (D) has strong peak amplitude harmonics from the 120kHz,200kHz, 275kHz,370kHz,430kHz,550kHz right up to 750 kHz . The duration of intensity however is less than Figure 3 and again could be a good differentiator. With the same energy density used for LSP, both responses see Figures 3 and 4 (A and C ) display a similar amplitude amount.

Figure 3 displays the continuous time series waveform (E) and STFT of the same waveform (F). The amplifier gain set to 40 dB and again, the response obtained is well within the limit of the AE sensor. The AE STFT Figure 3 (D) has strong peak amplitude harmonics from the 120kHz,150kHz, 300kHz,330kHz, and little else after. These differences among the different confinement layers where Figure 3 (A and B) displays signals of LSP in air, Figure 3 (C and D) with gel and Figure 3 (E and F), water. Water appears to have more direct confinement for energy and with higher energy amplitude confined to lower frequency bands - all these signals show differences and could be used for controllability when considering the correct application of confinement layer. Moreover, in air, significant energy dispersion results in lower amplitude signals. In contrast, water confines energy more effectively, directing it and concentrating higher amplitude(s) in lower frequency bands. The duration of intensity however is less again than both Figures 3 (A and C) and again could be a good differentiator. With the same energy density used for LSP both responses see Figure 3 (E) displays an amplitude amount almost at the saturation point of the sensor and for follow on tests a lower amplifier gain would be more suitable.

Experiment 2 Investigating LSP and different power energies through acoustic emission

The original version of this paper is available on https://www.scientific.net/EI.19.39.pdf

[picture]

Fig. 4. Acoustic Emission Output Parameters Duration (s) and Counts (n) for Tests 9, Test 15 and Test 16 .

Fig. 4. Acoustic Emission Output Parameters Duration (s) and Counts (n) for Tests 9, Test 15 and Test 16 .

[picture]

Fig. 5. Acoustic Emission Output Parameters Amplitude (dB) and Rise Time (s) for Tests 9, Test 15 and Test 16.

Fig. 5. Acoustic Emission Output Parameters Amplitude (dB) and Rise Time (s) for Tests 9, Test 15 and Test 16.

Looking at AE parameters from the non-continuous data displayed in Figures 4 and 5, the AE duration and counts are good differentiators; however, the AE amplitude is not. This should not be the case as there are very different power settings for Experiment 2. That said the amplitude in dB is not a sensitive as voltage seen in continuous signals and therefore the AE amplitude parameter in this case is a poor differentiator for LSP. That said, the AE rise time may also be poor differentiator if Test 9 overlays with Tests 15 and 16. For these results there needs to be further signal processing to carry out segregation between the different power settings and effects against the material under test.

Figure 6 displays the continuous waveform time series (A) and the STFT (B) - comparing with the other tests these are both good differentiators and would be the preferred choice for simulation models as well as machine learning models. Therefore, the amplifier setting of 20 dB is sufficient for effectively differentiating different parameters within LSP as the power density is set near to the maximum and the response is well within the sensor mid-range which is ideal for response comparison. Figure 8 STFT (Bottom) displays a lot of amplitude harmonics right up to 750 kHz which correlates with the results obtained in Experiment 1 and bridges the results with confidence here. With water confinement, the duration of STFT is again low, comparing well with results displayed in Figure 3 (F).

[picture]

Fig. 6. Acoustic Emission single pulse for LSP (A): Time series pulse and (B): Short-time Fourier of Test 9. (C): Time series pulse and (D): Short-time Fourier of Test 15. (E): Time series pulse and (F): Short-time Fourier of Test 16.

Fig. 6. Acoustic Emission single pulse for LSP (A): Time series pulse and (B): Short-time Fourier of Test 9. (C): Time series pulse and (D): Short-time Fourier of Test 15. (E): Time series pulse and (F): Short-time Fourier of Test 16.

Figure 6 displays the continuous waveform time series (C) and the STFT (D) - comparing with Figure 6 (A and B), its less, as its 50% energy density for LSP and so the amplifier setting of 20 dB is sufficient to effectively differentiate parameters within the LSP signal. Very similar STFT to Figure 6 (B) albeit the amplitude intensity is less which is expected as the applied energy density is nearly 50% less. The frequency band harmonics are similar to Figure 6 (B).

Figure 6 displays the continuous waveform time series (E) and the STFT (bottom) - comparing with Figures 6 ( A and B ) and ( C and D ), its less than both as its 15% energy density for LSP. The STFT to Figure 6( F) is not the same as Figures 6( B and D) as the duration of the frequency bands are a lot larger. Could this be to less intense noise present and therefore not masking the higher resolution data? This would need further investigations in future work but nevertheless provides a solid avenue for focus.

Figure 7 displays two responses of Barkhausen noise, indicative of a combination of residual stress and hardness. The Barkhausen noise is an electromagnetic post LSP response of the material. The baseline response is overlayed and greater in signal strength than the water confined 100% energy density. This suggests that the water-confined sample exhibits greater material hardness compared to
the baseline, the baseline however, has an amplitude of 147.4 pmp and water confined 100% energy density, an amplitude of 143.20 mp . As the latter is less, this is significant of being harder where an increased value (higher response) is significant to a softer material.

[picture]

Fig. 7. Stress Tech 350 , Barkhausen Noise response where the base line condition (black response) and water confinement at 100% (gold response)

Fig. 7. Stress Tech 350 , Barkhausen Noise response where the base line condition (black response) and water confinement at 100 % (gold response)

Discussion of results. The analysis of the gained in-situ data, in the form of profiles and AE signals, reveals distinct characteristics associated with different laser energies and confinement conditions. Higher laser energies typically lead to stronger AE signals[7]. The presence and type of confinement layer also significantly influence the shock pressure generated and, consequently, the material's response[1].

Post-process NDT measurements demonstrate the effectiveness of LSP in inducing compressive residual stresses and altering surface hardness[3]. The magnitude and depth of the CRS layer are found to be dependent on the laser parameters used[7]. Correlations are observed between the characteristics of the in-situ multispectral data and the measured residual stress and hardness values. For instance, specific features in the AE signals, such as amplitude and frequency content, may be indicative of the level of compressive stress induced[3] Similarly, the rate and magnitude of the temperature change during LSP could be related to the depth of the affected zone[1].

The NDT measurements correlate well with recent X-ray diffraction (XRD) residual stress measurements and are currently being used in machine learning models determining the levels of residual stress through AE measurements. The AE has demonstrated similar features found in previous work [10-11] used for a similar purpose in developing machine learning models for control of surface quality.

Conclusions

The original version of this paper is available on https://www.scientific.net/EI.19.39.pdf

The results provided by this work are very encouraging going forward where the correct sensor sensitivities and optimisation were found. The LSP process is very noise intensive and therefore 20 dB , the lowest amplifier gain setting, is the most suitable when considering all tests, especially with the higher laser power densities. It was noticed that there is still variation within the obtained AE data, and this is down to the effects of LSP outside the confinement layer/applied protective coating. Interestingly, the water confinement layer directs the laser shock pulse and hence the higher amplitude intensity is detected as an AE response. In addition, different duration and frequency bands are experienced for different energy densities and this is interesting from a differentiation point of view to provide control through intelligent algorithms. Results achieved in this work link well with other recent similar works [10-11] where AE patterns correlate to different levels of both elastic and plastic material deformation.

For future work the integration of machine learning algorithms with the collected data holds the potential for developing digital models capable of predicting the quality of the LSP process in real-time[5]. By training these models on the relationships between LSP parameters, in-situ
multispectral data, and post-process material properties, it becomes possible to assess the effectiveness of the LSP treatment during the process itself. That said, more boundary conditions and corresponding data is required to build a more robust machine learning system that can differentiate between foreseen and unforeseen data. This capability is crucial for real-time, online quality control and can lead to more efficient and reliable manufacturing processes which can be applied in a closed loop control fashion for industrial application.

Acknowledgements

The original version of this paper is available on https://www.scientific.net/EI.19.39.pdf

This research is supported by the EPSRC funded Innovation Launchpad Network+ (No. RIR26C230615-3). Without such a program initiative, this work would not have been possible.

References

The original version of this paper is available on https://www.scientific.net/EI.19.39.pdf

X. Meng, H. Wang, W. Tan, J. Cai, J. Zhou and L. Liu, (2020), Gradient microstructure and vibration fatigue properties of 2024-T351 aluminum alloy treated by laser shock peening, Surface and Coatings Technology, Volume 391, 125698.

Ozevin, D, (2020), "MEMS Acoustic Emission Sensors" Applied Sciences 10, no. 24: 8966.

R. Qin, Z. Zhang, J. Huang, Z. Du, X. Xiang and G. Wen, (2023), Acoustic emission for surface quality monitoring in laser shock peening via dual-feature fusion convolution neural network, Optics and Laser Technology 164, 109505.

J. Wu, J. Zhao, H. Qiao, B. Sun and Y. Lu. (2020). A new acoustic emission on-line monitoring method of laser shock peening, Optik, Volume 205, 163578,

S. Nasiri, M. R. Khosravani, (2021) Machine learning in predicting mechanical behavior of additively manufactured parts, Journal of Materials Research and Technology, Volume 14, Pages 1137-1153,

A. G. Sanchez, M. Leering, D. Glaser, D. Furfari, M. E. Fitzpatrick, J. A. Wharton, P. A. S. Reed (2021), Effects of laser shock peening on the mechanisms of fatigue short crack initiation and propagation of AA7075-T651, Surface Engineering.

S. Banerjee, J. P. Phillips, J. Nygaard, P D. Mason, K. Ertel, M. De Vido, T. Butcher, S. Tomlinson, J. Smith, R. Allott, C. Edwards, and J. Collier, (2019), Acoustic signature of laser shock peening for a qualitative evaluation of residual stresses, Applied Physics A, Vol 125, 8.

Kaleris, K., Stelzner, B., Hatziantoniou, P., Trimis, D., & Mourjopoulos, J. (2021). Laser-sound: optoacoustic transduction from digital audio streams. Scientific Reports, 11, 476

P. Shukla, H. Wu, and R. Crookes (2019), Shock-wave induced compressive stress on alumina ceramics by laser peening, Materials and Design 167107626

Qin, R., Zhang, Z., Griffin, J. M., Huang, J., Wen, G., He, Weifeng., Chen, Xuefeng, (2025), Incorporating machine learning in shot peening and laser peening: A review and beyond, Advanced Engineering Informatics, Volume 65, Part C, .

Qin, R., Zhang, Z., Griffin, J.M., Wang, J., Wen, G., He., Chen, X., Huang, J., (2025) Explainable deep learning framework for residual stress monitoring in laser shock peening via acoustic emission, Journal of Industrial Information Integration, Volume 47, 100904, ISSN 2452-414X, .

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The original version of this eBook is available on https://www.scientific.net/book/engineering-innovations-vol-19/978-3-0364-1217-7

Engineering Innovations, ISSN: 2813-1002, Vol. 19, pp 51-64

doi: 10.4028/p-Z7EgR3

© 2026 The Author(s). Published by Trans Tech Publications Ltd, Switzerland.

Submitted:
2025-09-01

Revised:
2026-02-02

Accepted:
2026-02-14

Online:
2026-06-11

Improved Performance Assessment of Photovoltaic Systems Using Paraffin-Based Phase Change Materials and Innovative Container Designs

The original version of this paper is available on https://www.scientific.net/EI.19.51.pdf

Zainal Arifin 1,a*, Ubaidillah 1,b, Wibawa Endra Juwana 1,c, Rendy Adhi Rachmanto 1, d, Eflita Yohana 2,e, Denny Widhiyanuriyawan 3,f, Noval Fattah Alfaiz 1, g, Singgih Dwi Prasetyo 4, h, Mohd Afzanizam Mohd Rosli 5,i

¹Department of Mechanical Engineering, Universitas Sebelas Maret, Surakarta 57126, Indonesia

2 Department of Mechanical Engineering, Universitas Diponegoro, Semarang 50275, Indonesia

3 Department of Mechanical Engineering, Universitas Brawijaya, Malang 65145, Indonesia

4 Power Plant Engineering Technology, State University of Malang, Malang 65145, Indonesia

5 Fakulti Teknologi dan Kejuruan Mekanikal, Universiti Teknikal Malaysia Malaka, Hang Tuah Jaya, 76100 Durian Tunggal, Melaka, Malaysia

*Corresponding author: zainal_arifin@staff.uns.ac.id

a  zainal_arifin@staff.uns.ac.id, b  ubaidillah_ft@staff.uns.ac.id, c  wibawa_ej@gmail.com, d  rendy.ar@gmail.com, e  eflitayohana@live.undip.ac.id, f  denny_w@ub.ac.id, g  viozgaul@student.uns.ac.id, h  singgih.prasetyo.fv@um.ac.id, iafzanizam@utem.edu.my 

Keywords: Phase Change Material (PCM), Photovoltaic Thermal (PVT), Computational Fluid Dynamics (CFD)

Abstract

Integrating Phase Change Material (PCM) in Photovoltaic Thermal (PVT) systems is an effective solution to improve the cooling efficiency of solar panels. This study modeled and analyzed three configurations of paraffin-based PCM containers using the Computational Fluid Dynamics (CFD) approach, with radiation intensities of 300,600,900, and 1200 W/m2. The temperature distribution analysis showed that Model 1, with its design lacking additional partitions, produced a more homogeneous heat distribution and maintained lower PV-cell temperatures than Models 2 and 3. Model 1 recorded a maximum temperature of 27.4∘C at its highest intensity, while Model 2 and Model 3 reached 28.5∘C and 27.5∘C, respectively. Efficiency evaluations show that the Model 1 also produces the highest thermal efficiency, reaching 11.7% at 1200 W/m2, compared with the Model 2 at 10.5% and the Model 3 at 11.2%. The Model 1's superior performance is attributed to the PCM's ability to absorb latent heat effectively, slow the rise in PV cell temperature, and maintain stable energy conversion efficiency. Based on these results, Model 1 is recommended as the optimal configuration for PVT-PCM systems to improve thermal efficiency, maintain PV-cell performance, and extend the operational life of solar collector systems.

Introduction

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The use of solar energy is skyrocketing as the need for clean and sustainable energy increases in various sectors [1,2]. One of the main technologies of concern is the solar collector system, which functions to absorb solar radiation and convert it into heat energy [3,4]. Thermal collectors have an essential role in water heater applications, drying systems, and photovoltaic-thermal (PVT) systems that combine electricity and heat production in a single unit [5,6]. However, this system's main challenge is maintaining optimal thermal performance amid dynamic variations in solar intensity. The temperature instability of the working fluid can lead to overheating, which lowers the efficiency of the solar panel and shortens the overall life of the system [7,8].

One of the solutions that is now being developed to overcome this problem is the integration of Phase Change Material (PCM) in thermal collector design [9,10]. PCM stores and releases energy in latent heat during phase change processes, from solid to liquid and vice versa [11,12]. This capability allows the system to absorb excess heat when temperatures are high and release it when they drop, so
the collector's working temperature can be controlled passively. PCM integration has also been proven to improve thermal stability, extend optimal operating duration, and reduce extreme temperature fluctuations. This technology is increasingly relevant to PVP systems that require effective heat management without adding mechanical complexity [13,14].

Various studies have shown that applying PCM in solar collectors can significantly improve thermal efficiency and electrical performance [15,16]. Previous research has reported an increase in electrical efficiency of up to 17% and thermal efficiency of more than 60% with various innovations such as PCM container geometry, the use of metal foam, as well as combinations with nanoparticle technology [17,18]. Modification of the structure and material properties of PCM is one of the main focuses to get the best performance according to environmental conditions and application needs [19,20]. In addition to experiments, numerical simulation-based approaches are increasingly used because they can provide in-depth visualization and high accuracy in modeling heat transfer and fluid flow phenomena. Thus, computational methods such as Computational Fluid Dynamics (CFD) become a very potent tool in the exploration and evaluation of PVT-PCM systems [21,22].

Table 1 presents key findings from recent research on innovative approaches to optimizing PCMbased collector systems. These innovations include container geometry experiments, the addition of metal foam structures, efficient mathematical modeling, and the integration of ultrasound and nanoparticle technology. These results open new horizons in understanding how design and material parameters can be optimized to improve overall system performance. However, across these studies, there remain research gaps, especially in comparative performance between PCM types through standardized, systematic numerical simulations. This is important because selecting the correct type of PCM will significantly determine the technology's efficiency and success in the field.

Table 1. State of the art

Reference | Novelty | PCM | Findings
[17] | CFD study of PCM container geometry configuration (trapezoid vs square) and the use of isolation | RT42, RT31, RT25 | Electricity efficiency increased by 17%, power output increased by 14.6%; trapezoidal containers are superior in cooling; Insulation increases PCM volume requirements
[23] | 2D transient CFD simulation for two organic PCMs with real-time data | Rubitherm 28 and 35 HC | Peak power increased by 10%, total annual energy increased by 3.5%; PCMs with low melting points fail to complete solidification at night
[24] | Direct experiment and simulation of CFD using soybean wax as an environmentally friendly organic PCM | Soybean wax | PV temperature drops from 60.7°C to 54.7°C (at 1100 W/m²); efficiency increased by 0.42% (at 900 W/m²); Cheap price and can be used repeatedly
[4] | PV–PCM integration with solar chimney and fin absorbers, as well as composite PCM | Paraffin/Copper Foam (Pa/CF), Sodium Acetate Trihydrate/Copper Foam (SAT/CF) | Electrical power increased by 16%, ventilation duration increased by 101%; Pa/CF gives a top output of 5.54 kWh
[11] | Numerical simulation of nano-PCM with various nanoparticles and variations of PV tilt angle | RT25HC + MgO, TiO₂, ZnO, CuO | Electrical efficiency 11.6% (horizontal); nano-PCM keeps the panel temperature 4°C lower; The best efficiency occurs when the orientation is horizontal

Based on previous studies, it is evident that there are still limitations in systematically comparing the thermal performance of various PCM configurations, especially in the use of detailed, accurate numerical simulation approaches. To address the research gap, the study focused on the thermal analysis of solar collector systems integrating three PCM container models via CFD simulations. The PCM used is paraffin, selected for its suitable melting point, high thermal stability, and broad applicability in PVP systems. With this approach, this research is expected to make a scientific contribution in determining the most optimal PCM configuration and material, while supporting the development of a more efficient, economical, and sustainable solar collector system design for future energy needs.

Method

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This research used a Computational Fluid Dynamics (CFD) simulation to evaluate the thermal characteristics of a Photovoltaic Thermal (PVT) system integrated with Phase Change Material (PCM) as a passive cooling medium. Paraffin was chosen as the PCM material due to its superior thermophysical properties for absorbing and storing energy via a latent heat transfer mechanism, which significantly improves thermal efficiency and stability of photovoltaic cell performance. Heat fluxes of 300 W/m2,600 W/m2,900 W/m2, and 1200 W/m2 were applied to the panel surface to assess the system's thermal response to varying solar radiation intensities. The system configuration consists of two main layers: a top layer of PV cells and a bottom layer of PCM containers, as shown in Figure 1. The geometric dimensional details of the three numerically analyzed PCM container designs are presented in Table 2 and further visualized in Figure 2.

[picture]

Fig. 1. Details of the arrangement of layers on the PVT-PCM system.

Fig. 1. Details of the arrangement of layers on the PVT-PCM system.

Table 2. Specification of the geometric size of PCM containers.

Specification | Detail
Material | Aluminum
Total size (W × W × H) | 660 × 550 × 42 mm

[picture]

Fig. 2. Geometric configurations of the three PCM container models: (a) Model 1 without partitions, (b) Model 2 with horizontal partitions, and (c) Model 3 with segmented internal barriers, designed to evaluate the influence of structural variation on heat transfer behavior.

Fig. 2. Geometric configurations of the three PCM container models: (a) Model 1 without partitions, (b) Model 2 with horizontal partitions, and (c) Model 3 with segmented internal barriers, designed to evaluate the influence of structural variation on heat transfer behavior.

The geometric design of the PV-cell and PCM container systems was developed in SolidWorks, enabling the creation of precise, detailed 3D models. The modeling process was conducted on a laptop equipped with an AMD Ryzen 5 7520U processor with Radeon Graphics, operating at 2.80 GHz ,
ensuring adequate computational power for the geometry construction and preprocessing stages. The numerical simulation of phase change phenomena in the Phase Change Material (PCM), including melting and solidification processes, was performed using ANSYS Fluent versions 18.2 and 22 under transient conditions. A mesh element size of 12 mm was selected for the final simulation to ensure an accurate representation of flow behavior and temperature distribution within the system domain. With this mesh configuration, the total number of elements generated for the collector model was 27,404 and 45,399 for Model 1, 29,500 and 52,216 for Model 2, and 29,905 and 53,054 for Model 3, respectively. As presented in Figure 3, the variation in maximum PV temperature and thermal efficiency becomes negligible when the mesh size is refined from 12 mm to 8 mm , with deviations below 0.2% for all models. This confirms that the selected 12 mm mesh has achieved grid independence and provides an optimal balance between numerical accuracy and computational cost. All simulations were conducted based on the thermophysical properties of PV cells and PCM materials described in Table 3.

Table 3. Material Characteristics [24-26].

Material | Density (kg/m3) | Specific Heat (J/kg-K) | Thermal Conductivity (W/m-K) | Viscousity (kg/m-s) | Pure Solvent Melting Heat (J/kg) | Solidus Temperature (K) | Liquidus Temperature (K)
PV-Cell | 2330 | 667 | 130 | - | - | - | -
Aluminium | 2719 | 871 | 202.4 | - | - | - | -
Paraffin | @800 @790 | 2150 | 0.2 | 0.01 | 240800 | 313.6 | 317.7

[picture]

Fig. 3. Mesh sensitivity analysis of the PVT-PCM system showing the maximum PV temperature and thermal efficiency for 16 mm,12 mm, and 8 mm mesh sizes at 1200 W/m2, confirming the grid independence of the selected 12 mm mesh.

Fig. 3. Mesh sensitivity analysis of the PVT-PCM system showing the maximum PV temperature and thermal efficiency for 16 m m , 12 m m , and 8 mm mesh sizes at 1200 W / m 2 , confirming the grid independence of the selected 12 mm mesh.

Theoretical Foundation & Formulation

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Numerical analysis of the PVT-PCM system was carried out using the Energy Conservation approach, where the temperature distribution and heat transfer process were modeled with the following energy equations [27]:

∂∂t(ρh)+∇×(ρuh)=∇×(k∇T)(1)

In systems involving Phase Change Materials (PCM), the enthalpy model describes the total energy accumulation, including sensible and latent energy during phase change. Total enthalpy is formulated as follows [28]:

h=Cp(T−Tref)+ΔHf×fL(2)

The liquid fraction of PCM (in the range 0 to 1 ) is the latent heat of melting, and Cp is the specific heat capacity. The heat transfer process in PCM and PV cells follows the principles of conduction and convection. For heat conduction, Fourier's law is applied fLΔHf [23]:

q=−k×∇T(3)

Meanwhile, Newton's law explains the heat transfer between the PV surface and the environmental fluid. [29]:

q=hc×A×(Ts−T∞)(4)

To evaluate the thermal performance of the collector system, a thermal efficiency formula based on the increase in PCM temperature is used [30]:

ηthermal =Irad×Am×Cp×ΔT(5)

To assess the effectiveness of PCM in lowering the temperature of PV [31]:

ηpv=ηref[1−βref(Tc−Tref)](6)

Values are obtained through simulations. TC

Result & Discussion

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The performance of the PVP system was assessed using numerical modeling in ANSYS Fluent. The Solidification and Melting module represents the thermal behavior and phase transition of Phase Change Material (PCM) heat storage materials. PCM is considered a strategic component of heat energy storage and distribution systems due to its ability to absorb and release heat efficiently. Therefore, the simulation is designed to trace the impact of PCM application on improving the overall thermal performance of the system. Computational Fluid Dynamics (CFD) techniques are applied comprehensively to describe the energy transfer process, PCM melting rate, and heat transfer efficiency. The simulation was carried out under transient conditions using ANSYS R18.2 to help students obtain a comprehensive picture of the PVT system's dynamic thermal behavior.

Temperature Distribution Analysis on PCM

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Figure 4(a-c) illustrates the temperature distribution of Phase Change Material (PCM) in three different structural configurations. The color in the image corresponds to local temperature, with a gradient from blue (cold) to red (hot) indicating the temperature profile. In Model 1 (Figure 4a), the temperature distribution is relatively homogeneous, with only a few localized hotspots. This shows good heat-conduction efficiency in the PCM, enabling effective thermal diffusion. This model shows that without many internal partitions, the PCM structure can accelerate temperature homogenization, optimizing latent energy storage during the transition phase. In contrast, Model 2 (Figure 4b) shows horizontal partitions that lead to heat accumulation in certain areas. This distribution pattern indicates that lateral (horizontal) heat transfer is impeded, resulting in a sharper temperature gradient between layers. While this barrier can slow overall PCM melting, it can be helpful for applications that require controlled thermal release. Model 3 (Figure 4c) shows a more fragmented heat distribution, with hotspots spread over small areas. This suggests that the additional bulkheads significantly inhibit heat flow, thereby providing greater local thermal insulation. Although heat distribution becomes more uneven, this characteristic helps extend the duration of heat discharge in systems that require longterm thermal stability.

[picture]

Fig. 4. Numerical visualization of temperature distribution within the PCM domain for (a) Model 1, (b) Model 2, and (c) Model 3 under identical radiation conditions, illustrating differences in heat diffusion and melting behavior.

Fig. 4. Numerical visualization of temperature distribution within the PCM domain for (a) Model 1, (b) Model 2, and (c) Model 3 under identical radiation conditions, illustrating differences in heat diffusion and melting behavior.

Temperature Distribution Analysis in PV-Cell

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Figure 5(a-c) presents the temperature distribution on the surface of the PV-cell after integration with the PCM in the same three configurations. In Model 1 (Figure 5a), the heat distribution shows a fairly even pattern across the PV surface. This signifies that PCM effectively controls PV temperatures, keeping surfaces within the optimal operating temperature range. This homogeneity
contributes to increased photovoltaic efficiency, as solar cell performance is sensitive to temperature. In Model 2 (Figure 5b), heat accumulates in several limited areas. This condition indicates reduced cooling effectiveness in certain regions due to the bulkhead structure's horizontal thermal resistance. As a result, the potential for PV performance degradation is greater at these hotspots, potentially accelerating PV material aging in the long run. Meanwhile, in Model 3 (Figure 5c), the temperature distribution is more focused on the subsurface. This indicates that the cooling is more concentrated, yet uneven across the PV surface. While this slows the rise in surface temperatures, the uneven heat distribution can still lead to microthermal stress, risking a degradation of PV's long-term efficiency.

[picture]

Fig. 5. Numerical visualization of PV-cell surface temperature distribution for (a) Model 1, (b) Model 2, and (c) Model 3, highlighting the effect of PCM configuration on cooling performance.

Fig. 5. Numerical visualization of PV-cell surface temperature distribution for (a) Model 1, (b) Model 2, and (c) Model 3, highlighting the effect of PCM configuration on cooling performance.

Figure 6 illustrates the results of a comparative analysis of PV-cell temperature variations across three paraffin-based PCM container models at light intensities of 300,600,900, and 1200 W/m2. At an intensity of 300 W/m2, Model 2 shows the highest temperature of 27∘C, while Model 1 performs best in maintaining the lowest temperature at 26.2∘C. When the intensity was raised to 600 W/m2, the same trend persisted: the Model 2 recorded 27.3∘C, while the Model 1 remained at 26.4∘C. At an intensity of 900 W/m2, the temperature difference between the three models becomes more pronounced, with Model 2 reaching 28∘C. In contrast, the Model 1 and Model 3 maintain lower temperatures in the range of 27∘C to 27.3∘C, respectively. Finally, at the highest intensity of 1200 W/m2, the Model 2 again shows a maximum temperature of 28.5∘C, while the Model 1 and Model 3 record 27.4∘C and 27.5∘C, respectively. Based on this pattern, it can be concluded that Model 1 consistently inhibits PV-cell temperature rise better than Models 2 and 3.

[picture]

Fig. 6. Comparative analysis of maximum PV-cell temperature under solar radiation intensities of 300−1200 W/m2 for the three PCM models.

Fig. 6. Comparative analysis of maximum PV-cell temperature under solar radiation intensities of 300 − 1200 W / m 2 for the three PCM models.

Analysis of Thermal Regulation Efficiency in PV-Cells

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Figure 7 shows a comparative analysis of the variation in energy conversion efficiency in PV-cells based on three PCM usage models at various solar radiation intensities ( 300,600,900, and 1200 W/m2 ). The data showed that PV-cell efficiency increased with increasing radiation intensity across all models, but there was a difference in efficiency performance among the three models. At low intensity (300 W/m2), Model 1 recorded the highest efficiency of 5.3%, followed by Model 3 at 5.1% and Model 2 at 5.0%. An increase in intensity of up to 600 W/m2 shows a similar trend, with Model 1 maintaining better performance ( 7.3% ) than Model 2 ( 6.5% ) and Model 3(6.8%). At 900 W/m2, Model 1's efficiency remained higher at 9.5%, followed by Model 3 ( 9.1% ) and Model 2 ( 8.8% ). At its highest intensity, 1200 W/m2, the Model 1 maintains its advantage, with an efficiency of 11.7%, compared to the Model 2 at 10.5% and the Model 3 at 11.2%.

[picture]

Fig. 7. Comparative analysis of PV-cell electrical efficiency under solar radiation intensities of 300−1200 W/m2 for the three PCM models.

Fig. 7. Comparative analysis of PV-cell electrical efficiency under solar radiation intensities of 300 − 1200 W / m 2 for the three PCM models.

The consistently higher efficiency of Model 1 across intensity variations demonstrates the significant role of the PCM's characteristics. The effectiveness of PCM in absorbing heat during phase change contributes to temperature control of PV cells. By maintaining a lower and stable PV-cell operating temperature, the PCM on Model 1 can reduce the thermal energy losses that typically occur at high temperatures, thereby improving the efficiency of electrical energy conversion. This phenomenon is consistent with the thermal theory, which predicts that PV-cell efficiency decreases as cell temperature increases. Optimal PCM acts as a latent heat storage medium, absorbing excess heat without causing a drastic rise in temperature and releasing it gradually as the intensity decreases. Thus, the PCM used in Model 1 is more effective at slowing the degradation of efficiency due to temperature fluctuations than the PCM used in Models 2 and 3. This is because model 1 can absorb excess heat energy without causing temperature spikes, thanks to a latent heat storage mechanism, and then release energy gradually as the radiation intensity decreases. The Model 1's design, which does not use partitions, allows for more even and efficient heat distribution compared to the Model 2 and Model 3, which use internal dividers. Uniform heat distribution accelerates the process of heat absorption by PCM, reduces internal temperature gradients, and slows down the rate of efficiency degradation due to local heat accumulation (hot spots) [32,33].

Conclusion

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This study has conducted numerical modeling and analysis of the Photovoltaic Thermal (PVT) system integrated with paraffin-based Phase Change Material (PCM) using three different container configurations. Simulations based on Computational Fluid Dynamics (CFD) were conducted to evaluate the characteristics of the temperature distribution and PV-cell efficiency variations in response to changes in solar radiation intensity from 300 to 1200 W/m2. Each PCM container model exhibits a unique thermal distribution pattern, which directly impacts cooling performance and PVcell surface temperature stability. The results of the temperature distribution analysis showed that Model 1, with its unpartitioned container structure, produced a more homogeneous and more effective heat distribution, inhibiting temperature rise and controlling the PV-cell surface temperature more stably and at lower levels than in Model 2 and Model 3. Model 1 consistently recorded the lowest PV-cell operating temperature, with a maximum value of 27.4∘C at an intensity of 1200 W/m2, while Model 2 and Model 3 reached 28.5∘C and 27.5∘C, respectively.

Analysis of PV-cell efficiency also showed that Model 1 performed best across all radiation intensities, achieving 11.7% at the highest intensity, compared to 10.5% for Model 2 and 11.2% for Model 3. This performance is directly attributed to the PCM's ability to effectively absorb and release heat through a latent heat storage mechanism, which slows down the rise in PV-cell temperature and maintains the efficiency of electrical energy conversion. Based on all simulation results, Model 1 is the best configuration for PVT-PCM system applications, as integrating paraffin-based PCM within a container structure without additional bulkheads provides an optimal balance of thermal stability, homogeneous heat distribution, and energy efficiency. Therefore, Model 1 is recommended as a flagship design to improve thermal efficiency, maintain PV-cell electrical performance, and extend the operational life of integrated solar collector systems.

Acknowledgments

The original version of this paper is available on https://www.scientific.net/EI.19.51.pdf

This paper is the result of research entitled "Pengembangan Pembangkit Listrik Tenaga Surya Sebagai Sumber Energi Bersih Stasiun Pengisian Kendaraan Listrik Umum (SPKLU)" funded by Sebelas Maret University through the Mandatory Riset (mr-uns) scheme with contract number 369/UN27.22/PT.01.03/2025.

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The original version of this eBook is available on https://www.scientific.net/book/engineering-innovations-vol-19/978-3-0364-1217-7

Engineering Innovations, ISSN: 2813-1002, Vol. 19, pp 65-73

doi: 10.4028/p-QxgY6n

© 2026 The Author(s). Published by Trans Tech Publications Ltd, Switzerland.

Submitted:
2025-09-02

Revised:
2025-09-28

Accepted:
2026-02-14

Online:
2026-06-11

Numerical Study of Four Hydrokinetic Savonius Turbines in a Staggered Co-Rotating Configuration in the Cooling Water Channel

The original version of this paper is available on https://www.scientific.net/EI.19.65.pdf

Mohammad Riko Putra Utama 1,2,a*, Triyogi Yuwono 1, b*

¹Department of Mechanical Engineering, Sepuluh Nopember Institute of Technology, Surabaya 60111, Indonesia

2 PT PLN (Persero), Jakarta 12160, Indonesia

a  m.rikoputra@hotmail.com, b  triyogi@me.its.ac.id

Keywords: numerical study, hydrokinetic savonius turbine, staggered configuration, coefficient of power, coefficient of moment.

Abstract

The increasing demand for electrical energy in Indonesia each year, driven by economic growth, population expansion, and the industrial sector, requires a transition from fossil energy to renewable energy (RE) to support the Net Zero Emissions (NZE) target by 2060. One such effort involves utilising the cooling water channel at the Paiton Power Plant by installing staggered corotating Savonius hydrokinetic turbines. In this study, a numerical simulation method was employed using ANSYS Software, with a 2 -dimensional (2D) geometric model, unsteady flow, and a realisable k−ε viscous model (RKE). The TSR variations were 0,0.2,0.4,0.6,0.8,1.0, and 1.2 . Savonius turbines arranged in a staggered co-rotating configuration with varying turbine spacing (S/D) of 2.10 and 16.86, respectively. This study aim to evaluate the performance of the four staggered co-rotating Savonius turbines, presented as graphs of Coefficient of Power (CoP) and Coefficient of Moment (Cm) versus Tip Speed Ratio (TSR), velocity contours, and pressure contours. The results obtained from studying four Savonius turbines arranged in a staggered corotating configuration show that the greater the distance between the turbines, the better the turbine performance. At distance of S/D=2.1, each turbine has an influence on the other. At distance of S/D=16.86, each turbine no longer influences the others, as the performance of each turbine is similar to that of a single turbine, thereby achieving optimal turbine performance.

Introduction

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With the increasing demand for electrical energy and the need to support the implementation of Net Zero Emissions (NZE) by 2060, an energy transition is required from non-renewable fossil energy to new renewable energy sources. One of the efforts to increase the installed capacity of renewable power plants is to utilise existing channels at the Steam Power Plant and then install a Savonius-type hydrokinetic turbine arranged in a staggered, co-rotating manner, allowing it to convert the strength of the water current into electricity. Hydrokinetic turbines are ideal for use because they have minimal environmental impact, can produce energy efficiently, and are relatively affordable [1] [2]. However, this turbine has the disadvantage of low efficiency. To increase the energy generated, multiple turbines can be installed in a single water flow.

Chen et al. conducted a study to determine the difference between two-dimensional (2- D) and three-dimensional (3-D) simulations in predicting the wake flow and local coupling effects of the Savonius array turbine. The results obtained were that 2-D simulations can be used as an initial analysis to assess the trend of array parameters [5].

Kailash et al. conducted experiments and numerical simulations on the performance of Helical Savonius Hydrokinetic Turbines (HSHKT) arranged in a line. In this study, it was found that with increasing distance between the two turbines, the interaction between the two turbines decreased [6]. Meanwhile, Nag and Sarkar also studied the interaction between two hydrokinetic Savonius turbines arranged in a line. The results obtained are that the greater the distance between the two turbines, the performance of the two turbines will increase, with optimum performance results at distance of 4D [7].

Priandika et al, conducted research on a numerical study of two tandem Savonius turbines arranged vertically along the channel. The greater the distance between the two tandem turbines, the better the performance of both. At distance of T/D = 2.1, the two turbines interact, resulting in lower performance than that of a single turbine. At distance of T/D = 70, the rear turbine no longer affects the performance of the front turbine; however, the front turbine still affects the rear turbine. The front turbine has the same performance as a single turbine, but the rear turbine has lower performance [8].

Wulaningtyas and Yuwono, conducted research on the optimal spacing between four turbines (in T/D units - the ratio of the distance between turbines to their diameter) to avoid negative interactions between turbines. This study uses numerical simulations using Ansys Fluent 2023 R2 with four tandem turbines rotating Counterclockwise and Clockwise [9]. The distances between the turbines (T/D) studied are 2.1, 4.4, 60, and 300. At close T/D distances ( 2.1 and 4.4), the turbines influence each other, reducing the performance of the front turbine. When the distance increases to T/D=60, the rear turbine's influence decreases, allowing the front turbine to perform similarly to a single turbine. At T/D = 300, both turbines operate optimally with minimal interaction, achieving efficient performance and increased torque and power output.

Based on the background and previous studies, there has been no study that examines the performance with variations in distance on four Savonius hydrokinetic turbines arranged in a staggered co-rotating manner on a power plant cooling water channel, therefore it is important to conduct this study to determine the optimal distance between turbines, so that the energy output produced can be maximised.

Research Scheme.

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This study aims to determine the optimal spacing to achieve maximum turbine performance. The research configuration is illustrated in Figure 1. Four Savonius turbines are arranged in a staggered configuration, and all turbines rotate in a clockwise direction.

[picture]

Fig. 1. Savonius turbine configuration is arranged in a staggered configuration and rotates in the same direction.

Fig. 1. Savonius turbine configuration is arranged in a staggered configuration and rotates in the same direction.

The variations in this study are the distance between center turbine ( S ) against the diameter of blade (D) (S/D), at 2.10,8.65 and 16.86. The dimensions of Power Plant water channel are described as 15,000 mm×10,000 mm, with a water velocity of 2 m/s at a temperature of 35∘C.

Determining the Domain, Meshing and Boundary Conditions.

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The domain scheme and boundary conditions used in this study are illustrated in Figure 2, which shows two domain simulations: the rotating domain for the Savonius hydrokinetic turbine and the stationary domain for the water channel. While the boundary conditions used in this study are the inlet to define the water flow at a certain speed, the canal wall and turbine blade is defined as the wall, the outlet is defined as the pressure outlet and the interface for the boundary between the rotating domain and the stationary domain. The meshing results are shown in Figure 3.

[picture]

Fig. 2. Scheme of the Research Domain.

Fig. 2. Scheme of the Research Domain.

[picture]

Fig. 3. Savonius turbine meshing
(a) overall meshing, (b) meshing around the turbine blade.

Fig. 3. Savonius turbine meshing (a) overall meshing, (b) meshing around the turbine blade.

Processing.

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The next step is to configure the setup window for the processing stage. Settings include selecting the solver (pressure- or density-based), velocity formula (absolute or relative), time (steady or transient), and 2D space (planar, axisymmetric, or axisymmetric swirl). Next, set the model, material, cell zone conditions, and boundary conditions. This study uses a realizable k- ε turbulent model with enhanced wall treatment. Because that viscous model gives good results and shows accuracy in predicting some flow characteristics [10][11].

The material used is water with ρ=994.08 kg/m3 and μ=0.00072 kg/m·s. The gradient method uses cell-based least squares, with a second-order upwind scheme for pressure, momentum, and turbulent kinetic energy, and an absolute residual criterion of 10−5. The simulation was conducted with 3600 times steps and 20 iterations.

Model.

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The fluid flow under investigation in this study is characterized by its unsteadiness and is modelled using the Navier-Stokes equation. The equation under consideration is one employed in the field of incompressible flow, as delineated in the subsequent equation:

ρDV→Dt=−∇→P+ρg→+μ∇2V→(1)

The Navier-Stokes equation (1), when defined in Cartesian coordinates ( x,y,z ), yields the subsequent equation:

ρ(∂u∂t+u∂u∂x+v∂u∂y+w∂u∂z)=−∂P∂x+ρgx+μ(∂2u∂x2+∂2u∂y2+∂2u∂z2)..(2)

ρ(∂v∂t+u∂v∂x+v∂v∂y+w∂v∂z)=−∂P∂y+ρgy+μ(∂2v∂x2+∂2v∂y2+∂2v∂z2)..(3)

ρ(∂w∂t+u∂w∂x+v∂w∂y+w∂w∂z)=−∂P∂z+ρgz+μ(∂2z∂x2+∂2z∂y2+∂2z∂z2)(4)

The continuity equation for incompressible flow is as follows:

∂u∂x+∂v∂y+∂w∂z=0(5)

[picture]

Fig. 4. Grid Independency Test (a); Graph of Validation Result (b).

Fig. 4. Grid Independency Test (a); Graph of Validation Result (b).

Grid Independence Test and Validation.

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One way to ensure meshing quality is to increase the number of elements until the Coefficient of Moment (Cm) value stabilizes. At this stage, simulations were performed on a single Savonius turbine with elements ranging from 35,345 to 135,747 , at a TSR of 0.8 and a flow velocity of 2 m/s. Based on figure 4, at the number of elements 85199 the error of Cm value show 0.3%, there is no significant changes along with the increase in the number of elements. To validate it, a comparison was made of the Coefficient of Power (CoP) values from the simulation with the experimental data from Patel et al. [12]. As shown in Figure 4(b), the CoP value in this study has similar graphic trend to the study by Patel et al., with an average error of 2.09% or <5%, so that the simulation that has been carried out can be declared valid.

Result and Discussion

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Coefficient of Moment (Cm).

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Figure 5 shows a comparison graph of the Moment Coefficient (CM) versus Tip Speed Ratio (TSR) for staggered and single turbine configurations at S/D=2.10 and S/D=16.86. Figure 5(a) demonstrates the CM versus TSR for staggered and single turbine configurations at S/D=2.1. The CM value decreases as the TSR increases. The CM of turbines 1,2 , and 3 tend to be similar to that of a single turbine, but turbine 4 shows a significant decrease due to its rearward position, making it difficult for fluid flow to reach it and being affected by the wake effect from the previous turbine. However, turbine 4 still rotates clockwise. This indicates that at S/D=2.1, the four turbines still interact with each other. Figure 5 (b) at S/D=8.65 show that TSR 0.6 for turbine 1 is not much different from the single turbine, and the CM values of turbine 2 and turbine 3 clearly show a higher difference compared to the single turbine, turbine 1 , and turbine 4 which have the lowest CM values of the other turbines. Figure 5(c) at S/D=16.86 shows that the CM value at a TSR of 0.6 is nearly the same as that of a single turbine. This is because the distance between the turbines is far enough so that they do not affect each other, allowing the fluid flow to reach the turbine optimally and produce performance like a single turbine.

[picture]

Fig. 5. Comparison graph Coefficient of Moment against Tip Speed Ratio of staggered turbine S/D =2.1 with single turbine (a); S/D=8.65 with single turbine (b); S/D=16.86 with single turbine (c).

Fig. 5. Comparison graph Coefficient of Moment against Tip Speed Ratio of staggered turbine S/D = 2.1 with single turbine (a); S / D = 8.65 with single turbine (b); S / D = 16.86 with single turbine (c).

Coefficient of Power (CoP).

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Figure 6 compares the Coefficient of Power (CoP) versus Tip Speed Ratio (TSR) between staggered and single turbine configurations at S/D spacings of 2.10 and 16.86. At S/D=2.10 (Figure 6a), the CoP trends for all four turbines are similar to those for a single turbine, increasing to a TSR of 0.6 and then decreasing. However, the maximum CoP value for a single turbine ( 0.211 ) is higher than that of turbines 1 through 4 , especially turbine 4 , which has the lowest value ( 0.127 ), due to position and wake effects. At high TSRs (1-1.2), the CoP of turbine 1 is higher that that of a single turbine due to the solid wall effect caused by the close spacing, indicating that there is still interaction between the turbines that reduces efficiency.

Conversely, at S/D=16.86 (Figure 6b), all turbines show trends and CoP values nearly identical to those for a single turbine. This indicates that at this spacing, interaction between the turbines does not occur, thus optimizing the performance of each turbine.

[picture]

Fig. 6. Comparison graph Coefficient of Pressure against Tip Speed Ratio of staggered turbine S/D =2.1 with single turbine (a); S/D=8.65 with single turbine (b); S/D=16,86 with single turbine (c).

Fig. 6. Comparison graph Coefficient of Pressure against Tip Speed Ratio of staggered turbine S/D = 2.1 with single turbine (a); S / D = 8.65 with single turbine (b); S / D = 16 , 86 with single turbine (c).

Velocity and Pressure Contour.

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Figure 7 shows the velocity and pressure contour patterns across all turbines, which are different from those of a single turbine, due to the close spacing between the turbines, which influence each other. Turbine 4 has the lowest value due to the wake effect from the turbine in front of it, as seen from the low pressure on the concave returning blade ( -4000 to -2000 ), and the higher pressure on the convex returning blade (-2000 to 0 ). However, turbine 4 still receives acceleration from the narrow gap between turbines 2 and 4, causing the pressure to be lower on the convex advancing blade (-6000 to -2000), and higher on the concave advancing blade (-2000 to +1000 ). This pressure difference produces a positive torque, so that turbine 4 continues to rotate clockwise even though it has a smaller Coefficient of Moment (CM) value than the other turbines.

[picture]

Fig. 7. (a) Velocity Contour, (b) Pressure Contour of Turbine Configuration S/D = 2.1.

Fig. 7. (a) Velocity Contour, (b) Pressure Contour of Turbine Configuration S/D = 2.1.

Figure 8 shows the velocity and pressure contour patterns across all turbines,In turbine 1 , the flow velocity on the surface of the convex advancing blade of turbine 1 reaches more than 3.5 m/s, so that the flow velocity makes the pressure on the surface of the convex advancing blade of turbine

1 have a low value ranging from ( -7000 to 0 ), while on the other hand, on the surface of the concave advancing blade, the resulting flow velocity reaches 0.75 m/s, so that the pressure value on the surface of the concave advancing blade is higher ( +1000 to +2000 ). Meanwhile, in turbines 2 and 3, due to the influence of the distance between turbine 1 and turbine 2 and the distance between turbine 1 and turbine 3, there is a narrowing of the cross-sectional area of the free flow in the inlet direction towards the gap between the turbines, thereby increasing the fluid flow velocity that hits the convex advancing side of turbine 2 and turbine 3 , and reducing the pressure in that area. While the low velocity that hits the concave advancing side increases the pressure in that area, so that the two turbines have a higher CM value than turbines 1 and 4 .

[picture]

Fig. 8. (a) Velocity Contour, (b) Pressure Contour of Turbine Configuration S/D=8.65.

Fig. 8. (a) Velocity Contour, (b) Pressure Contour of Turbine Configuration S / D = 8.65 .

Figure 9 shows the results of velocity and pressure simulations at a TSR of 0.6 with a 30∘ angle and a S/D distance of 16.86. Based on the velocity contours, the pattern in turbine 1 resembles a single turbine because it is no longer influenced by the turbine behind it, resulting in a Coefficient of Moment (Cm) value similar to that of a single turbine. The flow velocity on the convex advancing blade surface of turbine 1 exceeds 3.5 m/s, resulting in low pressure ( -6000 to 0), while on the concave advancing blade the velocity is around 0.75 m/s with higher pressure ( +1000 to > +2000). A similar pattern also occurs in turbines 2, 3, and 4 because there is no longer interaction between the turbines, allowing the fluid flow to reach maximum velocity on the convex advancing blade side and low pressure, and high pressure on the concave advancing blade side due to the low velocity.

In turbine 2, the flow velocity on the convex side of the advancing blade exceeds 3.5 m/s at pressures of -6000 to +1000, while on the concave side it reaches 1.25 m/s at pressures of +1000 to +2000 . The convex side of the returning blade also exhibits a pressure variation ( -2000 to +2000 ). This pressure difference produces positive torque. Turbine 3 exhibits a similar trend, with a velocity of >3.5 m/s on the convex advancing blade and very low pressure ( -8000 to -1000 ), while the concave advancing blade has a pressure of +1000 to +2000 . The convex returning blade also has a higher pressure than its concave side. The difference in pressure on the advancing and returning blades causes the turbine to still produce positive torque.

Similarly, in turbine 4, which is no longer affected by the wake effect, the velocity on the convex advancing blade exceeds 3.5 m/s at a pressure of -8000 to 0 , and on the concave side reaches 1.25 m/s at a pressure of 0 to +2000 . The convex returning side experiences a pressure of -2000 to +2000 . All turbines exhibit a significant pressure difference between the advancing and returning blades, resulting in positive torque and clockwise rotation.

[picture]

Fig. 9. (a) Velocity Contour, (b) Pressure Contour of Turbine Configuration S/D=16.86.

Fig. 9. (a) Velocity Contour, (b) Pressure Contour of Turbine Configuration S/D=16.86.

Conclusion

The original version of this paper is available on https://www.scientific.net/EI.19.65.pdf

Research on varying the distance between turbines with 2 variations (S/D) of 2.10 and 16.86 on four hydrokinetic Savonius turbines arranged in a staggered configuration that rotate co-rotating on the power plant water channel, obtained the following conclusions:

The value of the Coefficient of Moment (Cm) and Coefficient of Pressure (CoP) on the four Savonius turbines in a staggered configuration with distance of S/D=16.86 tends to resemble a single turbine, and each turbine no longer influences the others.

The value of the Coefficient of Static Torque (CTs) at distance of S/D = 16.86 means that the entire turbine is capable of self-starting at any angle.

Credit Authorship Contribution Statement

The original version of this paper is available on https://www.scientific.net/EI.19.65.pdf

Triyogi Yuwono: Conceptualisation, Methodology, Writing - original draft, review & editing, Supervision, Project administration, Funding acquisition.
Mohammad Riko Putra Utama: Software, Numerical Simulation, Data Curation, Writing-review.

Declaration of Competing Interests

The original version of this paper is available on https://www.scientific.net/EI.19.65.pdf

The authors declare that they have no known competing financial interests or personal relationships that could influence the work reported in this paper.

Acknowledgement

The original version of this paper is available on https://www.scientific.net/EI.19.65.pdf

The authors acknowledge that the research constitutes a form of collaboration between the Perusahaan Listrik Negara (PLN) and the Sepuluh Nopember Institute of Technology (ITS).

Nomenclature

The original version of this paper is available on https://www.scientific.net/EI.19.65.pdf

References

The original version of this paper is available on https://www.scientific.net/EI.19.65.pdf

M. N. I. Khan, M. Tariq Iqbal, M. Hinchey, and V. Masek, "Performance of savonius rotor as a water current turbine," J. Ocean Technol., vol. 4, no. 2, pp. 71-83, 2009.

J. Khan, B. C. Hydro, J. Quaicoe, M. J. Khan, M. T. Iqbal, and J. E. Quaicoe, "River Current Turbine Technology: Progress, Prospects and Challenges," no. December 2020, [Online]. Available: .

A. Heydari, A. Mohammadi, A. Nabhani, and A. Mohammadi, "CFD Simulation of Savonius Cross-flow Hydrokinetic Turbine Using Various URANS Turbulence Models," World Congr. Civil, Struct. Environ. Eng., no. Csee, pp. 1-10, 2024, doi: 10.11159/iceptp24.141.

J. V. Akwa, H. A. Vielmo, and A. P. Petry, "A review on the performance of Savonius wind turbines," Renew. Sustain. Energy Rev., vol. 16, no. 5, pp. 3054-3064, 2012, doi: 10.1016/j.rser.2012.02.056.

Y. Chen, D. Zhang, P. Guo, Q. Hu, and J. Li, "A comparative analysis of 2-D and 3-D simulation for Savonius hydrokinetic turbine array," Ocean Eng., vol. 295, no. November 2023, 2024, doi: 10.1016/j.oceaneng.2024.116909.

G. Kailash, T. I. Eldho, and S. V. Prabhu, "Performance study of modified Savonius water turbine with two deflector plates," Int. J. Rotating Mach., vol. 2012, no. June 2012, doi: 10.1155/2012/679247.

A. K. Nag and S. Sarkar, "Performance analysis of Helical Savonius Hydrokinetic turbines arranged in array," Ocean Eng., vol. 241, no. May 2021, doi: 10.1016/j.oceaneng.2021.110020.

Y. E. Priandika, Y. Triyogi, and R. Hermawan, "Numerical Study of Tandem Installation of Twin Savonius Hydrokinetic Turbines on PLTU Paiton Cooling Water Channel," E3S Web Conf., vol. 517, 2024, doi: 10.1051/e3sconf/202451710001.

O. Wulaningtyas and Y. Triyogi, "Numerical Study of The Installation Configuration of Four Savonius Hydrokinetic Turbines in The Cooling Water Channel of PAITON Power," vol. 9, no. 1, pp. 24-31, 2025.

R. Ebrahimpour, Mohammad Shafaghat, Rouzbeh Alamian, "Numerical Investigation of the Savonius Vertical Axis Wind Turbine and Evaluation of the Effect of the Overlap Parameter in Both Horizontal and Vertical Directions on Its Performance," Symmetry (Basel), vol. 11, no. 6, p. 821, 2019, doi: 10.3390/sym11060821.

R. Shaheed, A. Mohammadian, and H. Kheirkhah Gildeh, "A comparison of standard k-ɛ and realisable k- turbulence models in curved and confluent channels," Environ. Fluid Mech., vol. 19, no. 2, pp. 543-568, 2019, doi: 10.1007/s10652-018-9637-1.

T. I. E. and S. V. P. Vimal Patel, Ganapathi Bhat, "Influence of overlap ratio and aspect ratio on the performance of Savonius hydrokinetic turbine," Int. J. ENERGY Res., 2016, doi: 10.1002/er. 3670 .

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Engineering Innovations, ISSN: 2813-1002, Vol. 19, pp 75-82

doi: 10.4028/p-X6eMh6

© 2026 The Author(s). Published by Trans Tech Publications Ltd, Switzerland.

Submitted:
2025-09-01

Revised:
2025-09-28

Accepted:
2026-02-14

Online:
2026-06-11

Numerical Study of Four Hydrokinetic Savonius Turbines in an In-Line Square Co-Rotating Configuration in the Cooling Water Channel

The original version of this paper is available on https://www.scientific.net/EI.19.75.pdf

Daniel Ardian Setiyanto 1,2,a*, Triyogi Yuwono 1, b*

¹Department of Mechanical Engineering, Sepuluh Nopember Institute of Technology, Surabaya 60111, Indonesia

2 PT PLN (Persero), Jakarta 12160, Indonesia

a  daniel.ardian.setiyanto@gmail.com, b  triyogi@me.its.ac.id

Keywords: savonius turbine, in-line square, clockwise rotation, numerical method.

Abstract

The 2019-2023 energy mix is still heavily dominated by coal and natural gas. Indonesia is committed to achieving Net-Zero Emissions (NZE) by 2060. One of the renewable energy sources that can be utilized is hydropower, also known as water energy. The application of water energy utilization as an energy source involves harnessing the flow in the PLTU cooling water channel. In this study conducted by numerical simulation method, the Savonius turbine is arranged in-line square co-rotating configuration in clockwise rotation with variation of distance between turbines (S/D) of 2.1 and 23.79 with S is the distance between two turbines both horizontally and vertically and D is the diameter of the Savonius turbine. Patel et al's research was used for the validation process, and an average error of 2.78% was obtained. In the S/D 2.10 variation, the four turbines function as a single unit, resulting in a flow blockage effect. In the furthest S/D variation of 23.79, the performance of the front turbines improves, but the rear turbines remain relatively the same because they are too close to the canal wall, resulting in a flow blockage effect as well.

Introduction

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Indonesia is among the signatories of the "Paris Agreement" - an international accord aimed at capping the rise in global average temperature at 1.5∘C[1] - and has committed to achieving Net Zero Emissions (NZE) by 2060. One of the emerging renewable energy sources that can be utilized is hydropower, also known as water energy. The utilization of water energy as an energy source has numerous applications. One such application is the use of the flow in the cooling water canal of the steam power plant.

Hydrokinetic turbines convert the kinetic energy of flowing water into mechanical energy through turbine shaft rotation. One such application is the Savonius turbine. The turbine was first developed by Sigurd J. Savonius in 1925. Savonius turbines are typically composed of two or three blades. The operational mechanism of the Savonius turbine is predicated on the principle of the variation in friction (drag) experienced by the convex and concave turbine blades. It has been established that the occurrence of friction on the convex turbine blades will result in the forward movement of the blades. Conversely, the presence of friction on the concave blades will result in the backwards movement of the blades. This phenomenon is the underlying cause of the Savonius turbine's rotation.

Savonius turbines boast a comparatively uncomplicated design when juxtaposed with other hydrokinetic turbines. Furthermore, these turbines are capable of receiving wind from all directions and possess the capacity for autonomous initiation [2]. Savonius turbines are equipped with two distinct types of blades: advancing blades and returning blades. Advancing blades are concave in shape, while returning blades are convex. The objective of both blades is to generate positive torque, negative torque and distinct drag forces on each turbine blade, with the disparity in drag forces yielding torque [6].

Golecha et al. [3] conducted a study on the interaction effect between two Savonius hydrokinetic turbines with variations in distance between the turbines. Zhang et al. [7] also studied the interaction between two Savonius hydrokinetic turbines in an in-line configuration, varying the
distance between the turbines and the fluid velocity range. Priandika et al. [5] also conducted related research to find the optimal distance between two Savonius turbines with an inline configuration. The aim was to determine an optimal distance at which the two turbines would not affect each other.

As demonstrated by the preceding studies, no research has been conducted on four Savonius turbines with an in-line square co-rotating configuration in the cooling water channel. The utilization of turbines with this configuration is intended to replace the use of energy contained in the cooling water flow of the cooling channel. It is anticipated that these studies will furnish a comprehensive overview of the performance of four Savonius turbines arranged in tandem with an in-line square co- rotating configuration on the cooling water channel.

Based on the background mentioned above and previous studies, the Savonius turbine is a topic worthy of further research. In this study, the unique and distinguishing factor is that Savonius turbines are installed in cooling water channels, which generate clean energy from the energy contained in the cooling water flow.

Numerical Simulations

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The present study will utilize numerical simulation with ANSYS Fluent 2023 R2 software to assess the performance of the Savonius hydrokinetic turbine.

Research Scheme.

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The objective of this study is to determine the optimal distance between the front and rear turbines to achieve maximum turbine configuration performance. The objective of this study is to obtain the value of the coefficient of moment (Cm) and the coefficient of power (CoP) from each variation of S/D. It is imperative to be cognizant of the repercussions that variations in the distance between turbines (S/D) engender. It is essential to understand the flow characteristics qualitatively through the control of velocity and pressure.

The configuration under scrutiny in this study consists of a set of four Savonius hydrokinetic turbines arranged in tandem. It is observed that all four turbines rotate in the same direction (clockwise). They are placed on the cooling water channel, as illustrated in Fig. 1.

[picture]

Fig. 1. The Savonius turbine configuration is arranged in tandem and rotates in the same direction.

Fig. 1. The Savonius turbine configuration is arranged in tandem and rotates in the same direction.

The variations in this study are in the distance between turbines, both horizontally and vertically (S/D), which from 2.1 and 23.79. The dimensions of the cooling water channel are described as 15,000 mm×10,000 mm, with a water velocity of 2 m/s at a temperature of 35∘C.

Pre-Processing.

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The numerical simulation process commences with the generation of geometry, meshing, and boundary conditions.

Geometry Creation.

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As illustrated in Fig. 1, the geometry is derived from the dimensions of the Savonius turbine, the dimensions of the cooling water channel, and the distance between the turbines, both horizontally and vertically. The design of the Savonius turbine under consideration are illustrated in Fig. 2. Dimensions of Savonius turbine are D=578 mm, b=40 mm,t=8 mm, L=1100 mm, and H= 1100 mm

[picture]

Fig. 2. The Savonius turbine configuration is arranged in tandem and rotates in the same direction.

Fig. 2. The Savonius turbine configuration is arranged in tandem and rotates in the same direction.

Determining the Domain, Meshing and Boundary Conditions.

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In this study, two domains are distinguished: the fixed domain and the rotating domain. During the simulation process, the fixed domain is characterized by its position, which remains constant (fixed position, i.e. in the cooling water channel), while the rotating domain is defined by its positional shift (rotation, i.e. the Savonius turbine). The research scheme is illustrated in Fig. 3.

[picture]

Fig. 3. Scheme of the Research Domain.

Fig. 3. Scheme of the Research Domain.

Meshing represents the subsequent stage in the process. Upon completion of the geometry creation process, the type of mesh employed in the fixed domain is structured, while in the rotating domain, an unstructured mesh is utilized. In the process of generating a structured mesh, the research domain is augmented with multiple auxiliary lines. These lines are utilized to divide the domain into smaller components through the process of cutting. As illustrated in Fig. 4, and Fig. 5, the domains are presented in conjunction with meshing on two adjacent Savonius turbines, and the meshing results at the tip of the Savonius turbine blade.

[picture]

Fig. 4. Meshing between the Savonius Turbine.

Fig. 4. Meshing between the Savonius Turbine.

[picture]

Fig. 5. Meshing at the Tip of the Savonius Turbine Blade.

Fig. 5. Meshing at the Tip of the Savonius Turbine Blade.

The definition of the research domain is provided to elucidate the nature of each constituent element within it. This stage is conducted on the boundary condition. As with the inlet section of the water flow in the canal/inlet-type velocity inlet, the canal wall and turbine blade are defined as walls. The outlet section is defined as a pressure outlet and as a boundary between the fixed domain and the rotating domain, and is called the interface, as depicted in Fig. 3.

Processing.

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In the configuration stage, it is necessary to establish the solver, which can be either pressurebased or density-based. Furthermore, the velocity absolute or relative formula must be specified, as well as whether the situation is steady or transient. Finally, the geometry may be a 2D planar, axisymmetric, or axisymmetric swirl. Subsequently, the solver must be determined, and the model, material, cell zone conditions, and boundary conditions must be specified. The velocity ( u ) must be set according to the research limits. The present study employs a realizable k-epsilon turbulent model with an enhanced wall treatment. The material is set to water at a density of 994.08 kg/m3 and a viscosity of 0.00072 kg/ms. The gradient method is then employed, utilizing least squares cells, pressure, momentum, and turbulence kinetic energy, with a second-order upwind scheme and an absolute residual criterion of 105. The number of time steps is 3600 , and the number of iterations is 20 .

Post-Processing.

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The post-processing stage represents the final phase of the simulation process. At this point, the simulation results are obtained, and a subsequent discussion is warranted. In this study, the number of time steps utilized is 3,600 , which corresponds to ten rotations of the Savonius turbine. The data set under consideration is derived from the final rotation, wherein the outcomes exhibited notable stability.

[picture]

Fig. 6. (a). Grid Independency Test; (b). The comparison of the coefficient of power values with those found in [4] research.

Fig. 6. (a). Grid Independency Test; (b). The comparison of the coefficient of power values with those found in [4] research.

Grid Independence Test and Validation.

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The grid independence test is carried out by comparing five mesh amounts, namely 35631, 133227, 142527, 162027, and 203027, with the average Cm results obtained. Based on Fig. 6, It can be seen that at a total mesh of 133227, the Cm value begins to stabilize, so the number of meshes 133227 is then used to shorten the simulation process duration. The present study employs a validation process that entails comparing its current research with that of previous studies in this field. In this instance, the research undertaken by [4] serves as a point of reference. The validation process involves comparing the Cm value to the TSR at a Reynolds number of 157,000. Fig. 6 presents a graph comparing the Cm values. The finding that the error value is less than 5% indicates that the validity of the simulation results has been established.

Result and Discussion

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As illustrated in Fig. 8, turbine 2 is not effective at an S/D ratio of 2.1 due to the flow blockade effect. Turbines 3 and 4 are still influenced by the wake from turbines 1 and 2 at an S/D ratio of 2.1. At an S/D ratio of 23.79, turbine 1 experiences an increase compared to an S/D ratio of 2.1 due to the flow blockade effect. However, turbine 2 experiences a slight decrease at an S/D ratio of 23.79 due to the flow blockade effect. Turbines 3 and 4 at an S/D ratio of 23.79 are still affected by the wakes of turbines 1 and 2 .

[picture]

Fig. 7. Evolution of Coefficient of Moment (Cm) as a function of TSR in varied relative distance (S/D) for: (a) turbine-1; (b) turbine-2; (c) turbine-3; (d) turbine-4.

Fig. 7. Evolution of Coefficient of Moment (Cm) as a function of TSR in varied relative distance (S/D) for: (a) turbine-1; (b) turbine-2; (c) turbine-3; (d) turbine-4.

As illustrated in Fig. 9, In Turbine 1, it can be observed that the CoP value at an S/D variation of 2.1 is lower than that of Turbine 1 at other S/D variations due to a flow blockage effect occurring at S/D 2.1, which hinders the performance of Turbine 1. In contrast, Turbine 2 experienced a performance drop at S/D 23.79 due to a flow blockage effect at that variation, which impairs the performance of Turbine 2. Turbines 3 and 4 generally exhibit low performance because they are still influenced by the wake from Turbines 1 and 2.

[picture]

Fig. 8. Evolution of Power Coefficient as a function of TSR in varied relative distance (S/D) for: (a) turbine-1; (b) turbine-2; (c) turbine-3; (d) turbine-4.

Fig. 8. Evolution of Power Coefficient as a function of TSR in varied relative distance (S/D) for: (a) turbine-1; (b) turbine-2; (c) turbine-3; (d) turbine-4.

As demonstrated in Fig. 9, This represents the velocity contour of the S/D variations 2.1 and 23.79. In the S/D 2.1 variation, the flow velocity acting on turbine 2 is much higher compared to turbine 1 in the same variation. This occurs due to the flow blockage effect, which causes the flow to move toward the outer side of the turbine configuration. The flow velocity on turbines 3 and 4 tends to be lower because of the wake effect from turbines 1 and 2. In the S/D 23.79 variation, the velocity striking the advancing blade of turbine 1 is greater than that of turbine 2's advancing blade. This is also due to the flow blockage effect, whereas turbines 3 and 4 can still be seen to be under the influence of the wake, causing the flow striking them to remain relatively low.

[picture]

Fig. 9. Comparison of the velocity path line structure at TSR 0,8 on variation of S/D: (a) 2.1 (b) 23,79.

Fig. 9. Comparison of the velocity path line structure at TSR 0,8 on variation of S/D: (a) 2.1 (b) 23,79.

Fig. 10 this represents the pressure contour for the S/D variations 2.1 and 23.79. In the S/D 2.1 variation, turbine 2 has a greater delta pressure compared to turbine 1 , indicating that turbine 2 has better performance, while turbines 3 and 4 show similar (relatively low) contours. The delta pressure on the returning blade is greater than that on the advancing blade, which causes the rotational direction to reverse toward the returning blade, or in real conditions, the turbine would no longer rotate. Meanwhile, in the S/D 23.79 variation, turbine 1 has a greater delta pressure than turbine 2, indicating better performance. Turbines 3 and 4 show relatively low contours because they are still influenced by the wake.

[picture]

Fig. 10. Comparison of the pressure contours at TSR 0,8 on variation of S/D: (a) 2.1 (b) 23.79.

Fig. 10. Comparison of the pressure contours at TSR 0,8 on variation of S/D: (a) 2.1 (b) 23.79.

Conclusion

The original version of this paper is available on https://www.scientific.net/EI.19.75.pdf

In cases where the turbines are relatively close together, the four turbines operate as a single turbine. This results in a flow blockage effect. The rear turbines function by utilizing the residual flow from the front turbines, where the residual flow velocity is weaker than the flow toward the front turbines. However, at the farthest variation of S/D 23.79, the performance of each turbine has not reached its maximum due to the turbines being located very close to the canal wall. This causes the flow blockage effect to occur again because the flow of water near the wall is obstructed. This study concludes that geographical separation between turbines does not always guarantee better performance for all four turbines. To ensure optimal operation of the front and rear turbines without mutual interference, it is important to consider the distance between the turbines and the canal wall.

Limitation

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This research has the following limitations: four turbines are arranged clockwise, and the fluid velocity in the cooling water channel is constant at 2 m/s. This study used numerical methods instead of experimental methods.

Credit Authorship Contribution Statement

The original version of this paper is available on https://www.scientific.net/EI.19.75.pdf

Triyogi Yuwono: Conceptualisation, Methodology, Writing - original draft, review & editing, Supervision, Project administration, Funding acquisition.
Daniel Ardian Setiyanto: Software, Numerical Simulation, Data Curation, Writing-review.

Declaration of Competing Interests

The original version of this paper is available on https://www.scientific.net/EI.19.75.pdf

The authors declare that they have no known competing financial interests or personal relationships that could influence the work reported in this paper.

Acknowledgement

The original version of this paper is available on https://www.scientific.net/EI.19.75.pdf

The authors acknowledge that the research constitutes a form of collaboration between the Perusahaan Listrik Negara (PLN) and the Sepuluh Nopember Institute of Technology (ITS).

Nomenclature

The original version of this paper is available on https://www.scientific.net/EI.19.75.pdf

References

The original version of this paper is available on https://www.scientific.net/EI.19.75.pdf

Di Leva, Chales E., Shi Xiaoxin, 2017. The Paris Agreement and the International Trade Regime: Considerations for Harmonization, Sustainable Development Law & Policy: Vol. 17 : Iss. 1, Article 4. http://digitalcommons.wcl.american.edu/sdlp/vol17/iss1/4.

Gandhini, K.S., Yuwono, T.Y., Studi Eksperimen Kinerja Turbin Angin Savonius Yang Terintegrasi dengan Gedung dengan Rasio G/D = 1,4558. JURNAL TEKNIK ITS Vol. 10, No. 2, (2021) ISSN: 2337-3539.

Golecha, K., Eldho, T.I., Prabhu, S.V., 2012. Study on the Interaction between Two Hydrokinetic Savonius Turbines. International Journal of Rotating Machinery 2012, 1-10. .

Patel, V., Bhat, G., Eldho, T.I., Prabhu, S.V., 2017. Influence of overlap ratio and aspect ratio on the performance of Savonius hydrokinetic turbine: Performance of Savonius hydrokinetic turbine. Int. J. Energy Res. 41, 829-844. .

Priandika, Y.E., Yuwono, T.Y., Hermawan, R., 2024. Numerical Study of Tandem Installation of Twin Savonius Hydrokinetic Turbines on PLTU Paiton Cooling Water Channel. E3S Web of Conferences 517, 10001 (2024). .

Shuhufam, A., Yuwono, T.Y., 2020. Studi Eksperimen Peningkatan Kinerja Turbin Angin Savonius dengan Penempatan Silinder Sirkular di Depan Returning Blade Turbin pada Jarak S/D = 2,6. JURNAL TEKNIK ITS Vol. 9, No. 2, (2020) ISSN: 2337-3539.

Zhang, Y., Kang, C., Zhao, H., Teng, S., 2019. Effects of in-line configuration of drag-type hydrokinetic rotors on inter-rotor flow pattern and rotor performance. Energy Conversion and Management 196, 44-55. .

This ePub file was created using machine-generated methods and automated software to convert the source text into an ePub format and may contain formatting inconsistencies from the original version. Formatting may vary depending on the device used.

The original version of this eBook is available on https://www.scientific.net/book/engineering-innovations-vol-19/978-3-0364-1217-7

Engineering Innovations, ISSN: 2813-1002, Vol. 19, pp 83-100

doi: 10.4028/p-ff5D0j

© 2026 The Author(s). Published by Trans Tech Publications Ltd, Switzerland.

Submitted:
2026-02-18

Revised:
2026-03-10

Accepted:
2026-03-11

Online:
2026-06-11

Numerical Analysis of Defect-Induced Stress and Deformation in a Progressive Cavity Pump Stator Elastomer

The original version of this paper is available on https://www.scientific.net/EI.19.83.pdf

Dwi Meilianto 1,a and Rachmat Sriwijaya 1, b*

1 Department of Mechanical and Industrial Engineering, Universitas Gadjah Mada, Indonesia

a  dwimeilianto@mail.ugm.ac.id, b  sriwijaya@gadjahmada.edu

*Corresponding Author: sriwijaya@gadjahmada.edu

Keywords: Progressive Cavity Pump; Finite Element Analysis; Hyperelastic Modeling; Ogden Model; Rotor-Stator Interference; Defect-Induced Stress Concentration.

Abstract

Progressive Cavity Pumps (PCPs) depend on elastomeric stators to achieve effective sealing and mechanical durability. However, manufacturing imperfections and operational wear can introduce surface discontinuities that modify rotor-stator contact conditions and generate stress concentrations. This study presents a three-dimensional finite element analysis to investigate defectinduced stress and deformation in a PCP stator elastomer. The stator is modeled as nitrile butadiene rubber (NBR) using a third-order Ogden hyperelastic formulation derived from experimental tensile testing, while frictional contact between the rotor and stator is incorporated to represent realistic operating conditions. Circumferential rectangular grooves of varying depths and multiple rotor-stator interference levels are systematically examined. The results demonstrate that increasing rotor-stator interference significantly elevates the maximum von Mises stress, maximum principal strain, strain energy density, and overall stator deformation. In contrast, increasing defect depth modifies the stress distribution and slightly increases structural compliance, leading to a reduction in peak notch-tip stress due to stress redistribution within the elastomer. Pronounced stress concentration is observed at the defect edges, particularly at the notch tip, which may represent potential crack initiation regions under cyclic loading. These findings clarify the combined influence of interference and geometric discontinuities on stator mechanical response and provide insights for improving defect-tolerant design and reliability of PCP stator elastomers.

Introduction

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Single-lobe Progressive Cavity Pumps (PCPs) are widely employed in industries such as oil and gas, wastewater treatment, pulp and paper, and food processing due to their ability to transport highly viscous, multiphase fluids with stable, continuous flow. In artificial lift applications, PCPs are recognized as one of the most effective lifting methods because of their operational reliability and tolerance to solids [1]. In these systems, the elastomeric stator plays a critical role in maintaining sealing performance, volumetric efficiency, and mechanical durability. The rotor-stator interference generates the contact pressure required for sealing; however, excessive mechanical loading may accelerate material degradation and reduce service life.
Extensive research has been conducted to analyze PCP performance from both fluid dynamic and structural perspectives. Several studies have focused on three-dimensional flow modeling and slip prediction using CFD approaches to evaluate flow rate and volumetric efficiency under varying operating conditions [2,3]. Other investigations employed finite element analysis (FEA) to examine rotor-stator interference, contact stress, and laden torque, identifying interference magnitude, eccentricity, and stator thickness as dominant parameters influencing mechanical response [4-7].
Fluid-structure interaction (FSI) models have been developed to capture the combined effects of fluid pressure and stator deformation, demonstrating that interference and pressure substantially influence pump performance [8-10]. Recent research has incorporated thermo-mechanical coupling and clearance optimization, emphasizing the roles of contact stress, thermal expansion, and fluid pressure in stator deformation behavior [11-12]. Additionally, analytical modeling approaches have been introduced to predict pump performance based on geometric and operational parameters [13-14].

While previous studies offer valuable insights into pump performance, contact mechanics, and structural optimization, they primarily focus on ideal stator geometries. In real-world applications, however, manufacturing imperfections, material inhomogeneity, and operational wear can introduce localized surface defects along the stator inner surface, potentially altering rotor-stator contact conditions and mechanical response. These geometric discontinuities may substantially alter rotorstator contact conditions, redistribute stresses, and generate localized stress concentrations that accelerate crack initiation and material degradation.
Under cyclic rotor motion, the stator elastomer experiences repeated loading-unloading cycles, making it susceptible to fatigue damage, particularly in regions where stress concentrations are present. Localized stress amplification at defect tips may increase strain energy density, promoting early crack initiation and propagation. Fatigue behavior in elastomers is strongly influenced by stress concentration, crack growth resistance, and cyclic strain amplitude, as extensively discussed in classical rubber fatigue studies [15-16]. Therefore, even small geometric discontinuities may significantly reduce service life by accelerating crack growth under repeated mechanical loading. Although fatigue life is not explicitly modeled in this study, stress concentration is evaluated as a primary indicator of potential crack initiation.
This study conducts a three-dimensional finite element analysis to evaluate the effects of surface defect depth and rotor-stator interference on deformation, von Mises stress distribution, maximum principal strain, and strain energy density in a PCP stator elastomer. The stator material is represented as nitrile butadiene rubber (NBR) using a third-order Ogden hyperelastic formulation derived from experimental tensile data. This model is chosen for its capacity to accurately capture the nonlinear behavior of hyperelastic materials under large deformations and to achieve improved fitting accuracy across a wide strain range [17-19].
Currently, no published research systematically investigates the combined effects of rotor-stator interference and surface defect depth on stress concentration in PCP stators using a three-dimensional hyperelastic finite element analysis (FEA) model.
This study considers frictional contact between the rotor and stator, with defects idealized as rectangular grooves to represent localized geometric discontinuities along the stator inner surface. By systematically varying interference and defect depth, the research seeks to elucidate defect-induced stress concentration mechanisms and to inform defect-tolerant design and reliability improvement in PCP systems.

Methodology

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Hyperelastic Coefficient

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The NBR hyperelastic material was prepared according to ASTM D412, as shown in Fig. 1. This test is a standard tensile test for vulcanized rubber and thermoplastic elastomers [20]. The SHIMADZU AGX-S 10 kN machine was used for ASTM D412 testing, as shown in Fig. 2.

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Fig. 1. Dimension of the test sample according to the ASTM D412C standard [20].

Fig. 1. Dimension of the test sample according to the ASTM D412C standard [20].

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Fig. 2. Uniaxial tensile test using the SHIMADZU AGX-S 10 kN machine.

Fig. 2. Uniaxial tensile test using the SHIMADZU AGX-S 10 kN machine.

The stress-strain curve obtained from the tensile test was used to determine the hyperelastic material coefficients. Curve fitting was conducted in ANSYS utilizing the third-order Ogden model.

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Fig. 3. Stress-strain curve fitting of the tensile test using the hyperelastic material model.

Fig. 3. Stress-strain curve fitting of the tensile test using the hyperelastic material model.

Hyperelastic parameters were calibrated using uniaxial tensile data. While multi-axial calibration could enhance model robustness, previous studies demonstrate that third-order Ogden fitting based on tensile data achieves reasonable accuracy for moderate strain ranges [17-19].
Statistical evaluation confirms that the third-order Ogden hyperelastic model closely aligns with experimental uniaxial tensile data, yielding a Pearson correlation coefficient of 0.997 and a coefficient of determination of 0.995 . The normalized RMSE remains below 2%, demonstrating high predictive accuracy, as shown in Fig. 3. The corresponding third-order Ogden coefficients are presented in Table 1.

Table 1. Third-order Ogden coefficients.

Material | Coefficients
-2.7218 | -5.055 | -2.084 | 2.6327 | 0.10342 | 2.6326 |

Furthermore, these coefficients were implemented as material input parameters for the elastomeric stator in ANSYS.

PCP Design

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A conventional single-lobe PCP stator was modeled, as illustrated in Fig. 4. The geometric parameters of the PCP are provided in Table 2. Nitrile Butadiene Rubber (NBR) was chosen as the stator material and represented using a third-order Ogden hyperelastic formulation based on experimental tensile data.

The rotor was modeled as a rigid body in the simulation. The steel properties, including an elastic modulus of 200 GPa and a Poisson's ratio of 0.3 , were defined using the ANSYS material library. Rotor deformation was neglected due to its substantially higher stiffness compared to the elastomeric stator.
The baseline interference was set to 0.45 mm , with additional values of 0,0.15, and 0.30 mm also evaluated. To simplify the model, the outer metal housing was omitted. The stator outer surface was fully constrained to represent perfect bonding and confinement by the surrounding steel housing. The elastic modulus of the housing material ( 200 GPa ) is several orders of magnitude higher than that of the elastomer stator. Therefore, the radial deformation of the housing is negligible compared to the stator deformation.
The rotor rotational speed was set to 100 rpm , corresponding to a rotational period of 0.6 seconds per revolution. Consequently, the stator's mechanical response is governed primarily by nonlinear contact and material behavior rather than dynamic effects.

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Fig. 4. Three-dimensional model of the PCP.

Fig. 4. Three-dimensional model of the PCP.

Table 2. Geometric parameters of the PCP [21].

Geometrical Parameter | Value | Unit
Eccentricity (E) | 4.039 | [mm]
Rotor Diameter (Dr) | 41.148 | [mm]
Stator Diameter (Ds) | 40.248 | [mm]
Interference Default w = (Dr - Ds)/2 | 0.45 | [mm]
Stator Pitch | 119.99 | [mm]
Rotor Pitch | 59.995 | [mm]
Number of Pitches | 3 | [-]

Various rotor diameters were employed to establish the interference condition [21]. Positive interference was achieved by increasing the rotor diameter while keeping the stator geometry unchanged.

Stator Defect

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The stator defect was represented as a rectangular circumferential groove positioned along the inner surface of the stator. This groove extends continuously around the entire inner circumference, as shown in Fig. 5. Three distinct defect depths and four rotor-stator interference values were examined, as summarized in Table 3.
The defect edges were modeled with sharp corners to capture potential stress concentrations. Although the groove geometry is circumferentially symmetric, the resulting stress distribution is nonuniform due to rotor eccentric motion and frictional contact conditions (friction coefficient μ=0.10 ), as illustrated in Fig. 6.

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Fig. 5. Rectangular defect on the elastomer stator.

Fig. 5. Rectangular defect on the elastomer stator.

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Fig. 6. Rectangular defect along the stator.

Fig. 6. Rectangular defect along the stator.

Table 3. Variation of rotor-stator interference and rectangular defect depth.

Interference

Depth of Rectangular Defect

0.00 | superscript reference
0.15 | superscript reference
0.30 | superscript reference
0.45 | superscript reference

Finite Element Modeling

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Finite element analysis was conducted using ANSYS Mechanical (Transient Structural). The model consisted of 428,362 elements and 631,925 nodes. A predominantly tetrahedral mixed mesh was employed with a global element size of 2 mm . Local mesh refinement was applied in and around the stator notch defect region, with a final refinement size of 0.251 mm , as illustrated in Fig. 7.

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Fig. 7. Finite element mesh showing local refinement around the stator notch defect.

Fig. 7. Finite element mesh showing local refinement around the stator notch defect.

The mesh quality assessment yielded an average orthogonality of 0.787 (very good) and a skewness of 0.212 (excellent), indicating good mesh quality for nonlinear contact analysis. A mesh sensitivity study was conducted to ensure convergence of the peak von Mises stress values near the defect region. The mesh sensitivity study is shown in Fig. 8.
The boundary conditions included a fixed support applied to the stator's outer surface to represent perfect bonding with the housing. Consequently, constraining the outer stator surface provides a reasonable first-order approximation for the stator-housing interaction and has been widely adopted in previous numerical studies of PCP stators where the housing stiffness is significantly higher than the elastomer stiffness.
A prescribed displacement was imposed to simulate rotor eccentric motion, and rotational motion was applied to the rigid rotor. Large deformation effects were enabled to account for geometric nonlinearity. The prescribed displacement is expressed in Eqs. (1)-(3), as follows [12]:

ux=ecos(ωt)−euy=esin(ωt)−e

(1)(2)

where ω is the angular velocity of the rotor and t is time. The rotational angle of the rotor is:

N=−ωt(3)

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(c)

Figure 8. (c)

Fig. 8. Mesh sensitivity study at the middle stator section with increasing number of elements: (a) the variation of principal strain, (b) the variation total deformation, and (c) the variation of maximum von Mises stress.

Fig. 8 indicates that the maximum von Mises stress, principal strain, and total deformation initially increase significantly at lower element counts (below approximately 60,000 elements), indicating that
the mesh is too coarse and the numerical solution has not yet stabilized. As the mesh density increases, the variation in these response quantities gradually decreases. When the number of elements reaches approximately 300,000 , the curves begin to exhibit a plateau behavior. Further refinement beyond 400,000 elements yields only marginal changes in the predicted values (approximately ±5% ), indicating that the solution becomes progressively insensitive to additional mesh refinement.
This behavior indicates that the numerical solution approaches an asymptotic value and can therefore be considered converged from an engineering standpoint. Consequently, a mesh containing approximately 400,000 elements or more is sufficient to ensure result accuracy while maintaining reasonable computational efficiency.

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Fig. 9. ANSYS model of the PCP stator.

Fig. 9. ANSYS model of the PCP stator.

Fig. 9 presents the three-dimensional finite element model of the PCP stator developed in ANSYS. The rectangular defect was modeled along the helical inner surface of the stator, following its geometric profile.
A cross-section was taken at the mid-length position (section E-E), corresponding to the location of maximum rotor-stator contact pressure observed during simulation. Peak stress and deformation responses were evaluated at this section. The peak stress values were recorded over a complete rotational cycle to ensure the maximum response was captured.
Fig. 10 illustrates the cross-sectional views of the stator within a single stator pitch, highlighting the variation in contact conditions along the helical geometry.

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Fig. 10. Cross-sectional views of the stator along one stator pitch.

Fig. 10. Cross-sectional views of the stator along one stator pitch.

The rectangular groove was intentionally modeled with sharp corners to represent a conservative defect geometry that could result in elevated stress concentrations. According to classical elasticity theory, sharp geometric discontinuities can produce theoretical stress singularities. In contrast, hyperelastic materials such as NBR exhibit nonlinear stress-strain responses and finite deformation behavior, which tend to limit unrealistic stress amplification. The mesh sensitivity study shown in Fig. 8 indicates that the predicted peak stress approaches a stable asymptotic value as mesh density increases, suggesting that the numerical solution is sufficiently converged for engineering applications.

Influence of Interference and Defect Depth on Deformation and von Mises Stress

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Although increasing the defect depth increases overall deformation, the peak von Mises stress at the notch tip decreases. This behavior can be attributed to local compliance effects associated with the deeper groove geometry. As the defect depth increases, the surrounding elastomer becomes locally more compliant, allowing greater deformation and redistributing the contact pressure along the groove surfaces. Consequently, the stress concentration at the notch tip is alleviated due to load redistribution along the groove surfaces.
In hyperelastic materials such as NBR, nonlinear stress-strain behavior further contributes to this phenomenon, as increased strain does not necessarily correspond to proportional stress amplification. Therefore, deeper defects promote stress redistribution rather than intensified tip concentration.
As defect depth increases, the elastomer deforms differently, affecting strain-related indicators. Areas near the notch tip exhibit more localized deformation, leading to higher principal strain and strain energy density during rotor-stator contact. Although the peak von Mises stress drops as the defect gets deeper, the shape of the defect still changes the local strain field in the stator. These results show that defect geometry affects both the magnitude of stress and the development of deformation-related damage in hyperelastic stator materials.

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Fig. 11. Maximum von Mises stress distribution at representative stator cross-sections (A-A to EE) for 0.45 mm interference and a 3 mm rectangular defect.

Fig. 11. Maximum von Mises stress distribution at representative stator cross-sections (A-A to EE) for 0.45 mm interference and a 3 mm rectangular defect.

Fig. 11 presents the axial variation of maximum von Mises stress across five representative crosssections (A-A to E-E), as illustrated in Fig. 10. In this case, the stator is subjected to 0.45 mm interference and a 3 mm rectangular defect. The stress magnitude and localization vary along the stator length due to the helical rotor-stator engagement and eccentric motion of the rotor.
The maximum von Mises stress values in sections A−A,B−B,C−C,D−D, and E−E are 0.244 MPa , 0.257MPa,0.322MPa,0.314MPa, and 0.343 MPa , respectively. Sections located near peak contact
zones exhibit higher stress intensities, with pronounced localization around the defect edges. The asymmetric stress distribution indicates that the defect interacts with the contact region differently along the axial direction, demonstrating that the maximum stress does not occur uniformly but depends on the local contact condition within the helical geometry.

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Fig. 12. Maximum von Mises stress distribution at the middle stator cross-section with a rectangular defect depth of 3 mm for different interference values: (a) 0.15 mm , (b) 0.30 mm , and (c) 0.45 mm .

Fig. 12. Maximum von Mises stress distribution at the middle stator cross-section with a rectangular defect depth of 3 mm for different interference values: (a) 0.15 mm , (b) 0.30 mm , and (c) 0.45 mm .

Fig. 12, which presents a different observation plane than Fig. 10, depicts the maximum von Mises stress distribution at the mid-stator cross-section containing a 3 mm rectangular defect under varying interference levels. As the interference increases from 0.15 mm to 0.45 mm , the maximum von Mises stress rises markedly from 0.102 MPa to 0.343 MPa . This substantial increase indicates that rotorstator interference significantly amplifies the contact pressure and compressive preload at the interface.
The stress field extends across both the upper and lower contact regions as a result of the combined effects of rotor eccentricity and interference-induced contact interaction. The consistently higher stress in the lower contact region indicates a non-uniform load transfer mechanism. This asymmetry suggests that the rectangular defect locally alters the stiffness distribution of the elastomer, leading to intensified stress concentration near the defect region. Therefore, the interaction between geometric discontinuity and increased preload governs the localized stress amplification in the lower region.

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Fig. 13. Maximum von Mises stress distribution along the stator length for an interference of 0.45 mm with varying rectangular defect depth (a) 1 mm , (b) 2 mm , (c) 3 mm .

Fig. 13. Maximum von Mises stress distribution along the stator length for an interference of 0.45 mm with varying rectangular defect depth (a) 1 mm , (b) 2 mm , (c) 3 mm .

Fig. 13 indicates that the maximum von Mises stress along the stator length decreases as the defect depth increases. The stress is concentrated along the defect path. The peak stress at the notch tip decreases due to stress redistribution associated with increased local compliance.
For a given interference level, increasing the defect depth reduces the local von Mises stress peak at the notch tip. For example, at an interference of 0.45 mm , Fig. 13 shows that the maximum stress decreases from 0.477 MPa at a 1 mm defect depth to 0.427 MPa at 2 mm and 0.362 MPa at 3 mm . This reduction occurs because the elastomer becomes more flexible in certain areas, helping spread the stress and reducing stress concentration at the notch tip.

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Fig. 14. Maximum von Mises stress distribution at the tip of the rectangular defect for an interference of 0.45 mm with varying defect depths: (a) 1 mm , (b) 2 mm , and (c) 3 mm .

Fig. 14. Maximum von Mises stress distribution at the tip of the rectangular defect for an interference of 0.45 mm with varying defect depths: (a) 1 mm , (b) 2 mm , and (c) 3 mm .

Fig. 14 indicates that the maximum von Mises stress is concentrated at the tip of the rectangular notch across all defect depths. The peak stress is observed on the right side of the defect, corresponding to the leading contact region during counterclockwise rotor rotation as viewed from the drive end. This observed asymmetry arises from the combined influence of rotor eccentricity and frictional contact, which intensify local stress at the leading edge. The von Mises stress at the tip of the rectangular notch is 0.462MPa,0.385MPa, and 0.343 MPa for defect depths of 1 mm,2 mm, and 3 mm , respectively.
All stress contour plots presented in this section correspond to direct post-processing outputs from ANSYS Mechanical, and the original numerical color scales are preserved to allow clear visualization of stress magnitudes at critical locations.

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Fig. 15. Effect of rotor-stator interference and rectangular defect depth on maximum von Mises stress along the stator.

Fig. 15. Effect of rotor-stator interference and rectangular defect depth on maximum von Mises stress along the stator.

Fig. 15 indicates that rotor-stator interference predominantly governs the maximum von Mises stress in the stator, with greater interference resulting in higher overall stress levels.
While deeper defects may alter the overall stress field within the stator due to changes in load transfer and stiffness distribution, the peak von Mises stress at the defect tip diminishes. Under non-defective conditions, the stress distribution remains relatively uniform. Once a defect is introduced, a localized stress concentration develops around the notch. Consistent with Fig. 14, deeper defects alter the stress distribution, increasing total stress redistribution within the stator while reducing the local notch-tip peak stress.

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Fig. 16. Effect of rotor-stator interference and rectangular defect depth on maximum total deformation at the stator middle section.

Fig. 16. Effect of rotor-stator interference and rectangular defect depth on maximum total deformation at the stator middle section.

Fig. 16 demonstrates that the nearly parallel trends of the curves indicate defect depth does not significantly alter the effective structural stiffness of the stator under interference loading. This finding suggests that deformation in the middle section is primarily governed by bulk compression,
rather than by localized stress-concentration effects. The global deformation response of the stator is thus determined by the overall interference between the rotor and stator, which produces compressive loading across the elastomer body. While the presence of a rectangular defect introduces a geometric discontinuity along the stator surface, its effect on the overall deformation behavior at the middle section remains limited. Consequently, the deformation pattern reflects the global mechanical response of the elastomeric stator under interference-induced contact pressure, rather than localized geometric irregularities. These results indicate that, within the investigated defect depth range, the structural response of the stator is governed mainly by the magnitude of rotor-stator interference, rather than by variations in defect geometry.

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Fig. 17. Variation of maximum von Mises stress along the stator with respect to rectangular defect depth for different interference values (0,0.15,0.30, and 0.45 mm).

Fig. 17. Variation of maximum von Mises stress along the stator with respect to rectangular defect depth for different interference values ( 0 , 0.15 , 0.30 , and 0.45 mm ) .

Fig. 17 presents the variation in maximum von Mises stress as a function of rectangular defect depth for various interference values. Across all interference levels, maximum stress decreases as defect depth increases from 1 mm to 3 mm . Conversely, increasing rotor-stator interference substantially elevates the maximum von Mises stress, demonstrating that interference is the primary factor influencing stator stress levels.
The observed reduction in stress with increasing defect depth indicates a redistribution of contact pressure within the stator, thereby mitigating local stress concentrations. This finding aligns with the trends reported in Figure 15, where maximum stress progressively decreases with increasing defect depth at a constant interference level.
Mechanically, this behavior results from the interaction between the rotor and the elastomeric stator. As the rectangular defect deepens, the local stiffness of the stator near the defect region decreases, enabling the elastomer to deform more readily under applied rotor-stator interference. As a result, the contact load is distributed over a broader region of the stator surface instead of being concentrated at the defect edge. This redistribution lowers the peak von Mises stress at the defect tip, although the overall deformation of the stator may increase.
In summary, although interference strongly influences the stress response, deeper rectangular defects do not intensify stress concentration at the evaluated stator location. Instead, they alter the local loadtransfer mechanism within the elastomer.

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Fig. 18. Variation of maximum total deformation along the stator with respect to rectangular defect depth for different interference values ( 0,0.15,0.30, and 0.45 mm ).

Fig. 18. Variation of maximum total deformation along the stator with respect to rectangular defect depth for different interference values ( 0 , 0.15 , 0.30 , and 0.45 mm ).

Fig. 18 illustrates the variation in maximum total deformation along the stator as a function of rectangular defect depth at various interference levels. For all interference values, deformation changes only marginally as defect depth increases from 1 mm to 3 mm . A minor peak in deformation is observed at a defect depth of 2 mm , followed by a slight reduction at 3 mm .
In contrast, interference exerts a substantially greater influence on the magnitude of deformation. Higher interference levels consistently result in greater total deformation, indicating that interference is the primary factor governing stator deformation. These findings suggest that, within the investigated range, rectangular defect depth has a limited effect on maximum total deformation relative to rotor-stator interference. This observation is consistent with the trends reported in Fig. 16. Although von Mises stress is widely used to characterize overall stress distribution in engineering components, it is not a direct failure criterion for elastomeric materials. Therefore, additional indicators including maximum principal strain and strain energy density were also evaluated to better interpret the mechanical response of the hyperelastic stator. These parameters are commonly used in elastomer fatigue and crack initiation studies because they more directly represent the deformationdriven failure mechanisms of rubber-like materials. The combined analysis of von Mises stress, principal strain, and strain energy density therefore provides a more comprehensive assessment of potential crack initiation regions.

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Fig. 19. Maximum principal strain along the stator as a function of rotor-stator interference for nondefective and defective configurations.

Fig. 19. Maximum principal strain along the stator as a function of rotor-stator interference for nondefective and defective configurations.

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Fig. 20. Maximum principal strain at the stator midsection as a function of rotor-stator interference for non-defective and defective configurations.

Fig. 20. Maximum principal strain at the stator midsection as a function of rotor-stator interference for non-defective and defective configurations.

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Fig. 21. Strain energy density along the stator as a function of rotor-stator interference for nondefective and defective configurations.

Fig. 21. Strain energy density along the stator as a function of rotor-stator interference for nondefective and defective configurations.

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Fig. 22. Strain energy density at the stator middle section as a function of rotor-stator interference for non-defective and defective configurations.

Fig. 22. Strain energy density at the stator middle section as a function of rotor-stator interference for non-defective and defective configurations.

The strain response of the stator under varying rotor-stator interference is examined by analyzing the distribution of principal strain and strain energy density. Figure 19 presents the variation of maximum principal strain along the stator length for both non-defective and defective configurations. The results indicate that principal strain increases consistently with greater interference, demonstrating that higher interference induces increased elastic deformation within the stator elastomer. The nondefective stator exhibits the lowest strain values, while defective configurations display elevated strain levels due to local reductions in structural stiffness resulting from geometric discontinuities at the defect region.
A comparable trend is observed at the stator middle section, as shown in Figure 20. The maximum principal strain increases progressively with greater interference across all defect depths. However, the magnitude of strain varies according to defect geometry. The 1 mm rectangular defect produces the highest strain concentration, followed by the 2 mm and 3 mm defects. These results suggest that smaller defects create sharper geometric discontinuities, intensifying local strain concentration, whereas deeper defects permit deformation to redistribute over a broader region of the elastomer.

To further assess the mechanical response of the elastomer, strain energy density is analyzed along the stator and at the middle section, as depicted in Figures 21 and 22. Unlike the principal strain response, strain energy density demonstrates a distinctly nonlinear increase with interference. This behavior is characteristic of hyperelastic materials, in which stored elastic energy rises more rapidly than strain as deformation increases. Furthermore, defective stators consistently exhibit higher strain energy density than the non-defective condition, indicating greater localized energy accumulation near the defect region.
The integration of strain-based indicators with conventional stress analysis improves the reliability of interpreting defect-induced damage mechanisms in hyperelastic stator materials.
From a mechanical perspective, this observation is important because strain energy density is widely recognized as a key indicator of fatigue damage initiation in elastomeric materials. Increased localized energy accumulation suggests a higher probability of crack initiation and material degradation during cyclic rotor-stator interaction. Although deeper defects slightly reduce peak strain concentration, the presence of geometric discontinuities still results in higher strain energy accumulation compared to the non-defective condition.
Although direct experimental validation of defect-induced stress was not performed in this study, the predicted stress trends are consistent with previously reported interference-stress relationships in PCP stators [4,7]. Moreover, the hyperelastic material model was calibrated against experimental tensile data, providing confidence in the predicted nonlinear deformation and stress response. Therefore, within the investigated parameter range, the numerical simulations offer a reliable representation of the stator's mechanical behavior under varying interference and defect conditions. Despite these contributions, several limitations should be acknowledged. The present model idealizes defects as rectangular grooves with sharp corners and does not account for irregular wear patterns that may occur in real stator surfaces. In addition, fluid pressure and fluid-structure interaction effects were not included, and the fatigue life of the elastomer was not predicted directly. Future studies should incorporate experimental measurements of defect-induced stress fields, fluid-structure interaction simulations, and fatigue life prediction based on strain energy density or crack growth models to obtain a more comprehensive understanding of PCP stator durability.

Conclusion

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This study numerically investigates the effects of rotor-stator interference and rectangular surface defects on the mechanical response of a Progressive Cavity Pump (PCP) stator elastomer through three-dimensional hyperelastic finite element analysis. The stator is represented as NBR using a thirdorder Ogden model calibrated from experimental tensile data, and frictional contact is incorporated to capture nonlinear rotor-stator interactions. The results indicate that rotor-stator interference is the predominant factor influencing the stator mechanical response, leading to significant increases in maximum von Mises stress, maximum principal strain, strain energy density, and total deformation. Although surface defects cause localized stress concentrations at the notch edges, increased defect depth reduces the peak notch-tip stress due to enhanced local compliance and stress redistribution within the elastomer. In addition, the presence of geometric discontinuities results in elevated principal strain and strain energy density near the defect region, suggesting a higher potential for fatigue-related damage under cyclic rotor-stator interaction. Overall, the findings demonstrate that interference-induced preload primarily governs the stress and deformation behavior of the stator, while defect geometry mainly alters local stress distribution. These results provide quantitative insights into defect-interference interaction mechanisms and contribute to the development of more reliable and defect-tolerant PCP stator designs.

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Nguyen, T., Al-Safran, E., Saasen, A., & Nes, O. M. (2014). Modeling the design and performance of progressing cavity pump using 3-D vector approach. Journal of Petroleum Science and Engineering, 122, 180-186, .

Nguyen, T., Tu, H., Al-Safran, E., & Saasen, A. (2016). Simulation of single-phase liquid flow in progressing cavity pump. Journal of Petroleum Science and Engineering, 147, 617-623, .

Lake, G. J., & Lindley, P. B. (1964). Cut growth and fatigue of rubbers. II. Experiments on a noncrystallizing rubber. Journal of Applied Polymer Science, 8(2), 707-721, .

Mars, W. V., & Fatemi, A. (2002). A literature survey on fatigue analysis approaches for rubber. International Journal of Fatigue, 24(9), 949-961, .

Ganniari-Papageorgiou, E., Chatzistergos, P., & Wang, X. (2020). The influence of the honeycomb design parameters on the mechanical behavior of non-pneumatic tires. International Journal of Applied Mechanics, 12(03), 2050024, .

Rugsaj, R., & Suvanjumrat, C. (2019). Proper radial spokes of non-pneumatic tire for vertical load supporting by finite element analysis. International Journal of Automotive Technology, 20(4), 801-812, .

Daynes, S. (2025). Elastically isotropic auxetic and zero-Poisson's ratio TPMS-based metamaterials. Virtual and Physical Prototyping, 20(1), e2449569,

ASTM International, Standard Test Methods for Vulcanized Rubber and Thermoplastic Elastomers-Tension, ASTM D412-98a (Reapproved 2002), ASTM International, West Conshohocken, PA, 2003, doi:10.1520/D0412-98A.

Gamboa, J., Olivet, A., Iglesias, J. C., & Gonzalez, P. (2003). Understanding the performance of a progressive cavity pump with metallic stator. In Proceeding of the Twentieth International Pump Users Symposium, 2003 (pp. 19-31), .

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Engineering Innovations, ISSN: 2813-1002, Vol. 19, pp 101-106

doi: 10.4028/p-JOBaY6

© 2026 The Author(s). Published by Trans Tech Publications Ltd, Switzerland.

Submitted:
2025-09-08

Accepted:
2025-09-28

Online:
2026-06-11

Bayesian Strategy for the Volume Accuracy of Ink Droplets Continuous Estimation during Printing Based on Prior Knowledge with Earth Mover's Distance

The original version of this paper is available on https://www.scientific.net/EI.19.101.pdf

Deyi Kong 1,a, Jiankui Chen 1, b*, Jiacong Xiong 1,c

1 The State Key Laboratory of Intelligent Manufacturing Equipment and Technology, School of Mechanical Science and Engineering, Huazhong University of Science and Technology, Wuhan, 430074, China

a  d202280271@hust.edu.cn, b  chenjk@hust.edu.cn, cjiacongx@hust.edu.cn

Keywords: IJP OLED, volume accuracy, bayes inference, continuous evaluation.

Abstract

Monitoring the volume accuracy of ink droplets during printing is important for OLED panel production. This article uses data from ink droplet observation volumes before printing to build a membership distribution histogram and intuitively describes the volume status of multiple nozzles. A histogram in conjunction with the Earth Mover's Distance (EMD) indicator is used to design prior knowledge, which describes a subjective estimate of ink droplet volume distribution based on experimental data. And using the likelihood function, combining the sampled data of the ink droplets observation volume, obtains the volume accuracy estimate of the ink droplets during printing. This article proposes a Bayesian strategy for the volume accuracy of ink droplet continuous estimation during printing based on prior knowledge with EMD. This strategy can randomly inspect any number of samples of nozzles in the production process and get a reasonable estimate of volume after each sampling observation, which can realize continuous estimation and adjustment for the volume accuracy of ink droplets during printing.

Introduction

The original version of this paper is available on https://www.scientific.net/EI.19.101.pdf

Inkjet printing technology is widely used in the manufacturing of various flexible electronic products, especially in the manufacturing process of OLED display panels. Pixel volume filling defect is the main obstacle for the mass production of IJP OLED. With the gradual increase of display resolution and panel size, it is necessary to adopt multi-printhead splicing to increase the number of printing nozzles to improve IJP OLED's production efficiency [1]. Due to solute deposition, parameter change, and other interference factors, the volume of ink droplets may change, resulting in a decrease in volume accuracy of ink droplets during printing and panel defects [2]. Detecting the ink droplet volume of part nozzles to estimate the volume accuracy of all during the printing process is a reasonable solution with advantages.

The key step of IJP OLED production is observing precisely the volume of ink droplets before printing. For different application scenarios and process requirements, there are many ways to measure the volume of an ink droplet. At present, ink droplet volume measurement methods are mainly classified as offline measurement and online measurement [3]. At offline measurement, white light interferometry has the best accuracy. For droplets with a diameter of 50 microns, white light interferometry can achieve measurement accuracy that is suitable for obtaining a certain amount of average volume of deposition droplets [4]. Online measurement method including visual, laser phase doppler analysis (PDA), and the ultrasonic attenuation method. For the 10 pL of spherical droplets, the PDA method equivalent measurement precision can reach [5]. Traditional visual methods are difficult to achieve the equivalent measurement accuracy of. Compared with the visual method, the ultrasonic attenuation method measures the volume of an ink droplet within an error [6]. To achieve higher measurement accuracy, the time or cost of measurement is also increased. To continuously estimate the volume accuracy of ink droplets during printing, measures need to be taken to balance efficiency and precision.

This article proposes a Bayesian strategy for the volume accuracy of ink droplet continuous estimation during printing based on prior knowledge with EMD, which can realize the combination of subjective information and objective information during the printing process. This strategy can get more accurate estimates, without relying on a large amount of data training. This strategy can be used even if the process parameters and equipment change, which is efficient in realizing the stable control of the volume accuracy of ink droplets during continuous printing.

Description method of the ink droplet volume accuracy of multiple nozzles.

The original version of this paper is available on https://www.scientific.net/EI.19.101.pdf

Due to parameter fluctuation, installation accuracy, measurement accuracy, and other reasons, continuous measurement of the ink droplets' volume at a selected nozzle will result in different measurement results. In order to estimate the observation accuracy, we can use the median error to estimate the accuracy of the measurement result. At this scene, the median error of measuring result is defined as the times of continuous ink droplet volume measurement on a certain nozzle in a short period of time under the same conditions, Δ1,Δ2,…,Δn represents the square of the difference between the measured value and the measured mean value. The median error m is:

m=±[Δ1+Δ2+…+Δn]n(1)

m can reflect the measurement accuracy of the observed method and also represent the change of ink droplet volume in a short time scale. For a measuring result Va of a single nozzle, we can use [Va−m,Va+m] to represent its true value's possible range. In order to improve observation efficiency, we expect fewer observed frequencies. And we want to estimate the measuring result based on m.

Histogram {h} can represent a mapping of a multidimensional vector to a group of non-negative real numbers. These vectors represent a fixed interval of the histogram. We can divide the ink droplets observation volume data into M intervals. The midpoint of intervals is the average volume Vavg  of all ink droplets, and the distance between each interval is 2m. The center Vhi of each interval {hi} respectively Vavg −(M−1)m,…,Vavg −2m,Vavg ,Vavg +2m,…,Vavg +(M−1)m,hi represents the nozzles number of each interval, and i(1≤i≤M) is the index of the interval.

The membership degree of the ink droplet observation volume for each interval is wij.N is the number of nozzles, and the membership degree wij of the NO. j nozzle ink droplet observation volume Vj for interval hi is as follows:

ij={0i>11i=1wij={0i<M1i=Mwij={Vj−Va−12mi=a−1Va−Vj2mi=a0i≠a−1,aVha−1≤Vh1≤VhM. . . . . yi=Vha(1<a<M)(2)

The amount of each interval that can be transferred to a normalized result is:

hi={whi=∑j=1Nwij/N}(3)

Vhi can be a multidimensional vector describing the status of an ink droplet. Drop point deviation, Angle, velocity, and other characteristics of the ink droplet can also be used as indicators to describe the print status. In this paper, only the volume of the ink droplet is studied. Fig. 1(a) shows a set of
experimental test results. Fig. 1(b) shows five times of continuous ink droplet volume measurement on a certain nozzle in a short period under the same conditions. Fig. 1(c) shows converting the observation ink droplet volume to a membership histogram, which makes it easy to get the status of the ink droplet volume.

[picture]

Fig. 1. Observation volume data converted to a histogram. (a) a set of experimental test results. (b) multi-measurements from a certain nozzle. (c) Convert to histogram representation.

Fig. 1. Observation volume data converted to a histogram. (a) a set of experimental test results. (b) multi-measurements from a certain nozzle. (c) Convert to histogram representation.

[ dij ] represents the conversion cost between different intervals, and is represented here by the Euclidean distance between the mean volume represented by different intervals:

dij=|Vhi−Vhj|(4)

WORK(P,Q,F)=∑i=1a∑j=1bdijfij(5)

WORK(P,Q,F) denotes the cost of transformation between distributions P and Q. The above formula is subject to the following constraints:

fij≥01≤i≤a,1≤j≤b∑i=1afij≤whpi1≤i≤a∑j=1bfij≤whqj1≤j≤b∑i=1a∑j=1bfij=min(∑i=1awhpi,∑j=1bwhqj).

(6)(7)(8)(9)

Equation (5) restricts the transformation of the distribution from P to Q and vice versa. Equation (7) shows the maximum weight that P can transfer. Equation (8) limits the upper limit of transformation targets in Q. Equation (9) forces to movement of as much supply as possible. Equations (6)-(9) can be solved as a linear programming problem.

Solve the distribution transformation problem in the background of the corresponding transportation problem, and find the optimal flow F . The distance of EMD is defined as the result of the normalization of the total flow:

EMD(P,Q)=∑i=1a∑j=1bdijfij∑i=1a∑j=1bfij.(10)

Bayesian continuous estimation strategy.

The original version of this paper is available on https://www.scientific.net/EI.19.101.pdf

We can obtain all possible distribution results through enumeration, and obtain the prior probability P(ΩQ) of any distribution Q as follows:

p(ΩQ)=EMDQ−EMDmaxEMDmax−EMDmin(11)

Here, EMDQ is the distance between distribution Q and the ink droplet volume observation distribution P before printing, EMDmin  is the minimum distance among all distributions, and EMDmax  is the maximum among.

Likelihood function can introduce objective information of observation results into the Bayesian process. L(Ω)→P(V∣Ω), the likelihood function is proportional to the probability of observing the experimental result V in the printing process under a given distribution. V is an ink droplet volume observation result in the printing process. For the observed volume of ink droplet Va, combined with the median error and the actual volume of ink droplet Va∈(Va−m,Va+m), if Va≥Vavg+(M−1)m or Va≤Vavg −(M−1)m, we classify them into two extreme distributions {h1} and {hM}, and the corresponding likelihood function L(Ω) is:

L(Ω)={whMVa≥VhMwhi−1+whi+whi−1( Vhi−1−Va)+whi(Va−Vhi)2 mVhi−1<Va<Vhi(1≤i≤M)wh1Va≤Vh1(12)

The Bayesian rule can be used to combine the prior distribution with the observation information. An estimation approaching the truth can be obtained gradually from the sampling results. To ensure the reliability of estimates, we use the mean of the posterior estimates as the optimal estimation. H is the number of samples of the posterior distribution, M is the number of intervals, the result obtained is the ink droplets volume accuracy estimation of all nozzles w1,w2,…,wM. Estimation of the posterior distribution P(w1,w2,…,wM|[1,…,k]) is:

p(w1…wM∣V[1…k])=∑i=1Hw1ip(Ωi∣V[1..k]),∑i=1Hw2ip(Ωi∣V[1…k]),…∑i=1HwMip(Ωi∣V[1…k])(13)

Experimental verification.

The original version of this paper is available on https://www.scientific.net/EI.19.101.pdf

As shown in Fig. 2(a), the experiment is carried out in the independent domestic IJP OLED Equipment, which uses a QS256 inkjet printer head. And we use this equipment under the common process data. Fig. 2(b) shows the electron-hole transport layer (HTL) of the OLED panel printed. Fig. 2(c) shows the method we use for ink droplet observation, which takes an image by two cameras together. And this method takes more than 15 minutes to measure the ink droplet volume of the selected 256 nozzles. The basic parameter settings of the test process are shown in Table 1.

[picture]

Fig. 2. Experiment condition. (a) Independent IJP OLED equipment. (b) OLED panel after printing. (c) Observed method.

Fig. 2. Experiment condition. (a) Independent IJP OLED equipment. (b) OLED panel after printing. (c) Observed method.

Table 1. Experiment Parameters.

Parameter | Value | Parameter | Value
Nozzle number | 256 | Print height(mm) | 0.57
Nominal volume(pL) | 11.5 | Print frequency(kHz) | 20
Midian error(pL) | 0.15 | Print speed(mm/s) | 100
Nominal ink droplet velocity(m/s) | 3 |  |

According to the Bayesian theory, it is necessary to integrate the L(Ω)P(Ω) under the prior distribution to obtain the complete posterior distribution, which is very difficult to calculate the high-dimensional integral. In order to facilitate the calculation of posterior results, the prior distribution can be discretized with the minimum unit 0.02 , and the set of the prior distribution can be expressed as Q=0.02×{(q1,wq1),(q2,wq2),…,(qM,wqM)},(wq1+wq2+…+wqM=50),wq1,wq2,…,wqM is integer. Sampling enough nozzles would obviously yield a more accurate estimate. And we can clearly know that the ink droplet volume state does not change a lot in a short time. So we want to validate the Bayesian strategy under the condition of fewer nozzles and longer time. As shown in Fig. 3, the experiment is designed to conduct ink droplet observation of all nozzles at Day 1, Day 3, Day 10, Day 11, Day 15, and Day 18. And before the ink droplet observation of all nozzles, we sample 15% nozzles and use the Bayesian strategy to estimate the inkjet droplet volume accuracy based on the data of the last ink droplet observation of all nozzles.

[picture]

Fig. 3. Experimental test data.

Fig. 3. Experimental test data.

As shown in Fig. 4, the Bayesian strategy we propose achieves an overall good performance. Over the whole, when the number of sampling nozzles reaches 10%, the deviation estimated for each interval is less than 10%. If the number of samples is further increased to about 15%, the deviation estimated for each interval is less than 5%. And if the status of ink droplet volume changes little, which also means the interval between two status check times is short, a suitable estimation can be obtained from a small number of samples. And even after a long time, we can still get obvious results based on a sample of less than 15%. Compared with full observation of all nozzles, efficiency can be greatly improved by the Bayesian method we proposed.

[picture]

Fig. 4. (a) Estimation of day 3. (b) Estimation of day 10. (c) Estimation of day 15. (d) Estimation of day 18 .

Fig. 4. (a) Estimation of day 3. (b) Estimation of day 10. (c) Estimation of day 15. (d) Estimation of day 18 .

Conclusion

The original version of this paper is available on https://www.scientific.net/EI.19.101.pdf

In this paper, a Bayesian strategy for inkjet printing volume accuracy continuous evaluation based on an EMD prior is proposed, which can guarantee the stability of ink droplet volume accuracy and not affect the production efficiency. Compare the methods based on the model and artificial neural network, this method has obvious advantages. First, it has universality for different process parameters and equipment parameters, so this can be applied in any printing scenario and equipment. Second, this method doesn't need a lot of data for training, and only with limited data and computational processes can it achieve good results. In addition, this method can be combined with a control strategy and decision strategy to achieve the dynamic control of ink droplet volume accuracy. With the improvement of inkjet print accuracy, I think more and more sophisticated electronic components can be manufactured by inkjet printing.

References

The original version of this paper is available on https://www.scientific.net/EI.19.101.pdf

Gardner S D, Alexander J, Massoud Y, et al. Minimally produced inkjet-printed tactile sensor model for improved data reliability. 2020 11th International Conference on Electrical and Computer Engineering (ICECE). 2020.

IA. Zampetti, A.Minotto, F. Cacialli. Near-Infrared (NIR) Organic Light-Emitting Diodes (OLEDs): challenges and opportunities. Adv. Funct. Mater. 29, 1807623 (2019).

Qiangqiang Liu, Jiankui Chen, Hua Yang, Zhouping Yin. Accurate Stereo-Vision-Based Flying Droplet Volume Measurement Method. IEEE Transactions on Instrumentation and Measurement, 2022, VOL,71:5000116.

Shin D Y, Kim M Rapid jetting status inspection and accurate droplet volume measurement for a piezo drop-on-demand inkjet print head using a scanning mirror for display applications. Review of Scientific Instruments, 2017, 88(2):025109.

D. Y Shin, "Study on ink-jetted droplet volume measurement using surface energy patterned channels," in Printing for Fabrication 2019.

Sun H, Luo Y, Ding H. Research on Droplet Properties in Atomization using Optical Imaging Measurements. 2020 IEEE International Instrumentation and Measurement Technology Conference (I2MTC). IEEE, 2020.

This ePub file was created using machine-generated methods and automated software to convert the source text into an ePub format and may contain formatting inconsistencies from the original version. Formatting may vary depending on the device used.

The original version of this eBook is available on https://www.scientific.net/book/engineering-innovations-vol-19/978-3-0364-1217-7

Engineering Innovations, ISSN: 2813-1002, Vol. 19, pp 107-113

doi: 10.4028/p-D1g7wK

© 2026 The Author(s). Published by Trans Tech Publications Ltd, Switzerland.

Submitted:
2025-09-08

Accepted:
2025-09-28

Online:
2026-06-11

The Printing of Highly Uniform Display Device by Graph Convolutional Neural Network-Based Manufacturing

The original version of this paper is available on https://www.scientific.net/EI.19.107.pdf

Jiacong Xiong 1,a, Jiankui Chen 1, b* and Ziwei Zhao 1,c

1 The State Key Laboratory of Intelligent Manufacturing Equipment and Technology, School of Mechanical Science and Engineering, Huazhong University of Science and Technology, Wuhan, 430074, China

ajiacongx@hust.edu.cn, b*chenjk@hust.edu.cn, c  zhaoziwei@hust.edu.cn

Keywords: organic light-emitting diode, print manufacturing, integer planning, graph neural network, high uniformity manufacturing.

Abstract

The field of print display manufacturing still faces the challenge of uneven volume printing. This paper proposes an integer programming framework tailored to the printing process for volume optimisation, alongside a mimetic solving algorithm based on a graph convolutional neural network. These methods enable the accurate resolution of models and achieve volume uniformity with an error margin of less than 2%, effectively addressing inconsistencies in pixel pit printing. Simulation and experimental results validate the efficacy of the approach.

Introduction

The original version of this paper is available on https://www.scientific.net/EI.19.107.pdf

In contemporary times, the display is the core interface for human-computer interaction, and technological advances in this area have attracted much attention. Organic light-emitting diodes (OLEDs) stand out due to their ultra-thin, flexible, high-brightness, self-luminous and energy-efficient features [1−3]. These features suggest a promising future for OLEDs in mobile and computing devices, with the potential to become the next-generation mainstream display technology. Meanwhile, the OLED manufacturing process is undergoing a revolution. Compared with the traditional vacuum vapour deposition method, emerging inkjet printing technology has become the dominant approach due to its advantages in terms of low environmental requirements, a mask-free version, rapid development, and material efficiency [4]. Looking to the future, market demand for ultra-large-size and ultra-high-resolution OLEDs is pushing the precision control and mass production efficiency of the printing process to its limits. The related core challenges are detailed in Fig. 1.

[picture]

Fig. 1. OLED structure, and uneven volume of printed manufacturing oled pixel pits cause display defects.

Fig. 1. OLED structure, and uneven volume of printed manufacturing oled pixel pits cause display defects.

Fig. 1 illustrates the multilayer thin-film structure of organic light-emitting diodes (OLEDs), which is formed by printing a solution into pixel pits that are subsequently dried and cured. However, differences in the volume of droplets ejected by different nozzles of the printhead, when driven by the same piezoelectric waveform, lead to uneven film thickness after the solution has dried inside the pixel pit. This affects the uniformity of luminescence. Yet this issue has received little attention in
existing studies. Kateeva, U.S.A., first raised this issue. Madigan [5] of Kateeva published work on the volumetric uniformity of large-size substrate printing fabrication, proposing the idea of droplet mixing for printing, but did not present details related to path planning. Cao [6] initiated multi-nozzle printing experiments by adjusting the printhead waveform to reduce uniformity errors to 5%. Wang [7] proposed a closed-loop control framework for droplet regulation based on visual detection using neural networks and PID control processes to determine drive voltage adjustments. However, since debugging a solution to the error tolerance interval is difficult and costly, a more adaptable strategy is to design a fusion printing plan between ink drops. Xiong have proposed a droplet mixing inkjet printing planning model based on mixed integer programming [8], and have also proposed a GAT-based solution algorithm for solving integer programming models without mixing [9]. The main innovation of this paper is an integer programming model based on intelligent mixed inkjet printing, with the optimization objective of minimizing the number of inkjet printing operations, and using the GCN algorithm for solution. Lian [10] used an improved TLO method combined with deep reinforcement learning for the multi-objective optimisation of OLED printing. However, the rotational modelling method for the printhead is not applicable to large-scale production, and the effectiveness of the planning method has not been verified experimentally. Existing methods either do not address the issue of uniform volume printing or the solutions are insufficient to solve existing problems completely. This paper proposes a printing integer programming model based on a graph convolutional neural network to address the issue of achieving high uniformity when printing pixel pits. This model combines droplets from different nozzles to ensure that the volume of each pixel pit meets the required specifications, thereby achieving high uniformity in the printing process.

Printed Integer Planning Model Based on Graph Convolutional Neural Network

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Printing integer planning model.

The original version of this paper is available on https://www.scientific.net/EI.19.107.pdf

Printing pixel pits by combining droplets between different nozzles allows for complementary differences between droplets, resulting in hybrid printing. Based on this idea, we can design the corresponding printing planning model. Firstly, the printhead moving direction is determined as Y direction, and the substrate moving direction is X direction. We set multiple equally spaced nozzle movement stopping points in the Y direction in order to facilitate the construction of the positional relationship between nozzles and pixel pits, and to determine that the nozzles can be sprayed into the pixel pits. We assume that the number of nozzles is m, the number of pixel pits is n, and the number of stopping points is p. Thus we can construct the following constraint matrix:

A=(a1,1a1,2⋯a1,m×pa2,1a2,2⋯a2,m×p⋮⋮⋱⋮an,1an,2⋯an,m×p)ai,k×j={Vd,k,|yp,i−yn,jk|0,0, other 

(1)(2)

where the coefficient ai,k×j indicates whether nozzle k can print pixel pit i at stopping point j. When the absolute value of the difference between the Y-direction coordinates of nozzle k,yn,jk, and the coordinates of pixel pit i,yp,i, is in the sprayable range, the coefficient is set to be the volume of nozzle k,Vd,k, and it is 0 otherwise. The construction of nozzles is completed in which all pixel pits can be sprayed. At this point, the decision variable X represents the combination situation for pixel pit i :

X=(x1,1x2,1⋮xm×p,1).xk×j,1∈[0,Njet].

(3)(4)

where X∈ℤn×(m×p) represents the number of jetting times at stopping point j for the k-th nozzle, Njet  is the maximum printing times for a single pixel. The integer programming model is formulated as follows:

min∑j=1pyj,1 s.t. {∑k=1m∑j=1pai,k×jxk×j,1≤Vub,∀i∈[1,n]∑k=1m∑j=1pai,k×jxk×j,1≥Vlb,∀i∈[1,n]xk×j,1−yj,1≤0,∀k∈[1,m],∀j∈[1,p]xk×j,1,yj,1≥0,xk×j,1,yj,1≤Njet,xk×j,1,yj,1∈Z(5)

Vub and Vlb represent the upper and lower bounds for the desired printing volume, respectively. The optimization objective is the minimization of the total printing times, denoted by the sum of yj,1, where yj,1 represents the maximum printing frequency for all nozzles at stopping point j. By solving the integer programming model, the optimal planning model with the minimum printing times under the current conditions can be obtained.

Graph Convolutional Neural Network Solution Algorithm.

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In order to realize the efficient solution of the problem, we design the integer programming solution algorithm dedicated to the printing planning problem. Learning the branching strategy of the solver for the integer planning problem by mimicking it with a graphical convolutional neural network, the problem can be solved more efficiently and with no loss of accuracy.

 min {∑j=1pyj,1 s.t. {∑j=1p∑k=1mai,k×jxk×j,1≤Vub,∀i∈[1,n]∑j=1p∑k=1mai,k×∣xk×j,1≥Vlb,∀i∈[1,n]−−−1xk×j,1−yj,1≤0,∀k―∈[1,m],j∈[1,p]xk×j,1―,yj,1―≥0,xk×j,1―,yj,1―≤Njet―,xk×j,1―,yj,1∈Z Variable nodes an,1a1,m×pxm×0

Fig. 2. Transformation of integer programming problems into two-part graph structures.

We convert the printed integer planning model into a two-part graph structure with variables and constraints as a class of nodes, respectively, by the method shown in Fig. 2. In this way the non-Euclidean data can be trained so that the training results are not affected by the order of inputs.

Training and Computational Simulation

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Graph Neural Network Training.

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Following the prescribed approach, SCIP solver-derived print job data was collected and used to train a GCN solver. Evaluation metrics comparing GCN solver with the original SCIP are visualized in Fig. 3 and Fig. 4. Critically, GCN solver exhibits faster solve speeds, validating its performance advantage in this optimization context.

[picture]

Fig. 3. Loss convergence curve of GCN training process.

Fig. 3. Loss convergence curve of GCN training process.

[picture]

Fig. 4. Comparison of the solving results of SCIP solver and GCN imitation learning: distribution of solving case time and solving case time curve.

Fig. 4. Comparison of the solving results of SCIP solver and GCN imitation learning: distribution of solving case time and solving case time curve.

Combined Printing Computational Simulation.

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By solving the model with GCN, the printhead print path and the print timing of each nozzle can be obtained. To verify the validity of the model, the printing method I using the print drop fusion planning model is compared with the printing method II using the same nozzle drop fusion for simulation. The 394 PPI pixel panel parameters were used as the simulation object, and the pixels were arranged in a diamond arrangement, containing 8 sub-pixel pits within a single pixel, of which the number of RGB three sub-pixels were 2, 4, and 2, and the sub-pixel sizes were 49.98μ m, 35.56μ m, and 42.22μ m, with a pixel center spacing of 128.8μ m and an adjacent sub-pixel center spacing of 32.2μ m. The printhead uses a Dimatix Samba G3L printhead with a nozzle resolution of 1200 DPI and a pitch of 21.16μ m, with a total of 2048 nozzles. GCN and traditional method planning simulation results are shown in Fig. 5. The target volume of printed three-color pixel pits is: red: 9.6784 pl , green: 4.8392 pl , blue: 9.6784 pl . For ink droplet volume in the 10% fluctuation range, the use of traditional method to obtain the fusion volume in the 10% error range can't reach the display device for the film thickness. For the ink droplet volume in the 10% fluctuation range, the fusion volume obtained using traditional method is within 10% error, which cannot meet the requirements of the display device for film thickness, while using GCN, the volume is within 2% error, which significantly improves the volume uniformity between pixels and verifies the model planning.

[picture]

Fig. 5. 394 PPI pixel panel with ink drop fusion volume difference results from GCN and traditional method.

Fig. 5. 394 PPI pixel panel with ink drop fusion volume difference results from GCN and traditional method.

Experimental Verification

The original version of this paper is available on https://www.scientific.net/EI.19.107.pdf

The relevant printing experiments were carried out using the 200 mm×200 mm high-precision printing equipment developed in-house, to further verify the effectiveness of the proposed method. As shown in Fig. 6(a), the inkjet equipment in the IJP mainly completes the printing process, the external glove box to provide a gas environment, and the VCD equipment to complete the subsequent drying and curing process. Fig. 6(b) illustrates the internal structure of the IJP equipment and includes an ink droplet observation system, a printhead, a substrate platform and a positioning camera. Fig. 6(c) depicts a Dimatix Samba G3L piezoelectric printhead featuring a nozzle pitch of 21.166μ m, a nozzle pitch resolution (NPI) of 1200, a minimum print volume of 2.4pl(±10%) and a drop accuracy of 10 μm at a print height of 1 mm . The printed object is a 394 PPI OLED panel (pixel pitch of 64.4μ m ), provided by Wuhan Huaxing Optoelectronics Co., Ltd., China. As shown in Fig. 6(d), the panel is arranged in diamonds and the three RGB pixels are arranged in staggered rows at intervals to improve resolution, with a pixel size of 50μ m. The positioning accuracy of each motion axis of the device is ±3μ m..

[picture]

Fig. 6. 200-type high-precision OLED printing manufacturing equipment.

Fig. 6. 200-type high-precision OLED printing manufacturing equipment.

Firstly, the volume of each nozzle is detected to determine the volume distribution. In this paper, the equipment uses a flying droplet measurement method based on stereo vision, using a binocular camera to obtain a multi-view image of the droplet. An image segmentation algorithm is then used to accurately segment the droplet image based on the contours of the two projection images. Finally, a droplet reconstruction algorithm based on Hermite interpolation in polar coordinates is used to achieve a three-dimensional droplet image. The droplet volume is calculated with an accuracy of 3%. The obtained data is shown in Fig. 7.

[picture]

Fig. 7. Volume distribution of ink droplets for visual observation.

Fig. 7. Volume distribution of ink droplets for visual observation.

We use white light interferometry to detect the droplet volume of the pixel pit and obtain the result shown in Fig. 8. Compared with traditional methods, GCN greatly improves the uniformity of the volume.

[picture]

Fig. 8. 394PPI pixel panel using GCN and Traditional method pixel volume uniformity error detection results.

Fig. 8. 394PPI pixel panel using GCN and Traditional method pixel volume uniformity error detection results.

Summary

The original version of this paper is available on https://www.scientific.net/EI.19.107.pdf

We present a solution to the current problem of uneven ink droplet volume between pixel pits, which are deposited into the array's pixel pits during the printing process, and test the effectiveness of the method. We constructed a printing integer planning method based on a graph neural network and performed simulation calculations and experimental verifications of patterned printing with high uniformity. However, the method is still limited in terms of solving efficiency when applied to the fabrication of large-size substrates and needs to be improved subsequently.

References

The original version of this paper is available on https://www.scientific.net/EI.19.107.pdf

Chen H W, Lee J H, Lin B Y, Chen S, Wu S T. Liquid crystal display and organic light-emitting diode display: present status and future perspectives. Light: Science & Applications, 2018, 7(3): 17168-17168.

Garner S, Chowdhury D, Lewis S. Ultrathin glass substrates for thin, lightweight, flexible OLED lighting. Information Display, 2019, 35(4): 9-13.

Chowdhury D Q, Garner S M, Lewis S C. Application of OLED for automotive lighting. 2019 26th International Workshop on Active-Matrix Flatpanel Displays and Devices (AM-FPD). IEEE, 2019, 26: 1-3.

Yin Z P, Huang Y A, Bu N B, Wang X M, Xiong Y L. Inkjet printing for flexible electronics: Materials, processes and equipments. Chinese Science Bulletin, 2010, 55(30): 3383-3407.

Madigan C F, Hauf C R, Barkley L D, Harjee N, Vronsky E, Van Slyke S A. 30.2: Invited paper: Advancements in inkjet printing for OLED mass production. SID Symposium Digest of Technical Papers. 2014, 45(1): 399-402.

Cao X, Ye Y, Liu X, Guo T, Tang Q . 54.3: Realization of Uniform OLED Pixels based on Multi-nozzle by Inkjet printing. SID Symposium Digest of Technical Papers. 2021, 52: 395-397.

Wang T, Kwok T H, Zhou C, Vader S. In-situ droplet inspection and closed-loop control system using machine learning for liquid metal jet printing. Journal of manufacturing systems, 2018, 47: 83-92.

Xiong J, Chen J, Wang Y, Yin Z, Li Y. Planning method of droplet fusion scheduling based on mixed-integer programming. Science China Technological Sciences, 2024, 67(1): 157-171.

Xiong J, Chen J, Li Y, Yue X, Fu Y, Yin Z. Large-area OLED substrate printing path planning method based on multi-head GAT imitation learning to solve partitioned integer programming. Scientific Reports, 2025, 15(1): 21771.

Lian Z, Lv C, Lu W. Inkjet OLED printing planning based on deep reinforcement learning and reward-based TLO. Journal of Physics: Conference Series. IOP Publishing, 2023, 2450(1): 012081.

This ePub file was created using machine-generated methods and automated software to convert the source text into an ePub format and may contain formatting inconsistencies from the original version. Formatting may vary depending on the device used.

The original version of this eBook is available on https://www.scientific.net/book/engineering-innovations-vol-19/978-3-0364-1217-7

Engineering Innovations, ISSN: 2813-1002, Vol. 19, pp 115-124

doi: 10.4028/p-GzP1hK

© 2026 The Author(s). Published by Trans Tech Publications Ltd, Switzerland.

Submitted:
2025-01-03

Revised:
2026-02-03

Accepted:
2026-02-28

Online:
2026-06-11

Effect of Surface Roughness on the Friction Moment in a Ball Bearing

The original version of this paper is available on https://www.scientific.net/EI.19.115.pdf

Harsh Kumar 1,a* and Mayank Tiwari 2, b

¹Mechanical Engineering Department, Indian Institute of Technology Patna, Patna 801106, India

2 Mechanical Engineering Department, Indian Institute of Technology Palakkad, Kanjikode 678623, India

an  harsh_2021me08@iitp.ac.in, b  mayankt@iitp.ac.in

Keywords: Ball bearings, Lubrication, Rough surface, Friction torque

Abstract

Ball bearings are crucial components in various machinery and mechanical systems, finding applications across numerous industries such as automotive, aerospace, manufacturing, robotics, and household appliances. Minimizing friction and wear in the bearings is essential to enhance the efficiency, reliability, and lifespan of equipment. Friction moments in ball bearings occur due to rolling-sliding motion at the ball-race contact and sliding between the ball and cage. The magnitude of these friction moments depends on factors such as surface topography, load, and speed. Understanding how surface topography influences the friction moments in these bearings is crucial. This paper investigates the effect of surface roughness on the friction moment between the ball and race contact, as well as between the ball and cage contact. An analytical model is employed to estimate the friction torque within ball bearings, considering the total friction torque generated at the contact points between the balls and the race, as well as at the ball-cage interface. A mixed elastohydrodynamic lubrication model is used to estimate the friction coefficient at these contact points.

Introduction

The original version of this paper is available on https://www.scientific.net/EI.19.115.pdf

Bearings are commonly used in automobiles, industry and energy. Single-row ball bearings are especially favoured for their simple design, ability to run at high speeds, and low maintenance costs. However, heat resulting from the power loss due to internal friction in these bearings can affect running torque, which in turn reduces the performance and reliability of systems [1]. Load, speed, and surface roughness are some parameters that influence friction torque, making it crucial to understand and predict it accurately to improve system efficiency and optimize bearing performance.

In ball bearings, there are contacts at the ball-race interface and ball-cage interface. Due to this, there is a generation of friction moments. At the ball-race contact, rotational resistance is primarily caused by microslip, which results due to conformity or spin [2]. Additionally, there is a hysteresisinduced resistance torque in ball bearings [3]. However, in the event of ball-cage contact, the skidding between the balls and cage creates a friction moment. [4].

Several research works are done in the past to understand the influence of surface topography and texture on friction moment for such contacts. Under lubricated conditions, surface roughness has been shown to significantly affect the interaction between balls and screws, as it plays a key role in forming and stabilizing the lubricating film, which reduces direct asperity contact [5,6]. This can also be observed in the case of cylindrical roller bearings [7]. Gouda et al. [8] studied the effects of microtexture placement on the inner race on radial ball bearing tribodynamic performance. Han et al. [9] explored the impact of groove factor and surface roughness on tribological behavior and instability in dry contact.

In ball bearings, the impact of surface roughness on friction torque has not been thoroughly investigated. An analytical model for calculating the frictional moment generated due to contacts between the ball and cage and the ball and race in a low-speed, lubricated ball bearing is presented in this study. The model estimates the friction coefficient at the contact locations using a mixed elastohydrodynamic lubrication (EHL) technique. Ball-race and ball-cage friction moments are examined in relation to surface roughness and speed. This analysis also looks at how these parameters affect the contact force between the ball and the cage.

Theoretical Model

The original version of this paper is available on https://www.scientific.net/EI.19.115.pdf

An analytical framework of the schematic shown in Fig. 1 for calculating the friction moment in a lubricated ball bearing is explained in this section. The model is composed of three elements: ballrace interaction, ball-cage interaction, and a mixed EHL model. The friction torque at the ball-race contact (Trb) is analyzed under static conditions, whereas the friction moment at the ball-cage contact (Tcb) is assessed dynamically. A mixed EHL model is employed to calculate the friction coefficient at these contact points.

Total frictional moment ( Tbb ) in ball bearings is the combined result of the frictional torques generated at the ball-race contact and the ball-cage contact [10], expressed in Eq. 1:

Tbb=Trb+Tcb(1)

This study makes some assumptions to streamline the modeling process and remove less important aspects. First, friction moments due to dust covers and seals are excluded in this model since it is made for open bearings. Secondly, it ignores frictional moments due to lubricant drag losses, churning, and splashing. Lastly, it is assumed that the contact patch at the site of contact follows the Hertzian contact principle and that the bodies undergo local elastic deformation.

[picture]

Fig. 1 A schematic representing different contacts in a ball bearing

Fig. 1. A schematic representing different contacts in a ball bearing

Ball-Race Friction Torque Model. Microslip at ball-race contacts, as explained by Mindlin et al. [2], generates motion resistance in ball bearings. This leads to a rolling moment (Mr) and spin moment (Mz), as shown in Fig. 2, calculated using Eq. 2-7 based on the spin-to-roll ratio ( ϵ ). Pure rolling line positions ( γ1 and γ2 ) depend on ϵ. Friction coefficient μrb is determined using a mixed EHL model.

[picture]

Fig. 2 Cross-sectional view of ball-race contact

Fig. 2. Cross-sectional view of ball-race contact

If ϵ=0,

Mz=μrb Fn b24rbMr=0

(2)(3)

If 0<ϵ<0.5,

Mz=3μrb Fn b4(γ12(1−γ122)−γ22(1−γ222))Mr=μrb Fn b24rb(25+γ13(1−3γ125)−γ23(1−3γ225))

(4)(5)

If ϵ≥0.5,

Mz=0Mr=0.375μrb Fn b

(6)(7)

A rolling element moving along a raceway under compressive force experiences stress relief at the back while the front portion of the contact patch contracts in the rolling direction. A moment called the hysteresis rolling moment is produced by such repetitive stress underneath the rolling track. Rolling moment due to hysteresis ( Mh ) is calculated using Eq. 8 (Ref. [11], p. 285).

Mh=3α′Fna16(8)

As a result, the total friction moment at the ball-race contact ( Trb ) is the sum of all the moments produced at this contact.
where

Trb=Tconformity +Tspin+Thysteresis Tspin=Z×(Mz)sinαTconformity =Z×(MriRo+MroRi)2rb Thysteresis =Z×(MhiRo+MhoRi)2rb

(9)(10)(11)(12)

Ball-Cage Friction Torque Model. This ball-cage friction torque model is based on Jiang et al.'s model [4]. Fig. 3 illustrates the various reference frames utilized in the model. Frame s is the stationary frame fixed to the bearing axis, frame c is the reference frame attached to the centre of the cage, frame b is connected to the centre of the ball, and frame p is associated with the centre of the pocket.

[picture]

Fig. 3 Representation of different reference frames

Fig. 3. Representation of different reference frames

There are three possible contact scenarios at the ball-cage contact: single-point contact, doublepoint contact, and no contact, as depicted in Fig. 4. If ε≥∇ and Δx≠0, there is a single-point contact, if ε≥∇ and Δx=0, there is a double-point contact, while no contact arises when ε<∇.

ε=(|Δx|+|Dp2−k|)2+Δy2+Δz2,∇=Dp2−Db2(13)

[picture]

Fig. 4 (a) single point contact (b) double point contact

Fig. 4. (a) single point contact (b) double point contact

Assuming Hertzian contact, normal contact force ( FpbNp ) at ball-cage contact is determined from the contact deformation (δpb).

|FpbNp|=Kpbδpb32 FpbNp=|FpbNp|Npbp

(14)(15)

where

δpb=ε−∇ and Kpb is the contact stiffness 

Now, using the cage's tangential relative linear velocity and the ball-cage normal contact force, the tangential contact force (FpbTp) can be calculated. The mixed EHL model is used to determine the friction coefficient at the ball-cage contact ( μcb ).

FpbTp=−μcb| FpbNp|ΔvcbTp|ΔvcbTp|(16)

Using Eq. 17, the ball-cage contact force is calculated.

Fpbp=FpbNp+FpbTp(17)

After calculating the ball-cage contact force, total force vector ( Fc ) and total moment vector ( Mc ) in frame c are obtained by using Eq. 18-19.

Fc=∑i=1z Tpci FpbpMc=∑i=1z Tpci(rcqp×Fpbp)

(18)(19)

where Tpci=Tcpi−1
Mixed Elastohydrodynamic Lubrication Model. In the mixed elastohydrodynamic lubrication (EHL) model, the total load ( FT ) is divided between the asperity load ( Fa ) and the load supported by the lubricant film (Fh) [12]. Both asperities and the lubricant film shear, creating frictional forces at
the contact surfaces. The asperity shear force is determined by adding the shear force from each asperity calculated by employing the asperity contact model by Zhao et al. [13]. Nijenbanning's numerical solution [14] for elliptical contact is employed to calculate the central film thickness. The shear stress is then calculated using the Eyring shear stress, and the shear force within the film is determined by multiplying the shear stress by the hydrodynamic area. The formula for determining the total frictional force produced in contacting surfaces is given by Eq. 21. The friction coefficient is then determined using Eq. 22.

Ft=Fa+FhFf=Ff,a+Ff,hμ=FfFt

(20)(21)(22)

Methodology

The original version of this paper is available on https://www.scientific.net/EI.19.115.pdf

[picture]

Fig. 5 Numerically generated rough surface of (a) Sq=1μ m and (b) Sq=0.1μ m

Fig. 5. Numerically generated rough surface of (a) S q = 1 μ m and (b) S q = 0.1 μ m

In this paper, the effect of surface roughness on friction torque is analyzed. For the analysis, a 6205 ball bearing is considered, with its design parameters provided in Table 1. Rough surfaces with RMS roughness values of Sq=1μ m and Sq=0.1μ m are numerically generated under the assumption of a Gaussian distribution. Fig. 5 illustrates the generated rough surface. The mixed EHL model uses these numerically produced surfaces as input to predict the friction coefficient. The analysis is conducted under a constant axial load of 1000 N , with the outer race fixed and the inner race rotating at speeds from 100 to 500 RPM in 100 RPM increments.

Table 1 Input parameters for the 6205 ball bearing.

Parameters | Value
Number of balls, Z | 9
Mass of ball, Mb | 2.04 [gm]
Mass of cage, mc | 6.58 [gm]
Contact angle, α | 0.18 [rad]
Ball diameter, Db | 7.94 [mm]
Mass of inner race | 38.63 [gm]
Mass of outer race | 54.21 [gm]
Radius of inner race | 15.53 [mm]
Radius of outer race | 23.48 [mm]
Groove radius of inner race | 4.13 [mm]
Groove radius of outer race | 4.21 [mm]
Pocket diameter | 13.9 [mm]
Pocket depth | 6.81 [mm]

Results and Discussion

The original version of this paper is available on https://www.scientific.net/EI.19.115.pdf

This section examines a 6205 ball bearing's friction moment, highlighting the impact of speed and surface roughness. The impact of these variables on the estimated friction coefficient is also investigated in the study. In order to comprehend its contribution to ball-cage friction moment, the impact of surface roughness on the ball-cage contact force is also examined.

Variation of total friction moment. The variation of total friction moment ( Nmm ) with speed (RPM) for a 6205 ball bearing for two different surface roughness levels ( Sq=1μ m and Sq=0.1μ m ) is shown in Fig. 6. For both surface roughness, the data indicates that friction torque rises with rotational speed. Bearings with higher surface roughness ( Sq=1μ m ) consistently demonstrate greater friction torque compared to those with smoother surfaces ( Sq=0.1μ m ) across all speeds. The friction torque rises from 17.89 Nmm at 100 RPM to 25.09 Nmm at 500 RPM for the rougher surface ( Sq=1μ m ). Conversely, the friction torque increases from 8.67 Nmm at 100 RPM to 12.90 Nmm at 500 RPM for the smoother surface (Sq=0.1μ m). These results indicate that higher surface roughness significantly amplifies frictional losses, underscoring the importance of smoother surfaces in minimizing friction torque.

[picture]

Fig. 6 Plot of change in friction torque

Fig. 6. Plot of change in friction torque

Variation of friction coefficient at the contacts. The effect of surface roughness on friction coefficient (COF) at different locations of contact in a bearing at varying rotational speeds is shown in Fig. 7. In Fig. 7(a), the friction coefficient for the ball-outer race contact decreases with reduced surface roughness, showing higher friction coefficient for the rougher surface as compared to the smoother surface, with minimal impact from speed. Similarly, the friction coefficient at the ball-inner race contact is shown in Fig. 7(b), where a lower coefficient of friction results from a decrease in surface roughness, but speed has minimal bearing on roughness levels. The friction coefficient at the ball-cage contact, on the other hand, is highlighted in Fig. 7(c), where speed and surface roughness both have a substantial impact. At lower speeds, rougher surfaces exhibit higher COF, while smoother surface shows a marked decrease in COF as speed increases, indicating that higher speeds help reduce friction for smoother surfaces in this contact type. Overall, the results indicate that higher surface roughness leads to consistently higher COF values across all contact points and speeds. In contrast, smoother surfaces exhibit a slight reduction in COF with increasing speed, particularly in the ballcage contact, suggesting reduced frictional losses at higher speeds.

[picture]

Fig. 7 Coefficient of friction at (a) ball-outer race contact, (b) ball-inner race contact, and (c) ballcage contact

Fig. 7. Coefficient of friction at (a) ball-outer race contact, (b) ball-inner race contact, and (c) ballcage contact

Variation of friction moment at ball-race and ball-cage contacts. The total friction torque generated in a ball bearing is the sum of friction torque generated at the ball-race interface and the ball-cage interface. Therefore, to understand the effect of speed and surface roughness on the total friction torque, it's necessary to look at how ball-race friction torque and ball-cage friction torque change with these parameters. Fig. 8 comprises three subplots, each analyzing torque as a function of speed for two surface roughness conditions i.e. Sq=1μ m and Sq=0.1μ m. From Fig. 8(a), it can be observed that the ball-race torque remains almost constant for Sq=1μ m, with a value of around 17 Nmm , while for Sq=0.1μ m, it shows a slight decrease as speed increases, starting at approximately 8 Nmm at 100 RPM and dropping to about 7 Nmm at 500 RPM. Fig. 8(b) shows that the ball-cage friction torque increases with speed. For Sq=1μ m, the ball-cage torque starts at around 0.6 Nmm at 100 RPM and rises steeply to 8.2 Nmm at 500 RPM . Similarly, for Sq=0.1μ m, the ballcage torque grows steadily from about 0.5 Nmm to 6 Nmm over the same speed range, although the values remain lower than those for the rougher surface. Now, Fig. 8(c) depicts the variation of the total friction torque, which is the combined effect of the ball-race and ball-cage torques. For Sq=1μ m, the total torque increases gradually from 17.89 Nmm at 100 RPM to 25.09 Nmm at 500 RPM . For Sq =0.1μ m, the total moment starts at around 8.67 Nmm at 100 RPM and increases steadily to approximately 12.90 Nmm at 500 RPM . Overall, the figure highlights the significant influence of surface roughness on torque. Bearings with higher surface roughness ( Sq=1μ m ) exhibit consistently higher torque values across all speed ranges. Additionally, the ball-cage torque contributes more
significantly to the total torque at higher speeds, especially for rougher surfaces for the specified range of speeds.

[picture]

Fig. 8 Friction moment at (a) ball-race contact, (b) ball-cage contact, and (c) ball bearing

Fig. 8. Friction moment at (a) ball-race contact, (b) ball-cage contact, and (c) ball bearing

Variation of ball-cage contact force. Fig. 9 depicts the relationship between rotational speed and the maximum contact force between ball and cage for surface roughness of Sq=1μ m and Sq=0.1μ m. The force is observed to increase as the rotational speed rises, regardless of the surface roughness. For the rougher surface ( Sq=1μ m ), the maximum force is consistently higher than for the smoother surface (Sq=0.1μ m) at all speeds. At 100 RPM , the forces are relatively low, measuring 2.71 N for Sq=1μ m and 2.08 N for Sq=0.1μ m. As the speed increases to 500 RPM , the forces rise significantly to 27.95 N and 12.88 N for Sq=1μ m and Sq=0.1μ m, respectively. This pattern is evident at all speeds, demonstrating that both increased speeds and surface roughness contribute to increased contact forces. Because friction moment is directly influenced by contact force, the higher contact force results in an increase in friction moment at the ball-cage contact.

[picture]

Fig. 9 Change in ball-cage contact force

Fig. 9. Change in ball-cage contact force

Summary

The original version of this paper is available on https://www.scientific.net/EI.19.115.pdf

The presented analysis highlights the significant effect of surface roughness and speed on the friction torque of a 6205 ball bearing. Bearings with higher surface roughness ( Sq=1μ m ) consistently exhibit greater friction torque than those with smoother surfaces ( Sq=0.1μ m ) across all speeds. Friction moment increases with rotational speed for both surface roughness, with rougher surfaces amplifying frictional losses more significantly. The ball-cage contact contributes substantially to total torque at higher speeds, especially for rougher surfaces, due to the increased contact forces. These findings emphasize the importance of smoother surfaces in minimizing friction torque and reducing frictional losses, particularly at higher rotational speeds.

References

The original version of this paper is available on https://www.scientific.net/EI.19.115.pdf

C. Jin, B. Wu, Y. Hu, Heat generation modeling of ball bearing based on internal load distribution, Tribol. Int. 45 (2012) 8-15.

R.D. Mindlin, Compliance of elastic bodies in contact, J. Appl. Mech. (1949) 259-268.

M.J. Todd, K.L. Johnson, A model for coulomb torque hysteresis in ball bearings, Int. J. Mech. Sci. 29 (1987) 339-354.

S. Jiang, X. Chen, J. Gu, X. Shen, Friction moment analysis of space gyroscope bearing with ribbon cage under ultra-low oscillatory motion, Chin. J. Aeronaut. 27 (2014) 1301-1311.

C.G. Zhou, L.D. Wang, J.W. Shen, C.H. Chen, Y. Ou, H.T. Feng, Wear characterization of raceway surface profiles of ball screws, J. Tribol. 144 (2022) 111701.

B. Bachchhav, H. Bagchi, Effect of surface roughness on friction and lubrication regimes, Mater. Today Proc. 38 (2021) 169-173.

X. Li, Y. Liu, J. Huang, D. Sang, K. Yang, J. Ling, Influence of surface texture on pocket pairs lubrication performance of cylindrical roller bearings, Ind. Lubr. Tribol. 76 (2024) 1085-1097.

B. Gouda, N. Tandon, R.K. Pandey, C.K. Babu, Effects of positioning of inner race microtextures on the tribodynamic performances of radial ball bearings, Mech. Syst. Signal Process 223 (2025) 111908.

C.F. Han, H.Y. Chu, R.Y. Luo, N.T. Liao, C.C. Wei, G.L. Chen, Effects of groove factor and surface roughness of raceway in ball-bearing-like specimens on tribological behavior and the onsets of two instabilities of dry contacts, Wear 406 (2018) 126-139.

H. Kumar, V. Gupta, V. Bharath, M. Tiwari, S.K. Paul, L. Agrawal, A.P. Singh, A. Jain, Effect of Surface Roughness on the Friction Moment in a Lubricated Deep Groove Ball Bearing, Lubricant 12 (2024) 443.

K.L. Johnson, Contact mechanics, Cambridge University Press, UK, 1987.

K.L. Johnson, J.A. Greenwood, S.Y. Poon, A simple theory of asperity contact in elastohydrodynamic lubrication, Wear 19 (1972) 91-108.

Y. Zhao, D.M. Maietta, L. Chang, An asperity microcontact model incorporating the transition from elastic deformation to fully plastic flow, J. Trib. 122 (2000) 86-93.

G. Nijenbanning, C.H. Venner, H. Moes, Film thickness in elastohydrodynamically lubricated elliptic contacts, Wear 176 (1994) 217-229.

This ePub file was created using machine-generated methods and automated software to convert the source text into an ePub format and may contain formatting inconsistencies from the original version. Formatting may vary depending on the device used.

The original version of this eBook is available on https://www.scientific.net/book/engineering-innovations-vol-19/978-3-0364-1217-7

Engineering Innovations, ISSN: 2813-1002, Vol. 19, pp 125-130

doi: 10.4028/p-8H6dI4

© 2026 The Author(s). Published by Trans Tech Publications Ltd, Switzerland.

Submitted:
2025-07-29

Revised:
2026-02-10

Accepted:
2026-02-28

Online:
2026-06-11

A Study on the Effects of Cam Eccentricity and Cylinder Phase Angle on the Output Performance of a Two Cylinder Stirling Engine

The original version of this paper is available on https://www.scientific.net/EI.19.125.pdf

Chin-Kuei Lin 1,a, Shie-Chen Yang 2,b*, Wen-KO Liang 3,c and Yi-Xiang Wang 4,d

1−4 Chienkuo Technology University, No. 1, Jieshou North Road, Changhua City 500,Taiwan Changhua

a  kuei@ctu.edu.tw, b  scyang@ctu.edu.tw, c beiy19660@gmail.com, d  a0981938225@gmail.com 

Keywords: two cylinder stirling engine, cam, phase angle, power consumption

Abstract

This study experimentally investigated the effects of cam eccentric hole position and cylinder phase angle on the rotational speed and output power of a twin-cylinder Stirling engine. Experimental data were analyzed to identify the optimal combination of eccentric hole position and cylinder phase angle. The eccentric cam influences the piston's stroke and movement speed, which directly affect the engine's output power. Additionally, the cylinder phase angle impacts the continuity and stability of engine operation. Experimental results indicate that a cam eccentric distance of 5.5 mm leads to imbalanced piston motion and increased power consumption. In contrast, an eccentric distance of 5 mm combined with a cylinder phase angle of 80 degrees yields the lowest cam power consumption, thereby significantly enhancing the overall output efficiency of the Stirling engine.

Introduction

The original version of this paper is available on https://www.scientific.net/EI.19.125.pdf

The Stirling engine is an external combustion engine that generates mechanical energy by heating gas with an external heat source and using the gas's thermal expansion and cooling [1]. The α-type Stirling engine has two cylinders, also known as the two cylinder Stirling engine (the hot-end cylinder and the cold-end cylinder). The hot-end cylinder receives the external heat source, causing the gas inside to expand, pushing the piston outward. The cold-end cylinder is connected to the cooling system, allowing the gas to cool and the piston to move inward. The reciprocating motion of the piston is converted into rotary motion through a connected cam mechanism, which then drives external mechanical devices. The cam mechanism in the two cylinder Stirling engine mainly serves to convert rotary motion into linear motion, assisting the Stirling engine in completing its thermodynamic cycle. The shape and profile of the cam determine the movements of the piston, which in turn affects the engine's performance. In a two cylinder Stirling engine, the cam mechanism needs to coordinate the movement of the two cylinders, ensuring that the compression and expansion processes occur at the proper times. The position of the eccentric hole of the cam affects the engine's power output and rotational velocity. A larger eccentric distance results in a longer piston stroke, which increases the expansion and compression of the gas, thus improving the engine's power output. However, if the eccentric distance is too large, it could lead to imbalanced piston movement, increasing mechanical wear and vibration. The phase angle refers to the angular difference in the movement of the pistons in the two cylinders relative to each other, which significantly affects the performance, balance, and stability of engine. Proper phase angle ensures that the movements of the two cylinders complement each other to a certain extent, improving the overall gas exchange efficiency. On the contrary, an improper phase angle may cause gas loss or incomplete gas exchange, reducing thermal efficiency. In a two cylinder Stirling engine, the position of the cam's eccentric hole and the phase angle of the cylinders are crucial design parameters. The eccentric distance of the cam affects the piston stroke, gas circulation efficiency, and mechanical wear, while the phase angle influences gas exchange efficiency, engine balance, and power output, ultimately improving the performance and efficiency of the engine. In this study, experimental method was used to explore the effects of the cam's eccentric hole position and the cylinder phase angle on the output performance of a two cylinder Stirling engine. Experimental data are analyzed to determine the optimal eccentric hole position and cylinder phase angle.

Model of Two Cylinder Stirling Engine

The original version of this paper is available on https://www.scientific.net/EI.19.125.pdf

Design of two cylinder stirling engine

The original version of this paper is available on https://www.scientific.net/EI.19.125.pdf

The operating principle of Stirling cycle involves four steps: isothermal expansion, constantvolume cooling, isothermal compression, and constant-volume heating. In a two cylinder stirling engine, the two cylinders are typically arranged at right angles or in an opposing configuration, with the two pistons operating in coordination with a phase difference. When the gas in one cylinder expands, the gas in the other cylinder is compressed. Figure 1 shows the model design of two cylinder Stirling engine used in this study.[2,3] The physical design is shown in Figure 2.

[picture]

Fig. 1. Model design of the two cylinder Stirling engine.

Fig. 1. Model design of the two cylinder Stirling engine.

[picture]

Fig. 2 Physical design of the two cylinder Stirling engine.

Fig. 2. Physical design of the two cylinder Stirling engine.

Design of Planar Eccentric Cam

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The phase angle position of the planar eccentric cam in a two-cylinder Stirling engine influences the relative reciprocating linear motion of the pistons in both cylinders. This coordination enhances the engine's operational efficiency and stability, facilitating the effective conversion of thermal energy into mechanical output. The geometric design of the planar eccentric cam determines the amplitude of the pistons' reciprocating motion. Typically, the eccentric cam is a circular disc, where the eccentric distance enables the desired reciprocating movement of the driven components, as illustrated in Figure 3.
When the cam rotates at a constant speed, it converts rotational motion into linear motion of the driven components, either in an up-and-down or back-and-forth direction. The total rise distance corresponds to twice the eccentric distance of the cam [1, 4]. In a two cylinder Stirling engine, the geometric design of the planar eccentric cam determines both the amplitude of the pistons' reciprocating motion and their phase difference[5]. The geometric parameters include the eccentric distance (e), cam radius (R), and the radial displacement (D) of the driven components. As the eccentric cam rotates, the driven parts move in response to changes in the cam's rotational angle, generating a motion profile including displacement, velocity, and acceleration that can be expressed as:
Displacement Curve D(θ) :

D(θ)=R+e·cos(θ)=R+e·cos(ωt)(1)

Where θ is the cam's rotational angle and ω is the cam's angular velocity. When θ=0∘ and θ=360∘, the displacement reaches its maximum value of R+e. At θ=180∘, the displacement reaches its minimum value of R−e.

Velocity CurveV(θ):

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V(θ)=−e·ω·sin(θ)=−e·ω·sin(ωt)(2)

When θ=90∘ and θ=270∘, the velocity reaches its maximum and minimum values, respectively. At θ=0∘ and θ=180∘, the velocity is zero.

Acceleration Curvea(θ):

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a(θ)=−e·ω2·cos(θ)=−e·ω2·cos(ωt)(3)

When θ=00 and θ=1800, The acceleration reaches its maximum and minimum values. At θ=900 and θ=2700, the acceleration is zero.

[picture]

Fig. 3 Schematic diagram of planar eccentric cam.

Fig. 3. Schematic diagram of planar eccentric cam.

The eccentric cam of the two cylinder Stirling engine consists of the eccentric wheel and the large wheel, as shown in Figure 2. Both the eccentric distance and the radius of the eccentric cam influence the pistons' stroke, as well as the displacement, velocity, and acceleration, thereby affecting the engine's output power. The calculation of the engine's output power is based on the work done by the gas, with minimal energy loss in the cylinder leading to maximum power output[6]. The formulas for calculating the moment of inertia (I), rotational kinetic energy (K), and power consumed (P) by the cam are given as follows:[7]

I=12×m×γ2K=12×I×ω2P=ΔKt=K2−K1t

(4)(5)(6)

When the units of moment of inertia (I), rotational kinetic energy (K), and power consumed (P) are kg−m2, J, and N−m/s, respectively.

Design of Piston Phase Angle

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In a two-cylinder Stirling enginep[8], the pistons of the power cylinder and the exhaust cylinder are typically designed with a phase difference of 90 degrees or another specified angle. When one piston reaches the top (or bottom) of its stroke, the other piston is positioned at the midpoint of its stroke. This arrangement allows one cylinder to undergo the expansion (or compression) process while the other simultaneously undergoes compression (or expansion), thereby ensuring continuous and smooth engine operation. Generally, a 90-degree phase angle is preferred. However, to achieve higher power output or to accommodate greater mechanical stress, other phase angles ranging from 80 degrees to 180 degrees can also be considered.

Machining of Two Cylinder Stirling Engine

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The component parts of the two-cylinder Stirling engine include the cylinder (glass tube), heating tube (stainless steel), instrument chamber (aluminum), large wheel (aluminum), link (steel), eccentric wheel (aluminum), main bracket (aluminum), and connecting rod ring (stainless steel), etc. First, all components are designed using 3D Inventor CAD software. The parts are then fabricated through machining processes such as CNC milling, grinding, and press braking. Then the components are
assembled and adjusted for performance testing. Figure 4 shows the diagram of the completed machined parts, while Figure 5 presents the assembled engine. To accurately measure the temperature of the cylinder and the rotational speed of the wheel during the experimental process, a fixed measurement platform is constructed using aluminum alloy, with dimensions of 165 mm (length), 100 mm (width), and 150 mm (height). The physical setup of the experimental equipment and measurement platform is shown in Figure 6.

[picture]

Fig. 4 Diagram of the Completed Machined Parts.

Fig. 4. Diagram of the Completed Machined Parts.

[picture]

Fig. 5 Diagram of the Assembled Stirling Engine.

Fig. 5. Diagram of the Assembled Stirling Engine.

[picture]

Fig. 6 Diagram of experimental equipment and measurement platform.

Fig. 6. Diagram of experimental equipment and measurement platform.

Experimental Method and Equipments

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This study utilizes the cam eccentric hole distance and cylinder phase angle as design parameters to investigate their effects on the velocity and power output of a two cylinder Stirling engine. The eccentric hole distances examined are 4 mm,4.5 mm,5 mm, and 5.5 mm , while the phase angles considered are 80∘,85∘,90∘,95∘, and 100∘. It is hypothesized that an excessively large eccentric hole distance may result in unbalanced cylinder motion, thereby reducing thermal efficiency and decreasing power output. Conversely, moderate adjustments to the eccentric hole distance can enhance gas flow dynamics and improve power output. However, the eccentric hole distance should not be too small, as it may lead to mechanical interference.
First, the hot-end cylinder was heated using a gas torch for 45 seconds. A thermocouple, together with a temperature data recorder (model LRB4310), was used to measure the cylinder's temperature, which reached a maximum of 230.1∘C. After heating, the heat source was turned off, and the large eccentric wheel was manually rotated to utilize inertia for converting thermal energy into rotational power. Simultaneously, a non-contact tachometer (model DT2259) was used to measure the rotational speed. The highest and lowest rotational speeds of the eccentric wheel were recorded. Each experimental trial was repeated three times, and the average values were calculated for analysis. To
enable fast and accurate setting of the cylinder phase angle, a punch-forming machine is used to machine a phase-angle fixed wheel assembly rod. One end of the rod is fixed and ground at 0∘, while the other end is ground according to the desired phase angle.

Results and Discussion

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This study investigates the effects of cam eccentric hole position and cylinder phase angle on the rotational speed and output power of a twin cylinder Stirling engine. The cam eccentric distances examined were 4 mm,4.5 mm,5 mm, and 5.5 mm , while the phase angles tested were 80∘,85∘,90∘, 95∘, and 100∘. The measured rotational speed and power consumption values are presented in Table 1. Corresponding power consumption values at different rotational speeds are provided in Table 2 and illustrated in Figure 7. The lowest recorded power consumption, 0.1973 N· m/s, occurred with an eccentric distance of 4 mm and a phase angle of 100∘. In comparison, the configuration with an eccentric distance of 5 mm and a phase angle of 80∘ resulted in a power consumption of 0.517 N· m/s. However, as shown in Figure 7, the power consumption is consistently lower across all phase angles when the eccentric distance is set to 5 mm , indicating it as the most efficient setting overall.

Table 1 Values of rotational speed and power consumption

Speed rpm(rpm) /Power consumption(N-m/s) | eccentric distances(mm) 4.000 | 4.500 | 5.000 | 5.500
Speed (low) | 0.00 | 0.00 | 1672.33 | 7265.33
Seep (high) | 0.00 | 0.00 | 3576.33 | 23356.00
Power consumption | 0.00 | 0.00 | 0.52 | 30.74
Speed (low) | 1376.00 | 6539.67 | 1246.33 | 5104.67
Seep (high) | 6703.33 | 9096.67 | 5056.33 | 21550.67
Power consumption | 1.44 | 1.66 | 1.24 | 27.44
Speed (low) | 1432.67 | 2932.00 | 825.67 | 4993.33
Seep (high) | 6451.67 | 9758.67 | 4747.67 | 18393.33
Power consumption | 1.30 | 3.60 | 1.12 | 19.45
Speed (low) | 748.33 | 2447.00 | 725.00 | 4222.00
Seep (high) | 4049.00 | 14243.00 | 3988.33 | 20043.67
Power consumption | 0.52 | 8.19 | 0.80 | 23.82
Speed (low) | 980.33 | 1387.00 | 577.67 | 2346.67
Seep (high) | 2525.33 | 9792.33 | 3489.00 | 15948.67
Power consumption | 0.20 | 3.90 | 0.61 | 15.45

Table 2 Rotational speed and power consumption (average) with phase angle and eccentric distance

eccentric distances(mm) | Phase angle 80⁰ | 85⁰ | 90⁰ | 95⁰ | 100⁰
4 |  | 1.43 | 1.30 | 0.52 | 0.20
4.5 |  | 1.66 | 3.60 | 8.19 | 3.91
5 | 0.52 | 1.24 | 1.12 | 0.80 | 0.61
5.5 | 30.74 | 27.44 | 19.45 | 23.82 | 15.45

[picture]

Fig. 7 Relationship of power consumption and cam eccentric distances.

Fig. 7. Relationship of power consumption and cam eccentric distances.

References

The original version of this paper is available on https://www.scientific.net/EI.19.125.pdf

Definition and types of mechanical cam mechanisms on https://youtu.be/FbYEz0b8DPQ? feature=shared.

The kinematic characteristics of simple harmonic motion on

J.H. Zhou, Energy Star Stirling Engine, B.J of Physics of Chung-Ang University Physics Demonstration Laboratory Volume 30, Issue 4.(2008)

Institutional design and application, Unit three cam mechanism introduction on .

How the Stirling engine works on .

S.C. Liang, Small Stirling Engine Manufacture and Thermal Efficiency Analysis, master's Thesis, Department of Mechanical Engineering and Institute of Manufacturing Technology, Jianguo University of Science and Technology. (2014)

F.R. Li, An Experimental Study of Gamma Type Stirling Engine, master's Thesis, Department of Mechanical and Automation Engineering, I-Shou University.(2013)

P.H. Chen, Fabrication and analysis of -type Stirling engine, master's thesis, Institute of Mechanical Engineering, National Chung Hsing University.(2016)

This ePub file was created using machine-generated methods and automated software to convert the source text into an ePub format and may contain formatting inconsistencies from the original version. Formatting may vary depending on the device used.

The original version of this eBook is available on https://www.scientific.net/book/engineering-innovations-vol-19/978-3-0364-1217-7

Engineering Innovations, ISSN: 2813-1002, Vol. 19, pp 131-136

doi: 10.4028/p-Q7Sc7P

© 2026 The Author(s). Published by Trans Tech Publications Ltd, Switzerland.

Submitted:
2025-07-03

Revised:
2026-02-24

Accepted:
2026-02-28

Online:
2026-06-11

Auxiliary Lubrication Effects of Polyolefin Resin and Porous Polymer Materials Used in the Linear Slide Rails

The original version of this paper is available on https://www.scientific.net/EI.19.131.pdf

Hsiang-Yu Wang 1,a, Yuh-Ping Chang 2, b*, Por-Cheng Huang 1,c, Chun-Yi Kuo 1, d and Jin-Chi Wang 2,e

1 Naval Shipbuilding Development Center, Taiwan Navy

2 Department of Mechanical Engineering, Kun Shan University

a  andysti486@gmail.com, b  ypc0318@mail.ksu.edu.tw, c  physichem35@gmail.com, da19872476@gmail.com, e  markfish@mail.ksu.edu.tw 

Keywords: Polyolefin resin, polymer, auxiliary lubrication, linear guides

Abstract

With the rapid development of the precision machine tool industry, the requirements for the performance of the linear guides are becoming increasingly stringent. To transmit greater power and be used in the heavy-load environments, the contact surface between the balls and the rails must withstand great stress, and it is easy to cause severe wear of the material. Therefore, the applications of auxiliary lubrication technology to improve the overall lubrication properties has become the focus of the design of linear slides used under high load conditions. The effectiveness of polyolefin resin and porous polymers on the auxiliary lubrication is studied in this paper. The experimental results show that the porous polymer auxiliary lubrication device not only has a shorter running-in period and lower friction results, but also has acceptable anti-wear properties.

Introduction

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Since the load on the drive elements under extreme pressure conditions is much greater than that in traditional machine tools, the contact stress between the ball and the track will increase significantly, causing the rolling surface and the rolling element to be continuously subjected to repeated contact stress. When rolling contact fatigue reaches its limit, the rolling surface will produce many small cracks due to stress fatigue, and even fatigue cracks will occur through the expansion of small surface damage. This condition is called micro-pitting. The fatigue crack of micro-pitting is at a shallow angle to the surface until a small section of material separates to form a micro pit [1-2]. In the studies of the effect of surface roughness on pitting formation, it has been determined that the micropitting only occurs when the specific film thickness is low enough to facilitate contact between the asperity ends [2-8]. Hence, the micropitting can be considered as a progressive fatigue wear process. Lainé [9] showed that lubricant additives can affect the extent of micropitting by inhibiting the wear process, leading to an increase in the number of asperity stress cycles, and by changing the degree of contact friction, thereby changing the tensile stress and thus affecting crack initiation and propagation. Therefore, for linear guide rails, this experiment selected a porous polymer auxiliary lubrication device and used it with a lubricant containing extreme pressure additives for research. At the same time, it was compared with the auxiliary lubrication device commonly used in mass production, in order to improve the lubrication performance of linear guide rails under heavy loads.

Experimental Apparatus and Procedures

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Apparatus. The reciprocating friction testing machine and the measurement systems are used for the experiments as shown in Figure 1. The ball specimen is driven by a crank-slider mechanism. Moreover, the length of the crank is adjusted to as the stroke. The normal load is relative to the ball through the block specimen and disposed along the level rule. The ball specimen performs reciprocating friction motion on the block specimen, so that the load cell transmits the voltage
signals of the push and pull forces. Finally, the voltage signals of friction coefficient are input to the data acquisition system and fed to the personal computer for data analysis.

[picture]

Fig. 1 The schematic diagram of the friction tester with measuring systems

Fig. 1. The schematic diagram of the friction tester with measuring systems

Experimental specimens. The ball specimen is made of high-carbon chromium alloy steel (HRC 65,ϕ6.35 mm ). The contents of the ball specimen are given in Table 1. The block specimen is a common material of chrome-vanadium alloy steel for the slider. The contents of the block specimen are shown in Table 2. Schematic diagram and photo of the ball and block specimens are shown in Figure 2 and Figure 3, respectively. The materials of the auxiliary lubrication device are polyolefin resin and porous polymer, respectively. Among them, polyolefin resin is a commonly used material for mass production of auxiliary lubrication devices for linear guides. Before the experiment, it is immersed in VG68 oil with extreme pressure resistance until it reaches oil absorption saturation.

Table 1 Compositions of the high-carbon chromium alloy steel ball ( wt% )

C | Cr | Mn | Si | Ni | Cu
0.95-1.05 | 1.40-1.65 | 0.25-0.45 | 0.15-0.35 | ≤0.30 | ≤0.25

Fig. 2 Schematic diagram of the ball and block specimens

Table 2 Compositions of the chromium vanadium alloy steel (wt%)

C | Cr | Mn | V | Si | Ni | Cu
0.47~0.55 | 0.90~1.20 | 0.70~1.10 | 0.10~0.25 | ≤0.40 | ≤0.35 | ≤0.25

[picture]

Fig. 3 Photo of the ball and block specimens

Fig. 3. Photo of the ball and block specimens

Procedures. The experiments are conducted under the auxiliary lubrications of the polyolefin resin and the porous polymer, respectively. Table 3 lists the experimental conditions and parameters. After the tests, the captured friction data was calculated through computer software and converted into the physical quantity of dynamic friction coefficient. The oil was definitely cleaned up. Then, the wear depth was measured by a surface roughness meter. Moreover, the worn surfaces of the ball and the block specimens were observed using an optical microscope.

Table 3 Experimental conditions

Ball specimen | High-carbon chromium alloy steel ball (HRC 65, φ 6.35 mm)
Auxiliary lubrication device | Polyolefin resin / Porous polymer
Block specimen | Chromium vanadium alloy steel
Load | 100 N
Speed | 150 cpm / 450 cpm (cycles per minute)
Stroke | 12 mm
Friction time | 20 min 30 sec

Results and Discussions

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Dynamic variations of friction coefficient. Figure 4 shows that the initial running-in distances of polyolefin resin and porous polymer are 0.32 m and 0.19 m at 100 N and 150 cpm . Moreover, the average friction coefficients after the running-in period are approximately 0.33 and 0.26 , respectively. Figure 5 shows that the initial running-in distances of polyolefin resin and porous polymer are 0.22 m and 0.13 m at 100 N and 450 cpm . Moreover, the average friction coefficients after the running-in period are approximately 0.47 and 0.36 , respectively. As shown in Figure 4 and Figure 5, whether at a low speed of 150 cpm or a high speed of 450 cpm , the running-in period of the porous polymer is shorter than that of the polyolefin resin. Moreover, the friction coefficient of the porous polymer is also smaller than that of the polyolefin resin.

[picture]

Fig. 4 Dynamic friction coefficient at 100 N and 150 cpm : (a) polyolefin resin, (b) porous polymer

Fig. 4. Dynamic friction coefficient at 100 N and 150 cpm : (a) polyolefin resin, (b) porous polymer

[picture]

Fig. 5 Dynamic friction coefficient at 100 N and 450 cpm : (a) polyolefin resin, (b) porous polymer

Fig. 5. Dynamic friction coefficient at 100 N and 450 cpm : (a) polyolefin resin, (b) porous polymer

Optical microscope of the worn surfaces. Figure 6 shows that the wear width of the ball specimens coupled with polyolefin resin and porous polymer are 430μ m and 350μ m at 100 N and 150 cpm . Figure 7 shows that the wear width of the ball specimens coupled with polyolefin resin and porous polymer are 532μ m and 435μ m at 100 N and 450 cpm . As shown in Figure 6 and Figure 7, the anti-wear capability of porous polymer is better than that of polyolefin resin both at a low speed of 150 cpm and a high speed of 450 cpm .

[picture]

Fig. 6 Worn surface of the balls at 100 N and 150 cpm : (a) polyolefin resin, (b) porous polymer

Fig. 6. Worn surface of the balls at 100 N and 150 cpm : (a) polyolefin resin, (b) porous polymer

[picture]

Fig. 7 Worn surface of the balls at 100 N and 450 cpm : (a) polyolefin resin, (b) porous polymer

Fig. 7. Worn surface of the balls at 100 N and 450 cpm : (a) polyolefin resin, (b) porous polymer

The analysis of wear depth. Figure 8 shows that the block specimens of polyolefin resin and porous polymer show mild wear (the wear depth is about 0.4μ m ) and nearly original surface states respectively at 100 N and 150 cpm . Figure 9 shows that the block specimens of polyolefin resin and porous polymer show severe wear (the wear depth is about 1μ m ) and mild wear (the wear depth is about 0.5μ m ) respectively at 100 N and 450 cpm . The anti-wear capability of porous polymer is better than that of polyolefin resin both at a low speed of 150 cpm and a high speed of 450 cpm as shown in Figure 8 and Figure 9.

[picture]

Fig. 8 Wear depth of the blocks at 100 N and 150 cpm : (a) polyolefin resin, (b) porous polymer

Fig. 8. Wear depth of the blocks at 100 N and 150 cpm : (a) polyolefin resin, (b) porous polymer

[picture]

Fig. 9 Wear depth of the blocks at 100 N and 450 cpm : (a) polyolefin resin, (b) porous polymer

Fig. 9. Wear depth of the blocks at 100 N and 450 cpm : (a) polyolefin resin, (b) porous polymer

Conclusions

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The wear performance of polyolefin resin and porous polymer for auxiliary lubrication of linear guides under extreme pressure conditions are investigated in this paper. Based on the above experimental results, the following conclusions are drawn: (1) The larger the friction coefficient, the more severe the wear. The relevant experimental data all have corresponding results. (2) Whether at a low speed of 150 cpm or a high speed of 450 cpm , the running-in period of porous polymer is shorter than that of polyolefin resin, and the friction coefficient is also smaller than that of polyolefin resin. (3) The wear resistance of porous polymer is better than that of polyolefin resin whether at a low speed of 150 cpm or a high speed of 450 cpm .

Acknowledgements

The original version of this paper is available on https://www.scientific.net/EI.19.131.pdf

The authors would like to express their appreciation to the Ministry of Science and Technology in Taiwan, R. O. C. for their financial support under grant number NSTC 113-2221-E-168-001.

References

The original version of this paper is available on https://www.scientific.net/EI.19.131.pdf

A.V. Olver, The mechanism of rolling contact fatigue: an update, Proc. Inst. Mech. Eng. Part J J. Eng. Tribol., 219(5) (2005) 313-330.

E. Lainé, A.V. Olver, T.A. Beveridge, Effect of lubricants on micropitting and wear, Tribol. Int., 41 (2008) 1049-1055.

A. Oila, S.J. Bull, Assessment of the factors influencing micropitting in rolling/sliding contacts, Wear, 258(10) (2005) 1510-1524.

H.P. Evans, R.W. Snidle, K.J. Sharif, B.A. Shaw, J. Zhang, Analysis of microelastohydrodynamic lubrication and prediction of surface fatigue damage in micropitting tests on helical gears, J. Tribol., 135(1) (2012) 011501.

A. Clarke, I. J. J. Weeks, R. W. Snidle, H. P. Evans, Running-in and micropitting behaviour of steel surfaces under mixed lubrication conditions, Tribol. Int., 101 (2016) 59-68.

H. Cen, A. Morina, A. Neville, Effect of slide to roll ratio on the micropitting behaviour in rolling-sliding contacts lubricated with ZDDP-containing lubricants, Tribol. Int., 122 (2018) 210-217.

A. Kadiric, P. Rycerz, Influence of contact conditions on the onset of micropitting in rollingsliding contacts pertinent to gear applications, In: AGMA Fall Technical Meeting, 16FTM21 (2016).

A. Vrcek, T. Hultqvist, Y. Baubet, M. Björling, P. Marklund, R. Larsson, Micro-pitting and wear assessment of engine oils operating under boundary lubrication conditions, Tribol. Int., 129 (2019) 338-346.

E. Lainé, A. V. Olver, M. F. Lekstrom, B. A. Shollock, T. A. Beveridge, D. Y. Hua, The effect of a friction modifier additive on micropitting, Tribol. Trans., 52(4) (2009) 526-533.

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Keywords Index

A

Acoustic Emission Laser Shock Peening with Acoustic Emission In-Situ Monitoring for Quality Control

Ai Tools Al Tools for Plasma Diagnostics by X-Ray Imaging and Spectroscopy in the PANDORA Project Frame

Auxiliary Lubrication Auxiliary Lubrication Effects of Polyolefin Resin and Porous Polymer Materials Used in the Linear Slide Rails

B

Ball Bearings Effect of Surface Roughness on the Friction Moment in a Ball Bearing

Bayes Inference Bayesian Strategy for the Volume Accuracy of Ink Droplets Continuous Estimation during Printing Based on Prior Knowledge with Earth Mover's Distance

Beam Dynamics Beamlines of the EuPRAXIA@SPARC_LAB X-Ray FEL Facility

C

Cam A Study on the Effects of Cam Eccentricity and Cylinder Phase Angle on the Output Performance of a Two Cylinder Stirling Engine

Clockwise Rotation Numerical Study of Four Hydrokinetic Savonius Turbines in an In-Line Square Co-Rotating Configuration in the Cooling Water Channel

Coefficient Of Moment Numerical Study of Four Hydrokinetic Savonius Turbines in a Staggered Co-Rotating Configuration in the Cooling Water Channel

Coefficient Of Power Numerical Study of Four Hydrokinetic Savonius Turbines in a Staggered Co-Rotating Configuration in the Cooling Water Channel

Composite Films Excellent UV-Shielding Performance of PVA Film Using Methanol Extracted Longevity Spinach Leaves (Gynura Procumbens)

Computational Fluid Dynamics (cfd) Improved Performance Assessment of Photovoltaic Systems Using Paraffin-Based Phase Change Materials and Innovative Container Designs

Continuous Evaluation Bayesian Strategy for the Volume Accuracy of Ink Droplets Continuous Estimation during Printing Based on Prior Knowledge with Earth Mover's Distance

F

Flir Images Laser Shock Peening with Acoustic Emission In-Situ Monitoring for Quality Control

Free-electron Laser Beamlines of the EuPRAXIA@SPARC_LAB X-Ray FEL Facility

Friction Torque Effect of Surface Roughness on the Friction Moment in a Ball Bearing

G

Graph Neural Network The Printing of Highly Uniform Display Device by Graph Convolutional Neural Network-Based Manufacturing

H

High Uniformity Manufacturing The Printing of Highly Uniform Display Device by Graph Convolutional Neural Network-Based Manufacturing

Hydrokinetic Savonius Turbine Numerical Study of Four Hydrokinetic Savonius Turbines in a Staggered Co-Rotating Configuration in the Cooling Water Channel

I

Ijp Oled Bayesian Strategy for the Volume Accuracy of Ink Droplets Continuous Estimation during Printing Based on Prior Knowledge with Earth Mover's Distance

In-line Square Numerical Study of Four Hydrokinetic Savonius Turbines in an In-Line Square Co-Rotating Configuration in the Cooling Water Channel

Integer Planning The Printing of Highly Uniform Display Device by Graph Convolutional Neural Network-Based Manufacturing

L

Laser Shock Peening Laser Shock Peening with Acoustic Emission In-Situ Monitoring for Quality Control

Linear Guides Auxiliary Lubrication Effects of Polyolefin Resin and Porous Polymer Materials Used in the Linear Slide Rails

Longevity Spinach Leaf Extract Excellent UV-Shielding Performance of PVA Film Using Methanol Extracted Longevity Spinach Leaves (Gynura Procumbens)

Lubrication Effect of Surface Roughness on the Friction Moment in a Ball Bearing

M

Magnet Undulators Beamlines of the EuPRAXIA@SPARC_LAB X-Ray FEL Facility

Methanol Extraction Excellent UV-Shielding Performance of PVA Film Using Methanol Extracted Longevity Spinach Leaves (Gynura Procumbens)

N

Neural Networks Al Tools for Plasma Diagnostics by X-Ray Imaging and Spectroscopy in the PANDORA Project Frame

Numerical Method Numerical Study of Four Hydrokinetic Savonius Turbines in an In-Line Square Co-Rotating Configuration in the Cooling Water Channel

Numerical Study Numerical Study of Four Hydrokinetic Savonius Turbines in a Staggered Co-Rotating Configuration in the Cooling Water Channel

O

Optical Properties Excellent UV-Shielding Performance of PVA Film Using Methanol Extracted Longevity Spinach Leaves (Gynura Procumbens)

Organic Light-emitting Diode The Printing of Highly Uniform Display Device by Graph Convolutional Neural Network-Based Manufacturing

P

Phase Angle A Study on the Effects of Cam Eccentricity and Cylinder Phase Angle on the Output Performance of a Two Cylinder Stirling Engine

Phase Change Material (pcm) Improved Performance Assessment of Photovoltaic Systems Using Paraffin-Based Phase Change Materials and Innovative Container Designs

Photosensitive Packaging Excellent UV-Shielding Performance of PVA Film Using Methanol Extracted Longevity Spinach Leaves (Gynura Procumbens)

Photovoltaic Thermal (pvt) Improved Performance Assessment of Photovoltaic Systems Using Paraffin-Based Phase Change Materials and Innovative Container Designs

Plasma Diagnostic Al Tools for Plasma Diagnostics by X-Ray Imaging and Spectroscopy in the PANDORA Project Frame

Polymer Auxiliary Lubrication Effects of Polyolefin Resin and Porous Polymer Materials Used in the Linear Slide Rails

Polyolefin Resin Auxiliary Lubrication Effects of Polyolefin Resin and Porous Polymer Materials Used in the Linear Slide Rails

Power Consumption A Study on the Effects of Cam Eccentricity and Cylinder Phase Angle on the Output Performance of a Two Cylinder Stirling Engine

Print Manufacturing The Printing of Highly Uniform Display Device by Graph Convolutional Neural Network-Based Manufacturing

Progressive Cavity Pump; Finite Element Analysis; Hyperelastic Modeling; Ogden Model; Rotor-stator Interference; Defect-induced Stress Concentration Numerical Analysis of Defect-Induced Stress and Deformation in a Progressive Cavity Pump Stator Elastomer

R

Rough Surface Effect of Surface Roughness on the Friction Moment in a Ball Bearing

S

Savonius Turbine Numerical Study of Four Hydrokinetic Savonius Turbines in an In-Line Square Co-Rotating Configuration in the Cooling Water Channel

Staggered Configuration Numerical Study of Four Hydrokinetic Savonius Turbines in a Staggered Co-Rotating Configuration in the Cooling Water Channel

T

Time Series Laser Shock Peening with Acoustic Emission In-Situ Monitoring for Quality Control

Time-frequency Laser Shock Peening with Acoustic Emission In-Situ Monitoring for Quality Control

Two Cylinder Stirling Engine A Study on the Effects of Cam Eccentricity and Cylinder Phase Angle on the Output Performance of a Two Cylinder Stirling Engine

V

Volume Accuracy Bayesian Strategy for the Volume Accuracy of Ink Droplets Continuous Estimation during Printing Based on Prior Knowledge with Earth Mover's Distance

Voxes; Xes; Xas; Von-hamos Recent Developments of the VOXES Von Hamos X-Ray Spectrometer for Laboratory XES and XAS Studies

X

X-ray Imaging Al Tools for Plasma Diagnostics by X-Ray Imaging and Spectroscopy in the PANDORA Project Frame

X-ray Spectroscopy Al Tools for Plasma Diagnostics by X-Ray Imaging and Spectroscopy in the PANDORA Project Frame

Authors Index

A

Abral, H. Excellent UV-Shielding Performance of PVA Film Using Methanol Extracted Longevity Spinach Leaves (Gynura Procumbens)

Alfaiz, N. Improved Performance Assessment of Photovoltaic Systems Using Paraffin-Based Phase Change Materials and Innovative Container Designs

Arifin, Z. Improved Performance Assessment of Photovoltaic Systems Using Paraffin-Based Phase Change Materials and Innovative Container Designs

B

Biri, S. Al Tools for Plasma Diagnostics by X-Ray Imaging and Spectroscopy in the PANDORA Project Frame

C

Chang, Y. Auxiliary Lubrication Effects of Polyolefin Resin and Porous Polymer Materials Used in the Linear Slide Rails

Chen, J. Bayesian Strategy for the Volume Accuracy of Ink Droplets Continuous Estimation during Printing Based on Prior Knowledge with Earth Mover's Distance; The Printing of Highly Uniform Display Device by Graph Convolutional Neural Network-Based Manufacturing

F

Finocchiaro, G. Al Tools for Plasma Diagnostics by X-Ray Imaging and Spectroscopy in the PANDORA Project Frame

G

Griffin, J. Laser Shock Peening with Acoustic Emission In-Situ Monitoring for Quality Control

H

Huang, P. Auxiliary Lubrication Effects of Polyolefin Resin and Porous Polymer Materials Used in the Linear Slide Rails

J

Juwana, W. Improved Performance Assessment of Photovoltaic Systems Using Paraffin-Based Phase Change Materials and Innovative Container Designs

K

Kong, D. Bayesian Strategy for the Volume Accuracy of Ink Droplets Continuous Estimation during Printing Based on Prior Knowledge with Earth Mover's Distance

Kumar, H. Effect of Surface Roughness on the Friction Moment in a Ball Bearing

Kuo, C. Auxiliary Lubrication Effects of Polyolefin Resin and Porous Polymer Materials Used in the Linear Slide Rails

L

Liang, W. A Study on the Effects of Cam Eccentricity and Cylinder Phase Angle on the Output Performance of a Two Cylinder Stirling Engine

Lin, C. A Study on the Effects of Cam Eccentricity and Cylinder Phase Angle on the Output Performance of a Two Cylinder Stirling Engine

M

Mahardika, M. Excellent UV-Shielding Performance of PVA Film Using Methanol Extracted Longevity Spinach Leaves (Gynura Procumbens)

Manti, S. Recent Developments of the VOXES Von Hamos X-Ray Spectrometer for Laboratory XES and XAS Studies

Mascali, D. Al Tools for Plasma Diagnostics by X-Ray Imaging and Spectroscopy in the PANDORA Project Frame

Meilianto, D. Numerical Analysis of Defect-Induced Stress and Deformation in a Progressive Cavity Pump Stator Elastomer

Mishra, B. Al Tools for Plasma Diagnostics by X-Ray Imaging and Spectroscopy in the PANDORA Project Frame

N

Naselli, E. Al Tools for Plasma Diagnostics by X-Ray Imaging and Spectroscopy in the PANDORA Project Frame

Nguyen, F. Beamlines of the EuPRAXIA@SPARC_LAB X-Ray FEL Facility

P

Peri, B. Al Tools for Plasma Diagnostics by X-Ray Imaging and Spectroscopy in the PANDORA Project Frame

Pidatella, A. Al Tools for Plasma Diagnostics by X-Ray Imaging and Spectroscopy in the PANDORA Project Frame

Prasetyo, S. Improved Performance Assessment of Photovoltaic Systems Using Paraffin-Based Phase Change Materials and Innovative Container Designs

Q

Qin, R. Laser Shock Peening with Acoustic Emission In-Situ Monitoring for Quality Control

R

Rachmanto, R. Improved Performance Assessment of Photovoltaic Systems Using Paraffin-Based Phase Change Materials and Innovative Container Designs

Rosli, M. Improved Performance Assessment of Photovoltaic Systems Using Paraffin-Based Phase Change Materials and Innovative Container Designs

Rusli, M. Excellent UV-Shielding Performance of PVA Film Using Methanol Extracted Longevity Spinach Leaves (Gynura Procumbens)

Rácz, R. Al Tools for Plasma Diagnostics by X-Ray Imaging and Spectroscopy in the PANDORA Project Frame

S

Scordo, A. Recent Developments of the VOXES Von Hamos X-Ray Spectrometer for Laboratory XES and XAS Studies

Setiyanto, D. Numerical Study of Four Hydrokinetic Savonius Turbines in an In-Line Square Co-Rotating Configuration in the Cooling Water Channel

Shukla, P. Laser Shock Peening with Acoustic Emission In-Situ Monitoring for Quality Control

Smith, P. Laser Shock Peening with Acoustic Emission In-Situ Monitoring for Quality Control

Sriwijaya, R. Numerical Analysis of Defect-Induced Stress and Deformation in a Progressive Cavity Pump Stator Elastomer

T

Tiwari, M. Effect of Surface Roughness on the Friction Moment in a Ball Bearing

U

Ubaidillah, U. Improved Performance Assessment of Photovoltaic Systems Using Paraffin-Based Phase Change Materials and Innovative Container Designs

Utama, M. Numerical Study of Four Hydrokinetic Savonius Turbines in a Staggered Co-Rotating Configuration in the Cooling Water Channel

W

Wang, H. Auxiliary Lubrication Effects of Polyolefin Resin and Porous Polymer Materials Used in the Linear Slide Rails

Wang, J. Auxiliary Lubrication Effects of Polyolefin Resin and Porous Polymer Materials Used in the Linear Slide Rails

Wang, Y. A Study on the Effects of Cam Eccentricity and Cylinder Phase Angle on the Output Performance of a Two Cylinder Stirling Engine

Widhiyanuriyawan, D. Improved Performance Assessment of Photovoltaic Systems Using Paraffin-Based Phase Change Materials and Innovative Container Designs

X

Xiong, J. Bayesian Strategy for the Volume Accuracy of Ink Droplets Continuous Estimation during Printing Based on Prior Knowledge with Earth Mover's Distance; The Printing of Highly Uniform Display Device by Graph Convolutional Neural Network-Based Manufacturing

Y

Yang, S. A Study on the Effects of Cam Eccentricity and Cylinder Phase Angle on the Output Performance of a Two Cylinder Stirling Engine

Yohana, E. Improved Performance Assessment of Photovoltaic Systems Using Paraffin-Based Phase Change Materials and Innovative Container Designs

Yuwono, T. Numerical Study of Four Hydrokinetic Savonius Turbines in a Staggered Co-Rotating Configuration in the Cooling Water Channel; Numerical Study of Four Hydrokinetic Savonius Turbines in an In-Line Square Co-Rotating Configuration in the Cooling Water Channel

Z

Zhao, Z. The Printing of Highly Uniform Display Device by Graph Convolutional Neural Network-Based Manufacturing

Zikri, Z. Excellent UV-Shielding Performance of PVA Film Using Methanol Extracted Longevity Spinach Leaves (Gynura Procumbens)