Engineering Innovations Volume 6

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Volume 6 of
Engineering Innovations
ISSN print 2813-0995
ISSN web 2813-1002

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Table of Contents

The Impact of Gas-Liquid Dispersed Flow on Heat Exchanger Performance with Improvement Using CuO Nanofluid

Mustafa M. Hathal, Basim O. Hasan, Hasan Sh. Majdi

Research Effectiveness of Regeneral Brake Energy on Toyota Prius Vehicles by Matlab/Simulink

Minh Le, Toan Nguyen Van

Low Latency Extended Dijkstra Algorithm with Multiple Linear Regression for Optimal Path Planning of Multiple AGVs Network

Leong Wen Chek

An Example of the Use of Photogrammetry for Archiving the Shapes of Natural Materials and Creations

József Udvaros, Ondrej Takáč

Smart Contracts in Construction from Management to Materials: Acting as a Bridge between the Two

Norbert Forman, József Udvaros

Keywords Index

Authors Index

The original version of this eBook is available on https://www.scientific.net/book/engineering-innovations-vol-6/978-3-0364-1337-2

Engineering Innovations, ISSN: 2813-1002, Vol. 6, pp 1-22

doi: 10.4028/p-gdl41l

© 2023 The Author(s). Published by Trans Tech Publications Ltd, Switzerland.

Submitted:
2023-02-05

Revised:
2023-02-25

Accepted:
2023-03-23

Online:
2023-06-21

The Impact of Gas-Liquid Dispersed Flow on Heat Exchanger Performance with Improvement Using CuO Nanofluid

The original version of this paper is available on https://www.scientific.net/EI.6.1.pdf

Mustafa M. Hathal , Basim O. Hasan , Hasan Sh. Majdi

The Industrial Development and Regulatory Directorate/ The Ministry of Industry and Minerals/ Baghdad- Iraq.

The Scientific Society for Energy Studies and Research/ Baghdad- Iraq.

Chemical Engineering and Petroleum Industry Department/ AI-Mustaqbal University College/ Hilla- Iraq.

Chemical Engineering Department/ Al-Nahrain University/ Baghdad- Iraq.

*Corresponding Email: mus.mhathal@gmail.com,

basimohasan13@gmail.com, chasanshker1@gmail.com

Keywords: heat transfer, dispersion, air bubbles, copper oxide nanofluid

Abstract

Both the air-water dispersion coefficient and the air-nanofluid ( CuO ) dispersion coefficient were studied and measured in a double-pipe heat exchanger. Pumping air into a tank fitted with a Rushton turbulent impeller resulted in gas-liquid dispersion. In order to test the effects of varying operating conditions on the air-water and air-nanofluid dispersions, they were heated and pumped into the tube of a double-pipe heat exchanger. Reynolds numbers of on the shell side and on the tube side were used to get the total heat transfer coefficient ( ). The dispersion in the hot fluid tank was achieved by combining the two-phase fluids using a Rushton turbine impeller. It was discovered that the conscious phase saw a significant drop in the heat transfer coefficient when the air bubbles dissipated. Because the impeller's agitation speed affects the rate at which air bubbles are broken, the heat transfer coefficient in the case of dispersion rises as Reh and Rec rise. For all examined parameter values, CuO nanofluid showed significant heat transfer improvement. The heat transfer rate of gas-liquid dispersion was increased by nanofluid by as much as compared to gas-liquid dispersion..

1. Introduction

The original version of this paper is available on https://www.scientific.net/EI.6.1.pdf

Multiphase flows, which involve dispersions of gas-liquid, liquid-liquid, and solid-liquid, are commonly observed in numerous industrial applications. Dispersants are utilized in a variety of industrial processes, such as petroleum refining, bio-chemical reactor operation, mixing procedures, food production, metalworking, and numerous other applications. The rate of heat transfer during heat treatment processes is influenced by the presence of dispersants with varying concentrations and properties in the process or working fluids. Heat exchangers find extensive usage in diverse industrial sectors such as oil and gas industries, thermal power plants, refrigeration systems, cooling of electronics, air conditioning, and automobiles, among others [1]. They are employed for cooling, heating, or phase changing purposes. Enhancing the heat transfer properties facilitates the conservation of energy, reduction of expenses, and enhancement of product excellence [2]. The phenomenon of gas-liquid dispersion is a common occurrence in a variety of industrial settings, characterized by the presence of two distinct phases. Gas-liquid dispersion cases find common applications in various processes such as petroleum processing from wells, air-liquid mixing and separation, fermentation, bubble columns, solar collectors, and nuclear reactors [3]. Gas-liquid dispersion is a phenomenon that frequently transpires in geothermal wells. This can be attributed to the pressure gradient within refinery distillation towers or the presence of a non-boiling gas phase that disperses within the liquid phase, such as oil-gas in pore wells [4].Research pertaining to the impact of multiphase flows on heat transfer properties has been a subject of investigation for over sixty years. Various geometries and designs have been examined in these studies, such as bubble
column reactors [5], multi-phase contactors [6], horizontal pipes [7], and stirred tanks [8]. The intricate nature of dispersion behavior has prompted extensive research into heat transfer studies of such systems, with the aim of enhancing comprehension of the phenomenon.
The presence of gas in the pores of liquids within a well constitutes a fluid exhibiting two-phase flow, which has a notable impact on the characteristics of heat transfer. These blends are incorporated into a refinery system through a series of multi-pass heat exchangers and CDU pump around heat exchangers. The escalation of gas phase fraction in two-phase flows has been observed to diminish the rate of heat transfer [6], [8], [9], thereby posing a significant obstacle in the realm of energy conservation.
The presence of particles such as drops, bubbles, and solids within a liquid stream has a significant impact on various factors including the thermal conductivity of the liquid, the interaction between different phases and the wall, and the turbulence levels both within the bulk and in the vicinity of the wall. This factor has an impact on the efficiency of heat transfer in the equipment and the operational expenses associated with the process. The heat transfer rate in a processed fluid can be affected by the presence of fine solid particles, which is contingent upon the thermal properties of the dispersed particles and the hydrodynamic conditions. Some studies have reported an improvement in heat transfer enhancement, while a reduction in heat transfer has been observed under specific operating conditions. Consequently, it is imperative to thoroughly examine and delineate the various states of a system in order to gain a more profound comprehension and achieve efficacious equipment development. Nanotechnology is widely regarded as a promising technological advancement for augmenting the rate of heat transfer. In instances such as this, it is common practice to disperse nano-scale materials possessing high thermal properties into the processed fluid. This serves to enhance the fluid's thermal conductivity and augment its interfacial area, thereby facilitating superior energy management and recovery outcomes.[10]-[13]. Nanofluids refer to solutions of nanoparticles that serve as a working fluid within heat transfer equipment, including but not limited to heat exchangers and automobile radiators. Nano-fluids have been widely utilized by researchers to increase the rate of heat transfer in single-phase systems. There is a paucity of literature on the utilization of nano-fluid for augmenting heat transfer in twophase flow, as evidenced by the limited number of studies conducted on this topic. Complex hydrodynamic and heat transfer phenomena are anticipated in such systems, which merit thorough investigation and analysis.
The phenomenon of gas-liquid dispersion resulting in a two-phase flow occurs when gas bubbles are introduced into a liquid medium. This process is commonly observed in various industrial applications, including but not limited to fermentation processes, mixing of air with oil in the petroleum industry, cooling processes of equipment with high heat transfer requirements such as bioreactors with exothermic reactions, rockets, and nuclear reactors [14]. Gas-liquid dispersion is exemplified by the introduction of air into mixers or bubble columns for biochemical processes. The flow pattern, physical characteristics, and hydrodynamic properties of gas-liquid dispersion are contingent upon a range of parameters, including the flow rate and physical properties of the liquid [15]. Applied The occurrence of bubbles in the vicinity of a heating tube wall is facilitated by the heat flux, which induces a localized increase in temperature and subsequent boiling. This phenomenon has been observed in previous studies, where bubbles were found to form in close proximity to the wall surface. [16]The formation of gas bubbles occurs under adiabatic conditions. The features of biphasic flow can be delineated as the augmentation in the rate of heat transfer, which may escalate the likelihood of the emergence of two-phase flow. The hydrodynamic properties of fluid exhibiting streamline behavior are subject to modification due to the influence of interactions arising from the coexistence of two distinct phases. The reduction in pressure during operation results in the generation of larger bubbles within the operational segment, thereby impacting the heat transfer process in a similar manner, as previously noted in references [14] and [16]. This demonstrates the intricacy linked with the thermodynamics of gas-liquid dispersion in two-phase flow. Furthermore, local points of two-phase flow may exhibit complexities, hydrodynamic instability, and deviations from thermodynamic equilibrium between the two phases.

In order to address the intricacies involved, it is necessary to conduct experimental investigations and analysis to estimate the thermal and hydrodynamic behavior of two-phase flow [14]. Gas-liquid dispersed flow is characterized by the presence of small bubbles that are separated by a continuous phase. The present study illustrates a flow pattern wherein a low gas superficial velocity is injected into the liquid phase, which serves as the continuous phase.The phenomenon of dispersed phase flow is observed in both stationary and flowing systems of a continuous phase. The dynamic stability of various factors such as bubbles breakage, coalescence, bubble size, and interfacial area are constrained by distinct parameters [17]-[20]. The interaction between bubbles and the liquid phase, as well as the phenomena of bubble breakage and coalescence, are significant factors in the study of transport phenomena in gas-liquid dispersions [17]-[22]. The hydrodynamics of dispersed gas-liquid flow is commonly investigated through the observation of bubble coalescence and breakage. Prior research has investigated the impact of distinct operational variables on the hydrodynamics of bubbles within the continuous phase, specifically the liquid phase. These variables include the viscosity of the liquid phase, the flow rate of the liquid phase, the flow rate of the gas phase, the presence of solid particles in three-phase reactors, and the agitation speed for stirred tanks. [17]-[19]. The phenomenon of bubbles breakage is characterized by the rupture of larger "mother" bubbles into smaller "daughter" bubbles due to the influence of both internal and external forces, as described in previous literature [17-19, 23].
The impact of two-phase flow of air-water and air-oil on the heat transfer coefficient in coiled and jacketed agitated tank was examined by Rao and Murti [1973] [24]. The dimensionless groups' correlations for heat transfer enhancement in the presence of two-phase flow were obtained by the authors. The study revealed that, in general, there is an increase in heat transfer as the agitation speed for coil increases, whereas a decrease in heat transfer is observed for jacketed agitated tanks. The impact of liquid phase physical properties on heat transfer coefficient in a bubble column was investigated by Abid et al. [2015] [17]. Various liquids, including water, water with ethanol, water with glycerol, and water with glycerol, were utilized in the study. The researchers arrived at the conclusion that the size of bubbles is influenced by various factors such as surface tension, viscosity, thermal conductivity, and specific heat, which are all dependent on the type of liquid. Liquids with higher viscosity were observed to produce larger bubbles, leading to an increase in the heat transfer coefficient. Conversely, liquids with lower viscosity were found to generate smaller bubbles, resulting in a decrease in the heat transfer coefficient[25]. The study conducted by Kashinsky et al. [2014] [26] examined the heat transfer of two-phase flow in an inclined smooth channel, covering a variety of superficial liquid and gas velocities. The present study aimed to investigate the heat transfer coefficient and shear stress at various locations within a channel experiencing two-phase flow. The results indicate a noteworthy reduction in the rate of heat transfer as the superficial liquid velocity is increased across various inclination angles. In their study, Dizaji and Jafarmadar [27] examined the impact of gas-liquid dispersion in a bubbly flow within a horizontal double pipe heat exchanger. Specifically, they introduced air bubbles into a stream of water and analyzed the resulting effects. The findings indicated that the heat transfer process was augmented when gas bubbles were present. The augmentation of heat transfer was found to be inversely proportional to the increase in Reynolds number. Additionally, it was observed that the introduction of bubbles in the shell side resulted in a greater enhancement of heat transfer as compared to the tube side. The introduction of bubbles within a tube serves as an insulating mechanism, whereas the injection of air bubbles within the shell side promotes turbulence[28]. A correlation for heat transfer of two-phase flow in a horizontal pipe was proposed by Kim and Ghajar [2006] [3]. The experimental section was exposed to a heat flux varying from 3,000 to , with a corresponding Reynolds number range of 820 to 26,000 . The gas Reynolds number range was found to be between 560 and 48,000 . The findings indicate that there is a positive correlation between the heat transfer coefficient and the liquid Reynolds number. However, the relationship between the heat transfer coefficient and the gas Reynolds number is complex and dependent on the flow pattern of the gas-liquid dispersion. The impact of agitation speed and gas flow rate on the heat transfer coefficient for a jacketed agitation tank was examined
by Xu et al. [1997] [29]. A correlation of significant complexity was noted between the agitation velocity, gas flow rate, and heat transfer coefficient. A reduction in heat transfer was noted when the critical gas flow rate exceeded at an agitation speed of 300 rpm . An increase in heat transfer efficiency was noted when the agitation speed was maintained below 200 rpm . The prediction of entrained liquid fraction in adiabatic gas-liquid annular two-phase flow in vertical pipes was examined by Cioncolini and Thom in 2010 [30]. This study has evaluated nine empirical correlations using an experimental data bank consisting of 1504 data points for 8 distinct gas-liquid combinations and 19 tube diameters ranging from 5.00 mm to 57.1 mm . The study determined that the correlation between Sawant, Ishii, and Mishima and that of Oliemans, Pots, and Trompé yielded the most accurate representation of the available data. A novel methodology for correlation, which was developed based on physical intuition and dimensional analysis, has been suggested. This approach has demonstrated superior performance compared to the existing techniques and has the potential to offer additional physical understanding of the atomization process of liquid films. The recently proposed correlation is founded upon the Weber number of the core flow, which is a crucial dimensionless parameter in ascertaining the wall shear stress and the corresponding frictional pressure gradient of annular flows. In 2014, Vidal [31] proposed a no-slip technique for forecasting the friction factor and, by extension, the frictional pressure drop in instances of twophase flow. The proposed methodology, which relies on the redefinition of the mixture Reynolds number, exhibits superior precision in comparison to other techniques documented in the literature. Nonetheless, the aforementioned approach failed to incorporate pertinent two-phase flow data due to the homogeneous assumptions made. The present investigation involves the manipulation of the mixture Reynolds number by incorporating the characteristics of two-phase flow phenomena, specifically the void fraction and flow pattern parameters. The study utilized data that was publicly accessible in scholarly publications to conduct a comparative analysis between theoretical predictions and empirical observations. The present study analyzed various flow regimes in the context of biphasic horizontal flow of refrigerants and air-water mixtures. The predictions generated by the proposed method were subjected to comparison with a pre-existing model from the literature and other homogeneous methods. The results of the comparison validated the superior accuracy of the new predictions. This manuscript provides a comprehensive account of the enhanced friction factor technique.

The improvement of heat transfer in industrial processes has become an increasingly pressing challenge due to economic and safety considerations. The investigation of the heat transfer phenomenon in solid-liquid dispersion systems, also known as nano-fluids, is a crucial area of interest for researchers in order to facilitate the effective design of heat exchangers. The utilization of nanotechnology has been recognized as a noteworthy approach to improve heat transfer. This involves the dispersion of materials at the nano-scale into the process fluid, which serves to enhance the thermal conductivity of the working fluid [11]-[13]. The introduction of nanoparticles into the process fluid is a method employed to augment heat transfer by elevating the heat transfer coefficient in situations where forced convection is present. According to reference [32], enhancing the dispersion of high conductivity nanoparticles in a fluid leads to an augmentation of the heat transfer coefficient, thereby improving the heat transfer characteristics. The physical characteristics of the process fluid, including viscosity, density, heat capacity, and flow conditions, are also influenced by the dispersion of nanoparticles. The thermal conductivity of nanoparticles is contingent upon various factors, including temperature, nanoparticle size, pH , and nanoparticle concentration [33]. The static model of thermal conductivity postulates that nanoparticles remain immobile within the bulk fluid, which in turn exhibits a uniform distribution of nanoparticles. This fluid flows in a streamlined manner within the bulk fluid, as per reference [34]. The correlation between dynamic models and assumptions of random movement of nanoparticles within bulk fluid has been observed. However, the presence of flow turbulence has been found to deform the stream line flow structure and result in non-uniform distribution of nanoparticles, as reported in previous studies [33], [35]. The impact of nano-fluids on the dispersed flow's heat transfer rate has been the
subject of extensive research over the past few decades. The present investigations have employed nanoparticles, which are classified as solid materials of nano-scale dimensions, while being suspended in a large volume of fluid. This section provides an overview of prior research that is pertinent to the current study. Several studies that have been previously documented comprise:
In their study, Duangthongsuk and Wongwises (2009) [36] utilized TiO2 nanoparticles with a diameter of 21 nm and a concentration of to augment heat transfer within a horizontal pipe. This approach aimed to improve the efficiency of heat transfer in the system. The findings indicate that an improvement of was achieved, and the heat transfer coefficient exhibited an upward trend as the flow rate of the hot fluid increased, while it decreased as the temperature of the nano-fluid rose. The findings of the study indicate that the utilization of nano-fluid does not yield any noteworthy impact on the pressure drop. Moreover, the friction factor of the nano-fluid was found to be in close proximity to the friction factor of water in the absence of nanoparticles. The study conducted by Zamzamian et al. [2011] [32] investigated the augmentation of heat transfer in a heat exchanger through the utilization of ethylene glycol and ethylene glycol nanofluid. The study examined forced convection heat transfer and analyzed the impact of nanoparticle size, concentration, and temperature. The findings demonstrated a high level of concurrence with previously established correlations. The findings of the study suggest that there is a positive correlation between the concentration of nanoparticles and temperature with an increase in heat transfer coefficient by . In 2013, Darzi and colleagues [37] conducted an experimental study to assess the effects of a 20 nm Al 2 O 3 nanofluid on the heat transfer, pressure drop, and thermal performance of a double-tube heat exchanger. The study aimed to determine the effective viscosity of nanofluid within a temperature range of 27 to 55 degrees Celsius. The study involved conducting experiments at different Reynolds numbers, ranging from 5000 to 20,000 , while varying the concentration of nanoparticles up to by volume. The findings indicate a high likelihood of enhancing the thermal efficiency of heat exchangers through the incorporation of nanoparticles within the examined parameters, provided that there is no significant increase in pressure drop. An empirical correlation was established for the alteration of the Nusselt number, taking into account the Reynolds number and the concentration of nanoparticles.b The study conducted by Aghayari et al. (2014) [38] investigated the enhancement of the heat transfer coefficient and Nusselt number in a nanofluid that consisted of gamma-AL2O3 nanoparticles with a particle size of 20 nm and a volume fraction ranging from to . This study investigates the impact of temperature and nanoparticle concentration on variations in Nusselt number and heat transfer coefficient in a twin pipe heat exchanger with counter turbulent flow. The level of concordance between empirical observations and theoretical data derived from semiempirical equations is deemed acceptable. The results of the experiment indicate that there is a notable increase of in both the heat transfer coefficient and Nusselt number. It has been observed that the heat transfer coefficient increases in correlation with both the operational temperature and concentration of nanoparticles. In their study, Albadr et al. (2013) [39] incorporated Al2O3 nanoparticles with a size of 30 nm and a volume percentage range of as an additive to the thermal fluid within a shell and tube counter flow heat exchanger. The findings indicate that the augmentation of nanofluid volume fraction resulted in a marginal enhancement of the heat transfer coefficient. An increase in the volume fraction of nano-particles resulted in a subsequent increase in the viscosity of the bulk fluid, leading to an increase in the friction factor. The study conducted by Rostamzadeh et al. (2014) [40] aimed to investigate the mixed convection heat transfer of Al 2 O 3 -water nanofluid within a vertical, W-shaped copper-tube with a uniform wall temperature through experimental means. The experiments encompassed various intervals of complex variables such as Reynolds number, temperature, and volume fraction of particles. The findings indicate that an increase in Reynolds number led to an enhancement in the heat transfer coefficient rate for mean wall temperatures of 50 and . Furthermore, there was a slight increase in the heat transfer coefficient as the Reynolds number increased. Remarkably, the pressure differential of the nanofluid exhibited a proximity to that of the base fluid. Additionally, a novel correlation has been suggested for the computation of the Nusselt number within W-shaped tubes.

Due to the unpredictability of gas-liquid dispersion/nano particle interactions, research into the increase of heat transfer by employing nanofluid under two-phase flow gas-liquid dispersion is uncommon. Not much is known about how fine gas bubbles affect heat exchanger efficiency from publicly available sources. This study utilizes a double-pipe heat exchanger in order to examine the impact of air bubble dispersion in water on the heat transfer coefficient under varying gas and liquid flow rates. The use of copper oxide nanoparticles to improve heat transfer in gas-liquid mixtures is another area of investigation.

2. Experimental Work

The original version of this paper is available on https://www.scientific.net/EI.6.1.pdf

To illustrate the experimental setting, see Figure1. A double-pipe heat exchanger, agitated tank, coiled heater, water chiller, pumps, control valves, annular-type flow meters (rotameters), temperature data-logger, air pump, and U-tube manometer were all part of the experimental setup. The gas-liquid mixture was dispersed by inserting it into an agitated tank with 8 flat blade impellers (Rushton turbine). The impeller has a diameter of 60 mm with a blade that is 25 mm in length, 10 mm in breadth, and 3 mm in thickness.

Figure 1: Schematic diagram of experimental rig.

Figure 1. Figure 1: Schematic diagram of experimental rig.

1.Double pipe heat exchanger, 2. Agitated tank, 3.Coiled heater, 4.Agitator motor, 5.Air pump, 6.Temperature data-logger, 7. Centrifugal pump, 8. Control valve, 9. Rotameter), 10.Chiller, 11.Thermocouples, 12.Impeller.

The first stage of the tests consisted of determining the total heat transfer coefficient (Uo) for single phase using water as the working fluid in the tube (tube side) and annulus (shell sid) throughout a range of Reh (19900-64000) and Rec (4750-13100). The water in the agitated tank was heated to 43 degrees Celsius using a coil heater, and then cooled to 15 degrees Celsius using a chiller. A centrifugal pump was used to transfer hot water into the heat exchanger's tube side, while a separate pump sent cold water into the heat exchanger's shell side. The hot and cold water flows were controlled by control valves. Using annular flow meters, we were able to calculate a hot Reynolds number (Reh) of 1900-64000 and a cool Reynolds number (Rec) of 4550-13100 for the flows. With a data-logger recording temperatures and an inverted manometer measuring pressure drop, we determined how the heat exchanger performed throughout a range of Reynolds numbers.
The data logger recorded the intake and output temperatures of both fluids on an excel sheet inside a ram that was included within the device. Each of the three experimental components relied on the temperatures to calculate heat transfer rate, log mean temperature difference, and an overall heat
transfer coefficient. Each time a temperature reading was taken (every 5 seconds), the data logger would save it. After each test run was completed, the temperature was recorded for another 5 minutes. For this reason, we took 60 temperature readings at the beginning and end of each run. At least two separate runs were taken. In order to guarantee the reliability of the findings, each measurement was repeated 120 times. As a benchmark, we looked at the mean average.
It was determined how to compute the total heat transfer coefficient using the outside surface area of the inner tube.

The frication factor was calculated from pressure drop measurements for tube side as:

Part two of the trials included achieving dispersion by the introduction of air bubbles into an agitated tank that was being mixed by an 8 -flat blade impeller spinning at either 200 or 800 revolutions per minute. When the impeller spins, it causes air bubbles to break apart and scatter their fine offspring farther and wider. The water in the test portion was kept at 15 degrees Celsius, and a hot dispersion fluid was injected into it to generate a temperature difference of 43 degrees Celsius.
The shear force of the impeller in the tank fractured the injected air bubbles into smaller daughter bubbles, resulting in more dispersion. A helical heater kept the water in the tank at a constant 43 degrees Celsius. In a countercurrent flow arrangement, the gas-liquid combination was introduced into the tube side, and cold water was introduced into the shell side. The Reynolds number for the hot fluid ranged between 1990 and 64000. The water chiller supplied the annulus with water in the Reynolds number range. The agitation speeds varied between 200 and 800 rpm , while the air injection rates varied between 0.054 and .
In one set of studies, slow-motion cameras were used to observe the diffusion of gas through a liquid. The size of the daughter bubbles was extracted from the photos by comparing the size of the bubbles to a standard item and then measuring the difference in pixels. The diameter of the impeller shaft was used as the standard. An average of over 40 bubbles was used to determine the average bubble size for each scenario.
Finally, copper oxide nanoparticles with the characteristics listed in Table 1 were included. The method used was the same as that outlined for the single-phase experiment. Concentrations of nanofluid (from to ) were created by adding copper oxide ( CuO ) nano-particles to boiling water. Under controlled conditions, the nano-fluid was heated to 43 degrees Celsius in an agitated tank. The frigid water was around 15 degrees Celsius. Nano-fluids with Reh values between 1900 and 64,000 (determined from the physical parameters of the continuous phase, water) were injected into the tube side of the heat exchanger, maintaining a constant Rec of 9,000 . As the conditions changed, the tube's temperature and pressure decrease were recorded. Table 2 displays the characteristics of nanoparticles under typical operating circumstances.

Table 1:Properties of copper oxide nanoparticles.

size,
nm
Purity
%
Shape
Specific
gravity
Specific surface
area, m²/g
Thermal conductivity,
W/m².K

40
99
spherical
6.4
120
32.8

The number of bubbles per unit time was obtained by finding average size for each injection test based on visualization analysis as:

Volume averaged interfacial area based on bubbles size distribution is expressed as follows [Kataoka et al, 2012]:

when a Volume-averaged interfacial area, dp (the diameter of the mother and daughter bubbles as determined by the visualization research), (the void %), n (the number of bubbles), and V (the volume of the container). The volume of the liquid did not change much despite the pumping of air into it. hence, Eq. took into account just the volume of the single phase (water) (7(
The following expreaaions were used to demonstrate how to compute the standard deviation and confidence levels for use in statistical analysis:

The ensuing dispersion from the mean values of the total heat transfer coefficient is quantified by its standard deviation. The margin of error quantifies the range of possible errors. The results of the statistical evaluation of the current work's four sections are shown in Table 2.

Table 2: Averaged standard deviation and margin error of degree of confidence of for all experimental conditions.

Parameter | Single phase | Gas-liquid dispersion | Nano-fluid | Gas-nano-fluid dispersion
Stdev of Uo, W/m².k | 51 | From 48 to 50.9 | 49.35 | 52.31
Margin of error of U₀ of 90% degree of confidence, W/m².k | ±18.8 | ±18.3 | ±18.2 | ±19.3

3. Results and Discussions

The original version of this paper is available on https://www.scientific.net/EI.6.1.pdf

3.1. Bubbles breakage visualizations

The slow-motion video camera was used for the visualization. When analyzing the experimental data, the size of the bubbles was determined by comparing the picture pixels to the impeller shaft. In a gas-liquid dispersion flow, the heat transfer properties are heavily influenced by the bubble size and distribution. Figure2 illustrates that the size of the mother bubble and the number of bubbles per second are both functions of Qg. The creation rate of daughter bubbles depends on both Qg and N. As seen in Figures 3 and 4, a mixer was utilized to increase the rate of dissipation of energy, so encouraging the breaking and spreading of bubbles. Figure 3 displays the relationship between bubble diameter (dp) and agitation speed (N) over a range of Qg. Due to the constant generation of smaller daughter bubbles produced by the fragmentation and breaking of mother bubbles, dp typically decreases as N increases. Liao and Lucas [2009], Solsvik and Jakobsen [2015], and Hasan [2017b] are all corroborated by the visualization findings. As shown in Figures 4 and 5, the interfacial area of gas-liquid dispersion grows as N rises because the bubbles generate smaller pieces for the same gas flow rate. In bubbly dispersed flow, a greater gas flow rate ( Qg ) results in a greater number of fragments per second and a greater interfacial area between the gas and liquid
phases. According to Hasan, [2017b], a larger gas flow rate increases the number of mother bubbles in the test section, which in turn raises the risk of bubbles breaking. This holds true for a range of values of N .

Figure 2: Bubbles breakage by utilizing impeller and for various N.

Figure 2. Figure 2: Bubbles breakage by utilizing impeller and for various N.

Figure 3: Bubbles diameter vs. agitation speed for various .

Figure 3. Figure 3: Bubbles diameter vs. agitation speed for various .

Figure 4: Number of bubbles number per time vs. Agitation speed for various .

Figure 4. Figure 4: Number of bubbles number per time vs. Agitation speed for various .

Figure 5: Interfacial area vs. agitation speed for various .

Figure 5. Figure 5: Interfacial area vs. agitation speed for various .

3.2. Heat transfer coefficient

Figure 6 shows the global heat transfer coefficient vs Reh for a range of Rec values for single-phase flow. When both Reh and Rec are raised, the total heat transfer coefficient rises. For both hydrodynamic and thermal boundary layer degradation, a high Reynolds number is responsible [Brodkey and Hershey, 1988; Slaiman et al., 2007]. Turbulent eddies are created when a high Reynolds number is reached. Figure 7 demonstrates that the results from the current study accord well with the results from Dittus and Boelter's [1930]correlation for Rec .

Figure 6: Overall heat transfer coefficient vs. for various for single phase flow.

Figure 6. Figure 6: Overall heat transfer coefficient vs. for various for single phase flow.

Figure 7: Overall heat transfer coefficient vs. for single phase flow and previous work correlation of .

Figure 7. Figure 7: Overall heat transfer coefficient vs. for single phase flow and previous work correlation of .

For impeller and , as shown in Figure 8, the influence of Reh on the f factor for gas-liquid dispersion flow can be seen. Figure 8 shows that for different values of N , ffactor has an inverse relationship with the Reynolds number. At higher N, when smaller bubbles are produced, the friction factor is less. Since the smaller bubbles spend more time in contact with the medicine, the viscous forces of the stream lines are also reduced. Pressure loss and friction are both reduced under flowing circumstances as a consequence of the material's increased density and viscosity [Saffariet al., 2013]. At and , the friction factor decreases by around 25%.

Figure 8: Friction factor vs. for gas-liquid dispersed phase flow for various of when and .

Figure 8. Figure 8: Friction factor vs. for gas-liquid dispersed phase flow for various of when and .

A gas-liquid dispersion system is shown in Figure 9 with di , and different agitation speeds. Uo tends to rise when Reh rises because of the aforementioned enhancement in convective heat transfer. While under stress, Uo does not always follow the same pattern as Reh. This is because the thermal characteristics of the continuous phase and the turbulence level are affected by the complex behavior of bubble rupture and coalescence. It can be shown that the Uo for a single phase (water) is greater than the dispersion for weak to moderate Reh. Uo for dispersion is greater for high Reh and comparatively high N. The behavior of the heat transfer coefficient in an agitated gas-liquid dispersion is complicated. For all values of N , heat transmission improves with rising Reh and then declines between Reh and 37,000 . Once Reh is over 37,000 , the heat transfer coefficient begins to rise, eventually surpassing single-phase values at Reh>47,000. Xuet al. [1997] found a similar pattern of behavior in a system like this.

Figure 9: Overall heat transfer coefficient vs. Re for various of gas-liquid dispersion at .

Figure 9. Figure 9: Overall heat transfer coefficient vs. Re for various of gas-liquid dispersion at .

For and in a gas-liquid system, the relationship between Uo and N is shown in Figure 11. At Reh=64000, the optimum agitation speed is and the smallest Uo is recorded over the whole range of Reh. A fluctuating agitation speed, as measured by Uo, reveals the rates of fragmentation and coalescence under these varied operational settings. This intricate pattern of N's impact on Uo is influenced greatly by bubble size. When N is low, huge bubbles rise more quickly than the smaller bubbles created when N is high. Increases in N cause a greater proportion of bubbles to burst, with the result that more turbulence is generated and more heat transferred [Rao and Murti, 1973].

Figure 10: Overall heat transfer coefficient vs. for various of gas-liquid dispersion at .

Figure 10. Figure 10: Overall heat transfer coefficient vs. for various of gas-liquid dispersion at .

If and is entered into the gas-liquid system simulator, the resulting plot is shown in Figure 11. At Reh=64000, the optimum agitation speed is and the smallest Uo is recorded over the whole range of Reh. The rates of fragmentation and coalescence under these various operating settings are shown by the non-uniform trend of agitation speed in Uo. This intricate pattern of N's impact on Uo is influenced greatly by bubble size. When N is low, huge bubbles rise more quickly than the smaller bubbles created when N is high. As N rises, more bubbles rupture, with smaller bubbles inducing more turbulence while larger bubbles consolidate to improve the heat transfer coefficient [Rao and Murti, 1973].

Figure 11: Overall heat transfer coefficient vs. agitation speed for various of gas-liquid dispersion at and at .

Figure 11. Figure 11: Overall heat transfer coefficient vs. agitation speed for various of gas-liquid dispersion at and at .

3.3. Forced convection of nanofluid

The friction factor (f) vs the resistance to heat transfer (Reh) of nanofluid flow inside a heat exchanger at for different concentrations of nanoparticles is shown in Figure 12. (C). For a constant C , the Reynolds number has a negative effect on f . Consistent with the findings of Sahin et al. (2015), for small values of Reh, the friction factor for nanofluid is less than that for a single phase (water). For Reh >=43000, the concentration of nanoparticles has no appreciable impact on f . This behavior is grounded on the physicochemical characteristics of nanofluid, which exhibit a modest increase in viscosity and density. This impact becomes almost nonexistent at high speeds [Bianco et al., 2015].

Figure 12: Friction factor vs. Re of nanofluid for various .

Figure 12. Figure 12: Friction factor vs. Re of nanofluid for various .

As shown in Figure 13, as the concentration of nanoparticles rises, so does the heat transfer coefficient. According to the graph, the optimal value for enhancement at high Reh is , whereas at low Reh, the value is . For a concentration of , the heat transfer coefficient marginally reduces. A considerable increase in the heat transfer coefficient is seen between 0.5 and of nanoparticles, followed by a modest rise at and a subsequent reduction at of nanoparticles. Coefficient of heat transmission depends on nanofluid's physical characteristics and its hydrodynamics. As nanofluid thermal conductivity rises, so does the heat transfer coefficient. As C is increased, a fouling layer of nanoparticles forms on the tube's surface, reducing heat transmission and hence lowering Uo[Sarafraz et al., 2017]. Sarafraz et al(2017) .'s findings that the heat transfer coefficient of a plate heat exchanger rises with nanofluid concentration up to and begins to decline beyond this concentration are generally supported by the data presented here.

Figure 13: Overall heat transfer coefficient vs. of nanofluid for various at .

Figure 13. Figure 13: Overall heat transfer coefficient vs. of nanofluid for various at .

3.4. Forced convection of gas-nanofluid dispersion

Multiple graphs of the overall heat transfer coefficient versus Reh are shown in Figures 14 and 15. A higher Reynolds number improves heat transmission by a greater percentage. There seems to be no observable pattern of improvement with increasing N. As the thermal conductivity of the gasliquid dispersion increases, the influence of tiny bubbles is mitigated, resulting in improved heat transmission. When nanoparticles are introduced, the tiny bubbles responsible for the drop in Uo are compelled to merge into bigger bubbles. Nanoparticles distort tiny bubbles into bigger ones, and their collisions create turbulent intensities that destroy the thermal boundary layer. When employing nanofluids for improved heat transmission, the rule of thermal conductivity is crucial. The mechanics of nano-scale convection are discussed by Jang and Choi [2004]. The processes employed by Jang and Choi [2004] include the collision of molecules in the liquid phase, the collision of nanoparticles, the thermal diffusion of solid nanoparticles, and nano-scale Brownian motion generated by nano-scale convection. Here we see the originality of the current study, which, in contrast to the conventional approach of applying excessive liquid pressure to suppress twophase flow, improved heat transmission of the latter by using the nanoparticles' functional properties. Table 3 shows that the optimum heat transmission occurs at and Reh=64000.

Figure 14: Overall heat transfer coefficient vs. for gas-solid-liquid dispersion (gas dispersed in nanofluid) at and .

Figure 14. Figure 14: Overall heat transfer coefficient vs. for gas-solid-liquid dispersion (gas dispersed in nanofluid) at and .

Figure 15:Overall heat transfer coefficient vs. for gas-solid-liquid dispersion (gas dispersed in nanofluid) at and .

Figure 15. Figure 15:Overall heat transfer coefficient vs. for gas-solid-liquid dispersion (gas dispersed in nanofluid) at and .

4. Conclusion

The original version of this paper is available on https://www.scientific.net/EI.6.1.pdf

Heat exchanger performance was evaluated by calculating the total heat transfer coefficient under various testing settings. Heat transfer coefficient may be considerably impacted by two phase flow interaction at higher gas flow rates for different agitation speeds (gas-liquid dispersion as a
consequence of bubbles breaking). Hydrodynamic action improves heat transfer with greater contiguous phase Re and lower gas flow rate. The heat transmission coefficient drops to its lowest value- -under the most severe circumstances for bubble breakdown. As the concentration of nanoparticles grows, so does the value of the heat transfer coefficient. According to the findings, the optimal value for enhancement at high Reh is , whereas at low Reh, the value is . As the concentration increases, the heat transfer coefficient drops down somewhat. There seems to be no observable pattern of improvement with increasing N. By enhancing the thermal conductivity of gas-liquid dispersion under turbulent circumstances, when the influence of tiny bubbles is diminished, a increase in heat transfer is obtained. Adding nanoparticles forces the Uoreducing tiny bubbles to merge into bigger ones. Nanoparticles distort tiny bubbles into bigger ones, and their collisions create turbulent intensities that destroy the thermal boundary layer. In the presence of gas dispersion and nanofluids, friction is reduced because viscosity is decreased by the effect of increased mobility [Brodkey and Hershey, 1998].

Symbol | meaning | units
U | Overall heat transfer coefficient | W/m2.K
Re | Reynolds number |
T | Temperature difference |
Q | Gas flow rate |
di | Impeller diameter | mm

Bubbles diameter mm

Greek letters

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Symbol | meaning | units
Dynamic viscosity | Kg/m. s |
a | Interfacial area |
| Density |
| Gas void fraction |

Subscribes

The original version of this paper is available on https://www.scientific.net/EI.6.1.pdf

i | Impeller
b | bubbles
T | tank diameter
h | hot water
c | cold water
g | gas
o | outside surface of tube

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Engineering Innovations, ISSN: 2813-1002, Vol. 6, pp 23-29

doi: 10.4028/p-6s83p4

© 2023 The Author(s). Published by Trans Tech Publications Ltd, Switzerland.

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2023-06-21

Research Effectiveness of Regeneral Brake Energy on Toyota Prius Vehicles by Matlab/Simulink

The original version of this paper is available on https://www.scientific.net/EI.6.23.pdf

Minh Le Toan Nguyen Van

Vinh Long University of Technology Education, Viet Nam

minhl@vlute.edu.vn, toannv@vlute.edu.vn

Keywords: Brake regeneration, dynamic recovery, hybrid vehicle, green vehicle

Abstract

Regenerative brake control creates an optimal synergy between mechanical and electrical braking. Based on the study of vehicle dynamics under braking conditions propose a new control mode that ensures the best braking performance and maximum braking energy recovery. The implementation of the above control mode requires a combination of the traction control model and the brake control system. The HEV power distribution model is built using Matlab/ Simulink and the simulation results have shown a significant improvement in fuel consumption when using the regenerative braking system.

Introduction

The original version of this paper is available on https://www.scientific.net/EI.6.23.pdf

For conventional vehicles, a considerable amount of energy is converted into heat consumed during braking . To save energy, HEV will combine both a mechanical braking system and an electric brake system to both achieve braking efficiency and make the most of regenerative energy. The electric motor converts kinetic energy into electrical energy to charge the battery during braking.

Therefore, regenerative braking is an indispensable element to improve the fuel economy of HEVs. Studies have shown that, depending on driving conditions and control modes, energy regeneration can add to to a vehicle's total energy use [ 3,4 ]. The brake control mode has an important role in recovering as much energy as possible while maintaining the vehicle's stability during braking. Various studies have been carried out on regenerative braking systems to respond to brake control operations based on several rules [5], control strategies using fuzzy logic [6, 7], and control methods. neural network applications [8]. However, these studies mainly focus on improving energy recovery based on conventional factors without considering safety, reliability, and applicability criteria. expectations.

The brake control mode and brake force distribution in HEV proposed by the author in this paper have obtained maximum regenerative energy, safety, as well as easy application. Under braking conditions, the vehicle can achieve optimal regenerative energy efficiency. It can also prevent wheel locking and slippage by distributing the right braking force to the front and rear wheels according to operating conditions. Simulation results have demonstrated the effectiveness of the proposed brake control mode in ensuring safety, braking stability, and improving regenerative energy efficiency

Hybrid Vehicle Brake System

The original version of this paper is available on https://www.scientific.net/EI.6.23.pdf

Structure. In hybrid vehicles, the system is combined by two components, the mechanical brake system, and the electric brake system, to optimize braking force and ensure maximum regenerative energy recovery while still ensuring safety. In particular, the braking system on current hybrid models can be controlled separately from the braking torque at the front and rear wheel positions, this system is often combined in the usual form of two. The front wheel uses a combination of electrical and mechanical brakes, while the two rear wheels only use mechanical brakes. Because of such a structure, the energy recovered from regenerative braking is only obtained from braking at the front wheel by an electric motor and charged to a high-voltage battery. This type of structure contributes to a more efficient distribution of braking force between the front and rear wheels. However, if the load placed on the front or rear wheels is changed, the braking performance will also be changed. To maintain vehicle stability during braking, the rear wheels must not be locked in front of the front
wheels. Therefore, when studying brake control, it is necessary to study the distribution of braking force between the front and rear wheels, and the distribution of braking force between the mechanical brake system and the electric brake system.

Figure 1. Brake system on HEV.

Figure 1. Figure 1. Brake system on HEV.

Facility in Terms of Brake Force Distribution. Figure 2 shows the load shift of the vehicle between the front and rear axles after the vehicle brakes on a flat road. Here it is assumed that rolling resistance and air resistance are ignored because they are very small compared to the braking force. Where j is the deceleration of the vehicle when braking, M is the mass of the vehicle, and the force exerted by the vehicle is shown by Equation 1.

Figure 2. Forces acting on a car when braking on a level road.

Figure 2. Figure 2. Forces acting on a car when braking on a level road.

Considering the change of vehicle load from the rear wheel to the front wheel is shown by equation (2) below, and the braking force between the rear wheel and the front wheel is obtained from equations (3) and (4). , where and is the load placed on the front and rear axles is the coefficient of traction between the wheel and the road surface

In order to achieve high efficiency and stability during braking, the controller must distribute the maximum braking force to the front wheel and the wrong wheel so that both can lock the wheel at the same time, but without any crash. slide icon. To distribute the braking force according to the ideal
curve, it is necessary to determine the coefficient of friction between the wheel and the road surface. However, this coefficient of friction continuously changes with the state of the vehicle and depends a lot on the surface texture of the road surface, so it is difficult to determine this coefficient directly. Therefore, it is necessary to determine the ratio of brake force distribution of the front and rear wheels . These ratios are expressed as equations (5) and (6) and do not depend on the coefficient of friction.

From equations (5) and (6) it is found that the braking force depends only on the load and the acceleration slows down when braking. The braking torque at the front wheel and the rear wheel is determined according to equations (7) and (8)

To achieve high efficiency and stability during braking, the controller must distribute the maximum possible braking force to the front and rear wheels so that they can lock at the same time. To distribute the braking force along the ideal curve, we need to determine the coefficient of friction between the wheel and the road surface. However, because these coefficients of friction are constantly changing with the operating conditions of the vehicle, it is difficult to determine them directly. Therefore, we need to determine the ratio of brake force distribution of the front wheel (Rf) and the rear wheel (Rr). These ratios are expressed as equations (5), and (6) and they do not depend on the coefficient of friction. Where is the tire radius [m], g = 9.81[ m/s2] is the acceleration due to gravity.

Consider the same case when the car brakes on a slope. The calculation of the distributed load is as shown in Figure 3, the deceleration is measured by the accelerometer ( ), equation (9). The braking force is determined by equation (10)

Figure 3. Kinetic model when braking on a slope.

Figure 3. Figure 3. Kinetic model when braking on a slope.

Realizing that the load distribution is estimated by the signal provided by the accelerometer, the ideal braking force distribution can be realized even on flat or steep roads. From the above calculations, ideal braking force control can be achieved by estimating the actual vehicle weight and load distribution using accelerometers [8].

Optimum Control Mode for Energy Recovery During Regenerative Braking. The principle of the control mode is to distribute maximum braking force to the front wheel during braking. Also, ensure the rear wheel is not locked earlier than the front wheel by any coefficient of traction. The greater the braking force distributed on the front wheels; the more energy will be recovered to recharge the High-Pressure Battery. Braking performance requires that no wheels are locked and the braking force on the rear wheel must be higher than the ECE adjustment curve as shown in Figure 4. The braking force on the front and rear wheels depends on the deceleration rate of the vehicle. Vehicle and the coefficient of traction of the road surface [7].

Figure 4. Ideal characteristic of brake force distribution between front and rear wheels according to ECE standards and ideal characteristics.

Figure 4. Figure 4. Ideal characteristic of brake force distribution between front and rear wheels according to ECE standards and ideal characteristics.

When the braking force is less than , all braking force will be allocated to the front wheel to use regenerative braking, no mechanical braking force is applied to the front or rear wheels. When the braking degree is more than 0.2 g , the new mechanical brake system is started to work. Braking force will be distributed to the front and rear wheels according to the ideal distribution of braking force (I-curve).

At the same time, braking force on the front wheels will be distributed both electrically and mechanically with the maximum amount of energy recovered. The maximum braking power must not exceed the maximum power of the electric motor and the storage capacity of the battery. In addition, the minimum braking power on the rear axle must meet ECE standards. Therefore, the braking force on the front and rear wheels can vary within a certain range. This range is limited by the ideal brake force distribution curve and the ECE curve. However, depending on operating conditions, the braking force will be distributed to meet the maximum energy recovered from the front wheel.

Controller Design. In the research scope of this paper, brake control will focus on the maximum possible regenerative energy recovery efficiency and is controlled by several factors including vehicle speed, braking torque requirement and state of charge (SOC) of the high-voltage battery pack. The SOC of a high-voltage battery can affect the efficiency during the charging and discharging of the assembly. The optimal operation of high-voltage batteries should be controlled between and . The relationships between these elements will be implemented by the Fuzzy Logic controller [9].

Regarding the safety of automobile movement, if the required braking torque is large (in case of emergency), then the regenerative braking torque is now zero (mechanical brake only). Similarly, when the vehicle speed is high, also apply the mechanical brake only. If the vehicle speed is very low, the efficiency of the electric motor is very low, and the regenerative braking is not effective so it is not used [8].

To demonstrate the effectiveness of the brake controller, we use the actual vehicle with the specifications as shown in Table 1 to perform the simulation.

From the above analysis, we can see that the three inputs of the fuzzy logic controller include the required braking torque, SOC, and vehicle speed. To increase generality, all inputs and outputs should be normalized in the range [ 0,1 ]. The definitions of these inputs are listed below. The braking torque is divided into 5 subfunctions in the range as shown in Figure 5. The braking torque requirement is divided into 5 zones such as VH (very high braking torque), H (braking torque). High), M (Medium braking torque), L (low braking torque) and VL (very low braking torque).

Figure 5. Signal control according to Fuzzy logic, (a) Braking torque; (b)Vehicle speed; (c) SOC, (d) Output signal.

Figure 5. Figure 5. Signal control according to Fuzzy logic, (a) Braking torque; (b)Vehicle speed; (c) SOC, (d) Output signal.

Table 1. Specifications of vehicles included in the simulation.

Specifications | Value
Full load mass | 1805 kg
The standard long | 2.75 m
Car center of gravity | 0.76 m
Distance from center of gravity to center of front wheel | 0.982 m
Distance from center of gravity to center of rear wheel | 1,768 m
Grip coefficient | 0.22 -1

Similarly, the vehicle speed and input SOC also have five levels as shown in Figures 6 and 7 respectively. Since the optimal discharge-discharge process of the battery is between and of capacity, the SOC input is concentrated mainly in this region. The component functions are shown in Figure 5 (c).

The output of the brake controller is the ratio of the regenerative braking torque on the front axle to the total required torque. There are 5 component functions, VL, L, M, H, and VH, as shown in Figure 5 (d) .

The brake controller calculates the regenerative braking ratio on the front axle to ensure maximum regenerative energy while ensuring safety. The remaining braking force is distributed to the mechanical braking of the front and rear axles. Figure 9 shows the algorithm flowchart depicting the brake distribution on the front and rear wheels.

Figure 6. Flow chart of brake torque distribution on the front and rear axles.

Figure 6. Figure 6. Flow chart of brake torque distribution on the front and rear axles.

In the algorithm flowchart, , and are torque ratios, respectively.
Regenerative braking and required braking torque, required braking torque, front-wheel mechanical braking torque, rear-wheel mechanical braking torque, front - wheel regenerative braking torque, front-wheel maximum braking torque and rear axle maximum braking torque.

Simulation Results. The brake torque controller has been designed and simulated by Simulink/Matlab on the NEDC (New Europe Driving Cycle) test cycle. Figure 7 shows the desired actual speed of the vehicle and the calculated simulated speed asymptotically close to each other. Figure 8 shows a very high ratio of regenerative braking torque to required braking torque, which means that the brake controller recovers energy during braking very well. Figure 9, shows the change of battery charging status when the vehicle runs on the NEDC cycle. The battery always operates at its optimum range through these driving cycles. Finally, Figure 10 shows the operating points in the electric motor efficiency map. Operating points of a negative torque motor, i.e. the engine acts as a generator. The engine also operates in the high-efficiency area

Figure 7. Simulated NEDC velocity.

Figure 7. Figure 7. Simulated NEDC velocity.

Figure 9. High voltage battery charging current charging status.

Figure 8. Figure 9. High voltage battery charging current charging status.

Figure 8. Ratio of regenerative braking torque and required torque.

Figure 9. Figure 8. Ratio of regenerative braking torque and required torque.

Figure 10. Operating points of electric motors.

Figure 10. Figure 10. Operating points of electric motors.

From the above results, the model performed with the brake controller using fuzzy logic has achieved higher energy efficiency than the law-based brake controller, especially when the vehicle is operating in urban areas. In the urban driving cycle according to the US federal test cycle - UDDS (Urban Dynamometer Driving Schedule), the car using the regenerative brake controller using fuzzy logic saves compared to the system using the controller. Law-based brake control. Similarly, in the NEDC driving cycle, the vehicle using the fuzzy logic regenerative braking system saves 5.25% compared to the braking system with the rule-based controller. All of the parameters are presented in detail between the two simulation cycles in Table 2.

Table 2. Fuel consumption with regenerative braking using the law-based controller and fuzzy logic controller.

CYCLE
REGULAR
(l/100Km)
FUZZY LOGIC
(l/100 Km)
EFFICIENCY
(%)

UDDS
4,45
4,24
2,1

NEDC
5,76
5,51
2.5

Summary

The original version of this paper is available on https://www.scientific.net/EI.6.23.pdf

The brake controller using fuzzy logic has been designed and simulated, the results of which have been demonstrated in hybrid electric-motor vehicles with high regenerative energy results. The regenerative brake controller recovered the most energy and ensured safety during braking. Applying fuzzy logic to design the brake controllers with the proposed control strategy got better performance and more fuel economy. The brake controller always meets the requirements of the control process according to the brake pedal signal, the battery always provides enough power to meet each operating mode of the HEVs, and always ensures the stability of the vehicle during braking.

References

The original version of this paper is available on https://www.scientific.net/EI.6.23.pdf

Gao, Y., L.P. Chen, and M. Ehsani, Investigation of the effectiveness of regenerative braking for EV and HEV. SAE Trans., 1999: p. 3184-3190

Rajashekara, K., Power conversion and control strategies for fuel cell vehicles. In Proceeding of the 29th Annual Conference of the IEEE Industrial Electronics Society,2004. 3: p. 2865-2870.

Zolot, M., T. Markel, and A. Pesaran, Analysis of fuel cell vehicle hybridization and implications for energy storage devices. Proceedings of the 4th advanced automotive battery conference, 2004: p. 121-124

Guo, J.G., J.P. Wang, and B.G. Cao, Regenerative braking strategy for electric vehicles. Proceedings of the IEEE Intelligent vehicles symposium, 2005: p. 864-868.

Li, X.J., et al., Regenerative braking control strategy for fuel cell hybrid vehicles using fuzzy logic. Proceedings of the International conference on electrical machines and systems 2008: p. 712-2716.

Cao, J.B., et al., Neural network self-adaptive PID control for driving and regenerative braking of an electric vehicle. Proceedings of the IEEE International Conference on Automation and Logistics, 2007: p. 2019-2034.

Mehrdad Ehsani, Y. Gao, and A. Emadi, Morden electric, hybrid electric, and fuel cell vehicles: Fundamentals, theory, and design, 2nd ed 2010: CRC Press

W. K. Yap, and V. Karri. Modeling and Simulation of a Hybrid Scooter. World Academy of Science, Engineering and Technology 47, 2008

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Engineering Innovations, ISSN: 2813-1002, Vol. 6, pp 31-36

doi: 10.4028/p-t122xr

© 2023 The Author(s). Published by Trans Tech Publications Ltd, Switzerland.

Submitted:
2023-03-01

Accepted:
2023-03-12

Online:
2023-06-21

Low Latency Extended Dijkstra Algorithm with Multiple Linear Regression for Optimal Path Planning of Multiple AGVs Network

The original version of this paper is available on https://www.scientific.net/EI.6.31.pdf

Leong Wen Chek

MIMOS Berhad, Technology Park Malaysia, Bukit Jalil 57000, Kuala Lumpur, Malaysia

wenchek.leong@mimos.my

Keywords: Dijkstra algorithm, Linear Regression, AGV network, Collision, Deadlock

Abstract

Dijkstra algorithms are typically used to find the shortest path from a source node to a destination node. It is widely used in various applications due to its reliability and less complexity. This paper presents the extended Dijkstra Algorithm with lower latency and consumes less computing memory intended for implementation in many AGVs networks for effective decentralized task distribution path planning. This paper proposed linear regression normalization across the node network in Dijkstra architecture to reduce computing time and memory consumption. The issue addressed through this optimization focused on reducing the possibilities of collision between AGVs and deadlock. The extended Dijkstra algorithm significantly reduces computing time compared to the traditional Dijkstra algorithm. In addition, the proposed solutions suggest better AGV routing for collision avoidance and deadlock prevention possibilities.

Introduction

The original version of this paper is available on https://www.scientific.net/EI.6.31.pdf

Dijkstra algorithm was first conceived by Edsger W. Dijkstra [1,2], a computer scientist, back in 1956. It was designed to compute the shortest path between nodes in a graph in 1959. It was the first design to compute the shortest path in internet protocol, mapping localization, routing protocol, social network analysis and path planning [3, 4]. This model favors research as it is highly efficient in computing the shortest path of a complex network. Researchers, scientists, and engineers have widely used the algorithm in various applications. The algorithm may exist in many forms nowadays due to its wide application and many variants of the extended version of the Dijkstra algorithm modified for different application types.

Much research has been done on optimizing and extending the Dijkstra algorithm. Dijkstra algorithm with multi-objective variant has been designed from terrain data to compute the optimal traversal in 3D surface [5, 6]. The computed path was obtained and implemented in the Global Rover Horizontal Optimization Planner (GRTOP) automation system to simultaneously set up optimized routes for multiple constraints. In another work, the Dijkstra algorithm has been modified with a vehicle emission measurement model and vehicle fuel consumption for a better driving experience [7, 8].

Dijkstra's algorithm can be used to find the shortest route between one city and all other cities. Widely used application of shortest path algorithms in network routing protocols, most notably ISIS (Intermediate System to Intermediate System) and OSPF (Open Shortest Path First). It is also a subroutine in other algorithms such as Johnson's. For example, if the nodes of the graph represent cities and edge path costs represent driving distances between pairs of cities connected by a direct road (for simplicity, ignore red lights, stop signs, toll roads and other obstructions).

Fundamental of Dijkstra Algorithm

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The Dijkstra algorithm is a fundamental algorithm for the AGV path planning. AGVs are autonomous vehicles that are used in transportation of various industries, such as warehousing, manufacturing, healthcare and logistics. AGVs require to navigate through complicated environments with obstacles and multiple paths to reach the destinations accurately.

The Dijkstra algorithm is a shortest path algorithm that can find the shortest path between two nodes in a graph. In AGV path planning, the nodes represent the locations in the environment, and the edges represent the connections between them. The weights assigned to the edges represent the distance or travel time between the locations.

The Dijkstra algorithm operated by starting at the source node and calculating the shortest path to all other nodes in the graph. It keeps track of the distances to each node and the shortest path that led to the target node. The algorithm then selects the node with the shortest distance and adds it to the traffic list of visited nodes. It repeats this process until it reaches the final target node.

AGVs can use the Dijkstra algorithm to calculate the shortest path between the source location and the target location while avoiding obstacles and taking into account the AGV's speed and acceleration constraints. The algorithm can also be adapted to handle dynamic environments, where the weights on the edges are variable based on the real-time situations.

Overall, the Dijkstra algorithm is a fundamental algorithm for AGV path planning, as it can efficiently find the shortest path in complex environments with obstacles and multiple paths.

Implementation of Algorithm

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Let the node at which the Dijkstra algorithm starts to be computed be called the initial node. Let the distance of node be the distance from the initial node to . Dijkstra's algorithm will initially start with infinite distances and will try to improve them step by step. Dijkstra model works effectively in finding the shortest path for various applications such as mapping, path planning, routing protocol and social network analysis. It is intended to configure the shortest-path issue for directed graphs with positive weights. Moreover, it can calculate graphs with the cycle as well. However, non-positive weights significantly impact efficiency by generating incorrect results. The setback of the conventional Dijkstra model is the latency issue which consumes high computing memory and time. This is mainly due to the computing parameter, including node, path, etc.

Figure 1. Dijkstra algorithm routing.
a) Dijkstra computing process, b) results of Dijkstra shortest path finding.

Figure 1. Figure 1. Dijkstra algorithm routing. a) Dijkstra computing process, b) results of Dijkstra shortest path finding.

A traditional Dijkstra algorithm has been tested on a complex AGV network to determine its performance and setback. Fig. 1 (a) illustrates the computing process of the Dijkstra algorithm, while Fig. 1 (b) shows the results of the Dijkstra algorithm for four different nodes' weights. It can be concluded that the Dijkstra algorithm is best in finding the shortest path. However, it does not consider the traffic and unnecessary computing path. An unnecessary computing path produces higher memory consumption, which leads to longer computing time. The extended Dijkstra algorithm proposed in this paper eliminates the routing that provides possibilities of deadlock and collision.

Advantages of Extended Dijkstra algorithm in AGV path planning

The original version of this paper is available on https://www.scientific.net/EI.6.31.pdf

Extended Dijkstra algorithm contributes to a shorter computation time where AGVs often operate in time-critical environments, and path planning needs to be done promptly. The extended Dijkstra algorithm can be faster than the traditional Dijkstra algorithm, especially for larger networks, as it searches from both nodes the origin and target nodes at the same time. This reduces significantly the search space and can result in a shorter computation time.

Additionally, the Extended Dijkstra algorithm able to reduce the memory usage. This benefits the AGVs typically the one with limited on-board memory, and extended Dijkstra algorithm anle to reduce memory usage as it only needs to use the storage for saving data on the nodes visited from each end.

Real-time updates: AGVs can encounter unexpected obstacles or changes in the environment that require real-time path planning updates. The extended Dijkstra algorithm can allow for real-time updates as it only needs to update the path from the point of intersection, rather than recomputing the entire path.

Improved efficiency: AGVs are often used in environments with complex structures, such as warehouses or manufacturing facilities. The extended Dijkstra algorithm can efficiently handle complex graphs with multiple paths between nodes, making it suitable for AGV path planning.

Methodology

The original version of this paper is available on https://www.scientific.net/EI.6.31.pdf

Comparison analysis has been performed between both methods based on a simple AGV network, as in Fig. 2.

Method 1: Traditional Dijkstra Algorithm

The original version of this paper is available on https://www.scientific.net/EI.6.31.pdf

The traditional Dijkstra Algorithm is derived from the Euclidean distance between adjacent nodes.

Input:

The original version of this paper is available on https://www.scientific.net/EI.6.31.pdf

The shortest path obtained from a bidirectional graph as in Figure X with weights , all nodes set N, connected weighted edges set nodes W.

The source node A.
The target node D.

Output:

The original version of this paper is available on https://www.scientific.net/EI.6.31.pdf

Length and shortest path from the source node A to the target node D.
It can be predicted that produce positive weight network, . Therefore, the minimum will fulfill the equation as below:

minimum
Thus, the shortest path from node v1 to a different node in D is determined from longest distance to nearest as follows:

Assuming , by substituting into equation (1), the results are as follows:
minimum

minimum minimum minimum

The Adjacency matrix is defined as follows:

The distance matrix is defined as follows

Adjacent matrix, distance matrix and correlation matrix are used to determine the shortest path on the Dijkstra network matrix in computer program and algorithm. However, the case in Figure X is simple to AGVs path routing. It uses Dijkstra to compute the shortest path between source node A and target node D. Numerous N*N arrays are identified to compute and store graphical data, where N represents the network node's count. When the count of N is large, it requires higher CPU memory. If there are 4000 nodes, it requires 64 MB memory; if there are 8000 nodes, it requires 256 MB memory. This will give high latency to the system if multiple AGVs are waiting to compute the shortest path before executing a task. In this case, the Dijkstra algorithm is extremely difficult to implement in massive network analysis data.

Method 2: Extended Dijkstra Algorithm

The original version of this paper is available on https://www.scientific.net/EI.6.31.pdf

As mentioned earlier, the extended Dijkstra is suggested to be implemented to decentralize AGV task distribution path planning. The capability to find the shortest paths from the nearest AGV node to the task node stops the algorithm once the shortest path to the destination node has been determined.

The traditional Dijkstra algorithm has been used for several decades to compute the shortest path in a graph between two nodes by computing the distance matrix and adjacent matrix. The application is not only limited to computing the shortest path in a graph. It applies to any case with two defined parameters: adjacent and distance. The adjacent consists of the source node and target node.

This paper's proposed extended Dijkstra algorithm is derived from the inversed multi-linear regression. The derivation enables multiple Dijkstra algorithms to compute simultaneously with fewer possibilities of collision and deadlock. Besides, the proposed extended Dijkstra is also applicable for various applications such as multicasting, internet protocol and routing.

The proposed extended Dijkstra model is intended to be used in task distribution decentralization path planning for AGV with an extensive network. The proposed approach uses lower memory consumption, thus computing the shortest path faster than the conventional Dijkstra model. In addition, the proposed extended Dijkstra model can reduce collision and deadlock possibilities in multiple AGV network task distributions.

Result

The original version of this paper is available on https://www.scientific.net/EI.6.31.pdf

Fig. 2 illustrates the computed results using traditional and extended Dijkstra algorithms. The comparison was performed for the AGV path planning network to compute from source node A to target node D. In the case of implementation, and multiple AGVs share the same network executing multiple tasks simultaneously. The path is computed using the traditional Dijkstra algorithm utilizing the shortest path. Therefore, the path is computed preferably to avoid passing by many nodes and, at the same time, maintain the shortest distance from source node A to target node D. This solution is designed for multiple AGVs task distribution path planning.

Figure 2. Path-finding results.
a) Traditional Dijkstra algorithm, b) Extended Dijkstra algorithm.

Figure 2. Figure 2. Path-finding results. a) Traditional Dijkstra algorithm, b) Extended Dijkstra algorithm.

The computed path from origin A to destination D shown in Fig. 2 (a) path selected by Dijkstra algorithm using the shortest path but passing by common nodes and heavy traffic nodes which cause high possibilities of collision and deadlock if implemented in multiple AGVs network. Meanwhile for extended Dijkstra algorithm select the appropriate path while maintaining the shortest path without passing by heavy traffic nodes.

There are few setbacks identified in Dijkstra algorithm implementation in multiple AGVs task distribution path planning, especially in long compute time if involved multiple nodes and distance. There are few possibilities of collision and deadlock since the computed path using the Dijkstra algorithm passes through commonly used nodes which refer to high traffic congestion nodes and paths.

Simulation in Wolfram Mathematica shows traditional Dijkstra algorithm comsume 256 MB , 60 seconds, while extended Dijkstra algirithm consume seconds of memroy and computing time respectively for the same network.

Conclusion

The original version of this paper is available on https://www.scientific.net/EI.6.31.pdf

An extended version of Dijkstra algorithm has been proposed in this paper for multiple AGV path planning solutions. The traditional Dijkstra algorithm is used to compute the shortest path between
the source node and target node. However, the extended Dijkstra able to compute faster and consume less memory compared to the traditional Dijkstra algorithm. Furthermore, the extended Dijkstra algorithm reduce the possibilities of collision and facilitate with deadlock prevention in multiple AGVs network. It is also suitable to be used in large network of AGVs for task distribution path planning. The extended algorithm has been validated through an experimental verification using turtlebots by considering the inputs from LiDAR sensor as an additional weight to the network.

References

The original version of this paper is available on https://www.scientific.net/EI.6.31.pdf

E. W. Dijkstra, E. W. Dijkstra, E. W. Dijkstra, and E. W. Dijkstra, A discipline of programming. prentice-hall Englewood Cliffs, 1976.

E. W. Dijkstra, "A note on two problems in connexion with graphs," Numerische mathematik, vol. 1, no. 1, pp. 269-271, 1959.

M. Noto and H. Sato, "A method for the shortest path search by extended Dijkstra algorithm," in Smc 2000 conference proceedings. 2000 ieee international conference on systems, man and cybernetics.'cybernetics evolving to systems, humans, organizations, and their complex interactions'(cat. no. 0, 2000, vol. 3: IEEE, pp. 2316-2320.

Y.-z. Chen, S.-f. Shen, T. Chen, and R. Yang, "Path optimization study for vehicles evacuation based on Dijkstra algorithm," Procedia Engineering, vol. 71, pp. 159-165, 2014.

W. Fink, V. R. Baker, A. J.-W. Brooks, M. Flammia, J. M. Dohm, and M. A. Tarbell, "Globally optimal rover traverse planning in 3D using Dijkstra's algorithm for multi-objective deployment scenarios," Planetary and Space Science, vol. 179, p. 104707, 2019.

W. Fink, V. R. Baker, D. Schulze-Makuch, C. W. Hamilton, and M. A. Tarbell, "Autonomous exploration of planetary lava tubes using a multi-rover framework," in 2015 IEEE aerospace conference, 2015: IEEE, pp. 1-9.

D. Guo et al., "A vehicle path planning method based on a dynamic traffic network that considers fuel consumption and emissions," Science of the Total Environment, vol. 663, pp. 935-943, 2019.

Y. C. Zheng et al., "Study of multi-objective path planning method for vehicles," Environmental Science and Pollution Research, vol. 27, no. 3, pp. 3257-3270, 2020.

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Engineering Innovations, ISSN: 2813-1002, Vol. 6, pp 37-42

doi: 10.4028/p-81wb7g

© 2023 The Author(s). Published by Trans Tech Publications Ltd, Switzerland.

Submitted:
2022-11-21

Revised:
2023-01-20

Accepted:
2023-01-31

Online:
2023-06-21

An Example of the Use of Photogrammetry for Archiving the Shapes of Natural Materials and Creations

The original version of this paper is available on https://www.scientific.net/EI.6.37.pdf

József Udvaros and Ondrej Takáč

Department of Informatics, Faculty of Economics and Informatics, J. Selye University, Komárno, Slovakia

udvarosj@ujs.sk, takaco@ujs.sk

Keywords: Natural materials, creations, photogrammetry, drone.

Abstract

We can find cultural monuments everywhere. Smaller ones are often made from available and cheap natural materials - stones. However, due to the weather, due to their smaller dimensions, are often subject to vandalism, damage caused by careless handling or accidental damage. Sometimes they are just pieces of stone, but they have a deeper meaning in connection with cultural value, and for example by studying them in office spaces in the form of 3D models. In our contribution, we focus on the protection, study and preservation of such cultural and artistic monuments made of natural materials or generally stone natural formations or even large stone or brick surfaces. In our article, we want to focus on the practical feasibility rather than the mathematical background, as we believe that this information is much more useful in practice. We point out that it is advisable to use drones to check the condition of materials.

Introduction

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In Fig. 1 we present an example of the loss of cultural heritage. In the first row on the right, we can see the building as it existed in 2007. After 15 years, only the torso of one corner part remained of it - the bottom row on the right. But the situation is also very similar, for example, in surface or underground mines, where mining activities transform the character of the landscape. By extracting a significant part of the deposit, some of its parts are definitely lost. That's why we try to preserve them in digital form. A big help in our efforts is photogrammetry, where we make significant use of it. Currently, commonly available technical means are already at such a level that they allow their use not only for recreational activities, but can also replace professional means to a significant extent. Today, it would be hard to find a person who does not own a mobile phone or tablet. Photos obtained from these devices allow the creation of such 3D models that are a faithful copy of the real object. Acquiring photos is not only tied to ground activities, but we can also use drone and robot technology to a large extent. The created 3D models are also of great importance in the educational sphere, where the great benefit is not the sculpted model but the created model from a real object [1-4].

Using drones makes it possible to take the right pictures. Radácsi and his colleagues present a method using a drone to go through the shelf system of a decentrum, where the inventory is mapped using photographs, and the images are sent by the drone to a computer for image processing. In the case of image processing, QR code extraction and recognition are performed [5,6]. The method is complicated by the fact that in the center it is difficult to navigate based on the GPS coordinates due to the Faraday cage effect, so the drone's route must be automated, taking into account various aggravating circumstances. This method can be used well to examine the condition of outdoor monuments. With the help of the drone, we can automate the production of high-quality and highresolution images by entering exact GPS coordinates. In the case of image processing, a highresolution 3D model can be created.

Fig. 1 "Nebojsa" mansion in Slovakia. From the left side from the top: The mansion from the postcard from 1899, the mansion from the postcard before 1945, the mansion in 2007, an inscription describing the sale of bricks, the abandonment and deliberate dismantling of the mansion, the rest of the mansion in 2022 [7-10].

Fig. 1. "Nebojsa" mansion in Slovakia. From the left side from the top: The mansion from the postcard from 1899, the mansion from the postcard before 1945, the mansion in 2007, an inscription describing the sale of bricks, the abandonment and deliberate dismantling of the mansion, the rest of the mansion in 2022 [7-10].

Fig. 2 Normal case of stereophotogrammetry on the left and convergent imaging on the right [11].

Fig. 2. Normal case of stereophotogrammetry on the left and convergent imaging on the right [11].

One-frame photogrammetry, also called projective, can be considered the simplest of the photogrammetric methods. It uses a mathematical model of the projective transformation between two planes derived from projective geometry or the central projection of the plane. With the help of stereophotogrammetry, points on the imaged object can be evaluated not only horizontally, but spatially. Currently, the most widely used photogrammetric method is convergent imaging with a general orientation of the image axis. It is a multi-frame method that requires special photogrammetric software for analytical processing. The biggest advantage of convergent imaging, compared to stereophotogrammetry, is the just-mentioned general position of the axes of the camera shots (albeit always in an effort to maintain a sufficient overlap of the images and for the object in the images to occupy the largest possible area), and thus using this method objects of any shape can be measured and dimensions [11].

In creating the 3D models, we have been looking at two areas. The first is the creation of 3D models of large religious buildings and the second is the creation of 3D models of museum artefacts. Both areas have their own specificities that we have to accept. In the case of sacred buildings, there are often cases where part of the building is missing - for example, part of the roof. This is due to the smaller number of photographs that show that part of the building. Here we have to manually attach additional photos. Since these are larger buildings, some details may remain unrecorded [12]. For museum artifacts, the biggest problem is proper lighting. Busts and sculptures are usually not illuminated from all sides evenly. A strong difference in contrast - e.g. a strongly illuminated front part and a weakly illuminated back part can cause flaws on the dark side as well as incorrectly connecting the two parts, which the software starts to process as if separately. It creates a 3D model of the dimly lit part from the photos that capture the dimly lit part and vice versa. These parts are not
connected but are separated and displaced from each other. Eliminating this problem requires a lot of time in post editing [13].

Material and Methods

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In the next text, we would like to point out the practical implementability rather than the mathematical background, as we are of the opinion that this information will be of much better use in practice. As we see in Fig. 3, the first step is to obtain photos of the given object with sufficient coverage. We recommend at least 30 percent. Obtaining photos is not tied to the condition from all sides, but it is important to focus on obtaining photos at least from the point of view from which we want to obtain a partial 3D model. This is also suitable in those cases where we want to create a model from, for example, a rock wall or similar, where it is not realistic to obtain the shape of its opposite part.

Fig. 3 From the left: An example of the layout of the camera overlay when obtaining photos of the entire object; camera position only when obtaining photos from the front of the object and Tie points; object point cloud.

Fig. 3. From the left: An example of the layout of the camera overlay when obtaining photos of the entire object; camera position only when obtaining photos from the front of the object and Tie points; object point cloud.

The first step is the selection and elimination of bad, noisy photos or photos with a significant deviation from the average lighting conditions. These have a significant impact on the quality of the model, so let's try to get the photos in each shot under the same conditions. We will then use photogrammetric software to create a 3D model. There are several software that can be freely available or paid. We used the software Pix4Dmapper Pro ver. 2.0.104-64 bit. This has already proven itself in the past when creating 3D models of museum artifacts. One example is shown in Fig. 4, bottom left row, where this 3D model was created as part of a thesis [14]. Used hardware: Intel(R) Xeon(R) CPU E5-1650 v3 @ 3.50 GHz , RAM: 32GB; GPU: NVIDIA Quadro K4200 (Driver: 9.18.13.4121). Used Camera: Model Name OPPOReno5Z_4.7_4000x2256 (RGB). In the case of good photos, the software will create a model without the need for further editing. All these models presented by us were created in this way. If necessary, it is possible to make further manual adjustments either at the level of Tie points or Point Clouds, e.g. attaching another photo, matching the position of points on individual photos, etc. If the Points Cloud is already generated, the Triangle Mesh will be used to create the model itself. The latter may contain fragments outside the scanned object, e.g. grass or part of the flower planting, which is fine.

Fig. 4 Models of various memorial plaques or engravings on natural stone together with their Google maps coordinates and a bust stored in the Lapidarium museum.

Fig. 4. Models of various memorial plaques or engravings on natural stone together with their Google maps coordinates and a bust stored in the Lapidarium museum.

We can notice that when shooting only half, the distant background behind the subject will create a 2D vertical surface. We see this in Fig. 4 top left. All other models were created from photos taken from all sides. An example of turning the model can be seen in Fig. 5. We can use these methods with great advantage, e.g. when studying the structures of large natural stones and fractures, brick walls or various other objects from the office premises. Thus, it is not always necessary for an expert to go to the site, but e.g. other employees can create a series of photos and the expert will create a model from them. These procedures will similarly find their application in mineralogy.

Fig. 5 3D model of the memorial stone from different viewpoints. The stone is located at Google maps coordinates: . It was installed only after the creation of Google Street View.

Fig. 5. 3D model of the memorial stone from different viewpoints. The stone is located at Google maps coordinates: . It was installed only after the creation of Google Street View.

Photogrammetry is offered as one of the attractive methods in the current field of cultural heritage protection. Even cheaper phones or tablets already have various high-quality cameras that are suitable for this purpose, which eliminates the need for professional equipment. Thus, the necessary equipment is available, cheap and commonly used in everyday life.

With the help of drones, we can also approach places that would be difficult for humans, such as high-rise historic building parts, only from above or from the side or its parts accessible from a height (see Fig. 6). Our method is also supported by Zhou's research, according to him, many buildings reflect local customs, social conditions, economic level, human-earth relations, and have high artistic and tourism development value. In his article, he built a three-dimensional model of the village based on drone tilt photography technology, and revealed the steps and methods of using consumer-grade
drones to extract the landforms and three-dimensional modeling of traditional villages, and offered new opportunities for technical support for the protection of traditional villages. He contributed 7.9% to the repair works of village buildings with his method [15].

Fig. 6 A castle wall and hard-to-reach cornerstones

Fig. 6. A castle wall and hard-to-reach cornerstones

These procedures may also be suitable for students who have a visual non-verbal learning style, but also as project teaching procedures (intuitiveness, trial and error, ...). Photogrammetry uses knowledge from various fields, such as mathematics, geometry, physics, but also professional subjects such as optics, geodesy and, of course, mainly computer science. Therefore, it is also suitable for building subject relationships. Here, of course, we also understand the real connection of theory with practice. We mention secondary school as a suitable age category, where these procedures are of the greatest importance in implementation.

Last but not least, we would like to touch on the time required to create 3D models. The total generation time consists of three parts, namely Time for Initial Processing, Time for Point Cloud and Time for 3D Textured Mesh Generation. Our observations show that there is only a weak correlation between the model creation time and the number of photographs. A much stronger relationship exists between Time to create 3D model and Number of 3D points. These were obtained based on Tie points. In all cases of our measurements, this association showed a power-law regression dependence. However, there is a dependence between the number of photos and the quality of the model. We report the results of our observations in Table 1.

Table 1 The quality of the 3D model and the time required for creation

| 250 photos | 100 photos | 50 photos
Quality of model | Good ! | Good | Enough good *
Time of creation | Approx. 50 min | Approx. 20-30 min | Approx. 10 min

Good for educational purposes. With risk of surface ripple.
! A large number may cause an errors

Conclusion

The original version of this paper is available on https://www.scientific.net/EI.6.37.pdf

In conclusion, we can state that the 3D model created by photogrammetric procedures fully reflects reality. It enables non-abrasive measurements of the dimensions and volumes of real objects, but due to the full color and detail, it also retains its technical, technological and cultural value. It can be used for study and preservation for our future generations. Although such monuments or objects may disappear from our daily life, considering the trend of today, we can say that the 3D model remains in the digital world even after the destruction of the real object and can still be available in the form of a model. We present an effective method using drones, which can be well used to examine the condition of outdoor monuments. With the help of drones, we have the opportunity to automate by
entering exact GPS coordinates, and we can take high-quality and high-resolution images. In the case of image processing, a high-resolution 3D model can be created. The method does not require a drone with special features, just a consumer-grade drone, but you must have a high-resolution camera.

Acknowledgments

The original version of this paper is available on https://www.scientific.net/EI.6.37.pdf

"This publication was funded by the Pallas Athéné Domus Meriti Foundation."

References

The original version of this paper is available on https://www.scientific.net/EI.6.37.pdf

O. Takáč, D. Hrubý, V. Cviklovič. Possibilities of navigation of mobile agricultural robots on the principle of the geometrical objets detections. 1, Bucharest: UNIV Agricultural Sciences & Veterinary Medicine Bucharest, 2011. p. 206-208. ISSN: 2284-7995.

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The original version of this eBook is available on https://www.scientific.net/book/engineering-innovations-vol-6/978-3-0364-1337-2

Engineering Innovations, ISSN: 2813-1002, Vol. 6, pp 43-48

doi: 10.4028/p-hui96q

© 2023 The Author(s). Published by Trans Tech Publications Ltd, Switzerland.

Submitted:
2022-11-07

Revised:
2023-01-15

Accepted:
2023-01-31

Online:
2023-06-21

Smart Contracts in Construction from Management to Materials: Acting as a Bridge between the Two

The original version of this paper is available on https://www.scientific.net/EI.6.43.pdf

Norbert Forman and József Udvaros

1Faculty of Finance and Accountancy, Department of Business Information Technology, Budapest

Business School, Budapest, Hungary

forman.norbert@uni-bge.hu, udvaros.jozsef@uni-bge.hu

Keywords: blockchain, smart contract, decentralised construction, data privacy, algorithm, material quality, material overview

Abstract

The goal of this article is to demonstrate how smart contracts can be leveraged via blockchain technologies. This will enable us to create a bridge between management and the materials used in the construction industry. It is all too common for construction projects to encounter a number of problems along the way, including a lack of communication between builders, laborers, and vendors. As a result of these issues, the proposed timelines and quality of construction end up being compromised. Oftentimes, stakeholders involved in the project are not quite aware of the quality and quantity of materials that will be used to complete the project. It is precisely this problem that we aim to solve through the use of blockchain technology. As a result of the use of blockchain technology, smart contracts offer transparency and trust to their stakeholders. In order to achieve our primary objective, we intend to leverage the advantages of blockchain. This will enable us to ensure clear visibility of construction materials, deadlines, subcontractors and any other elements that are part of a construction project.

Introduction

The original version of this paper is available on https://www.scientific.net/EI.6.43.pdf

The key to success is understanding the details and small pieces of a project and this is not different in large-scale construction projects either. These projects tend to take a lot of time. They bring together a lot of people in different forms such as employees or subcontractors. Communication is often not the most effective among them since the project is not always clearly defined and the requirements are not visible to all stakeholders. The smallest pieces in a construction project are the materials, which basically ensure the overall quality of the construction. If we want to harness trust and transparency in a construction project, we need to ensure that the materials used meet the necessary quality requirements. Our objective is to build a model that will leverage smart contracts supported by blockchain technology in order to create transparency and trust among its stakeholders.

In order to obtain a deeper understanding of the struggles that the construction industry faces, we conducted in-depth interviews as part of this research. The goal of the research is to propose, as a possible solution, a model that can serve as a basis for addressing each of these challenges.

Literature Review

The original version of this paper is available on https://www.scientific.net/EI.6.43.pdf

Throughout the life cycle of a construction project, "information exchange across disciplines" needs to be supported by appropriate techniques, Celeck and colleagues argue in their article. New possibilities for data and process integration have emerged as a result of recent advances in Building Information Modelling (BIM), which uses open data formats, process mapping, and terminology. BIM data must be used consistently and traceably across multiple disciplines in construction projects. An example of this would be data objects or collections of attributes that are associated with a discipline or organization. Moreover, the information would be accompanied by a description of the transaction. Blockchain technology can be used to describe the discipline of issuance, the version, and the responsibility and liability of BIM objects. Blockchains provide stakeholders with enhanced security and can increase trust in BIM objects. To support the provenance of BIM data in construction projects, this paper proposes using Blockchain-based model information exchange. Stakeholders can
share BIM data more confidently, reduce costs, and mitigate risk with their proposed approach. This solution has been proven to identify competence levels in a real-world bridge construction scenario and to improve the implementation of BIM by testing the solution in a real-world bridge construction scenario [1].

Using BIM as an integrated smart contract progress payment administration system, Sonmez and collaborators present a novel system for the administration of progress payments based on the integration of smart contracts and building information models, where they use the real world and the blockchain to link both. During a survey of construction professionals, it was determined that the proposed system would significantly improve traditional payment procedures by speeding up the process. In addition, it would decrease the risk of disputes over progress payments, especially for lump sum projects. Researchers in the future can also investigate semi-autonomous BIM integrated smart contract systems to increase the adoption of BIM and smart contract technologies for construction progress payments [2].
[3] explores issues regarding BIM and design change management in the context of analyzing, managing, and visualizing design change. This paper discusses design change management using BIM in a concise manner. In addition, the authors briefly discuss the current developments in native BIM tools, as well as the functionality dedicated to design change management. This is tracking and analyzing design changes based on BIM models. A number of recent developments in design visualization are illustrated by a number of examples. Using BIM to maximize its potential can be achieved by automating or semi-automating analyses.

Blockchain Technology Overview. Blockchains have been described as distributed ledgers or append-only logs [4]. Its key feature is that it is distributed, meaning a single entity cannot add data to it from a centralized database. A chain of blocks is created using a cryptographic method. A consensus protocol is used when additional information needs to be added, which all peers need to agree on. Once an information block has been created, the so-called Merkle tree cannot be changed by cryptographic hashing. Essentially, the hash table is tamper-proof and transparent, since it contains a hash of the previous block for every block.

Consensus protocols will allow all nodes to agree on what information needs to be changed. For our model, we will focus on just a few essential protocols [5-7]. To begin with, there is proof of authority. To remain trustworthy, nodes must reveal their identities and register with a public notary database. Another aspect of a blockchain system is proof of work [8], which is the basis of most of them, including Bitcoin. An "zero-knowledge-proof" cryptographic puzzle must be solved to link two blocks collectively [9]. Every time the puzzle is solved, the difficulty increases, requiring more and more computational power to solve the puzzle. There are two additional protocols to be considered: proof of time elapsed and proof of activity [7,10]. Our model will depend on these protocols to prove the time spent on a particular task, although they are not as essential as the previous ones.

Blockchains and Smart Contracts. Due to their inherent security and tamper-proof capabilities, smart contracts are used in various sectors around the world. Within the logistics industry, especially supply chain management, blockchain-centric solutions are used to communicate with distributed databases. Cybersecurity is today's biggest challenge for any industry. There is a great deal of concern about data theft and leaks. A smart contract may contain sensitive data, which could provide an advantage to anyone with access to it. Cybercriminals may also access large sums of money through these smart contracts, making them even more vulnerable. In comparison to other IT systems, blockchain technology is more resilient to cyberattacks because it is based on append-only permissions and has a distributed and resilient nature [11].

A smart contract can handle financial transactions between two parties (such as a currency exchange) based on the cryptographic features of blockchains [12].

Smart Contracts Models based on Blockchains. A similar model to the one we are proposing can be found in many other areas as well. This is, for example, the software industry [13], the electricity
bill business [14] and the utility business as a whole [15]. The construction industry faces similar challenges to these other industries, so we will propose a comparable solution.

Model Implementation

The original version of this paper is available on https://www.scientific.net/EI.6.43.pdf

Problems that Smart Contract would solve in the Construction Industry. The following are the primary advantages of leveraging a smart contract:

It is all too common for information to be lost in construction projects. As a result, blockchain's distributed ledger can preserve life cycle information, which means warranties, certifications, and materials won't be lost. Regular evaluations of construction materials would ensure that maintenance is carried out correctly and on time.

Due to blockchain's decentralization, contracts are visible and searchable. In case of a problem, the contract can easily be traced back to its origin, preventing extra oversight.

We can easily verify that the quality of materials and work used by subcontractors meets government regulations, due to the open nature of smart contracts.

Using milestones in small contracts ensures that we only pay for what has been completed.

Due to the transparency of smart contracts, all parties are able to monitor the pieces and make suggestions and improvements where needed. All changes must also be approved by all stakeholders before the smart contract can be modified.

In complex construction projects, smart contracts can ensure that materials are efficiently and correctly used at all steps of the process.

Methodology

The original version of this paper is available on https://www.scientific.net/EI.6.43.pdf

We conducted in-depth interviews with 16 individuals from different positions within the Hungarian construction industry, in order to achieve a better understanding of the struggles and needs of the industry. During these interviews, our primary objective was to determine whether or not our proposed model was something they would require in their daily processes. This was in order to accomplish their goals. We also sought to ensure that our proposed models would appear to fit into their daily activities, as a secondary objective. In order to meet the above-mentioned objectives, each interview was conducted online and focused on the above-mentioned goals. After the interviews were conducted, we transcribed the conversations and analyzed the data to identify patterns, themes, and insights based on the data that was collected. As a result of these interviews, the following topics arose: budget overruns, schedule delays, quality issues, changes in the scope of work, and disputes with various parties. A high level of transparency within the project roadmap was viewed favorably by all participants, as they considered it to be beneficial for the project as a whole. In addition, they consider trust to be one of the main pillars of a successful project, since it is the essence of all communication.

The proposed model acts as a bridge between multiple stakeholders within the construction industry rather than replacing existing workflows. Trust and transparency will be ensured. This model shouldn't negatively affect the team's daily process; it should actually improve performance and visibility. It is possible to ensure the success of a construction project by using the right materials in the right places. Additionally, maintenance costs can be reduced. Our proposed model consists of the following steps:

Smart contracts will connect the two parties. Sources and requirements for construction project funds will be loaded by the customer.

Together with subcontractors or its own employees, the contracted party will further define the construction project and divide it into smaller pieces. This involves specifying the quality and quantity of materials used.

Each element of the construction project will require cryptographic credentials. The project timeline, materials used, subcontractors, and known limitations would be included.

The parties involved must set joint milestones and ensure that the materials used meet the requirements of the law and the customer.

Each milestone will need to have acceptance criteria as this will ensure payments are made to the contractors and subcontractors.

In a smart contract, a milestone represents a payment transaction. It can only be initiated when both hashes extracted from the defined tasks are a match and the stakeholders accept the milestone.

All the above mentioned steps are presented in the below diagram (Fig. 1):

Fig. 1 The steps in our proposed model

Fig. 1. The steps in our proposed model

Algorithm

The original version of this paper is available on https://www.scientific.net/EI.6.43.pdf

Using the model above, the following pseudocode shows how our smart contract with blockchain would work in a real-world application. This pseudocode shows the major functionality of a smart contract that triggers a transaction and explains the key elements that should be included in a smart contract

create constructionIndustryPaymentContract {
define contractor(name, fundSource)
define theContracted(name, fundSource)
define workers(names, roles)
define subContractors(contractors, roles)

define task(ID, requirement, materials, acceptanceCriteria, employee)
function initiateFundTransfer(contractor, subContractors, task) {

    If contractor.acceptanceCriteria.meet and taks.acceptanceCriteria.pass
            amount = taks.acceptanceCriteria.pass.totalTimeSpent
        startFundTransfer(contractor.fundSource, theContracted.fundSource, amount)
    end
}
function startFundTransfer(contractorFund, theContractedFund, amount) {
    subtract(amount, contractorFund)
    pay(amount, theContractedFund)
}
}

Conclusions

The original version of this paper is available on https://www.scientific.net/EI.6.43.pdf

A smart contract's quintessential part is to create trust between its stakeholders. Due to its transparency, blockchain creates an environment of trust, which is beneficial to any project, not just construction projects. In large-scale construction not all stakeholders have the necessary overview of the project to ensure that all used materials meet the requirements and demands of the contract and local laws. By leveraging smart contracts we solve exactly this issue and due to the contract's transparent nature we can always check and be sure that the used materials meet the necessary requirements. In addition to that, the in-depth interviews confirm the need for a similar model to be implemented within the construction sector as well.

Since a construction project is complicated enough on its own we do not wish to change the existing processes, only to support them. With our developed model and pseudocode, we just provide an additional layer of oversight for the project. It is imperative that our proposed model can be seamlessly integrated into the current workflow of the project.

We are planning to investigate different aspects of the construction industry in the future. We will tailor our model to each key aspect and not be too general as it is now. We acknowledge that human decision making plays a key role in establishing trust within a group, so our model does not aim to eliminate this aspect. We will simply strive to maximize trust levels.

References

The original version of this paper is available on https://www.scientific.net/EI.6.43.pdf

Y. Celik, I. Petri, M. Barati. Blockchain supported BIM data provenance for construction projects. Computers in Industry, 2023. 144, art. no. 103768. DOI: 10.1016/j.compind.2022.103768

R. Sonmez, S. Ahmadisheykhsarmast, A.A. Güngör. BIM integrated smart contract for construction project progress payment administration. Automation in Construction, 2022. 139, art. no. 104294. DOI: 10.1016/j.autcon.2022.104294

M. Juszczyk, A. Tomana, M. Bartoszek. Current Issues of BIM-based Design Change Management, Analysis and Visualization. Procedia Engineering, 2016. 164, pp. 518-525. DOI: 10.1016/j.proeng.2016.11.653

D. Yaga, P. Mell, N. Roby, K. Scarfone. Blockchain technology overview. US National Institute of Standards and Technology, 2018. . 6028/nist.ir.8202.

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D Calvaresi, J-P. Calbimonte, A. Dubovitskaya, V. Mattioli, J-G. Piguet, M. Schumacher. The good, the bad, and the ethical implications of bridging blockchain and multi-agent systems. Information (Switzerland) 2019.10(12). .

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Keywords Index

A

Agv Network Low Latency Extended Dijkstra Algorithm with Multiple Linear Regression for Optimal Path Planning of Multiple AGVs Network

Air Bubbles The Impact of Gas-Liquid Dispersed Flow on Heat Exchanger Performance with Improvement Using CuO Nanofluid

Algorithm Smart Contracts in Construction from Management to Materials: Acting as a Bridge between the Two

B

Blockchain Smart Contracts in Construction from Management to Materials: Acting as a Bridge between the Two

Brake Regeneration Research Effectiveness of Regeneral Brake Energy on Toyota Prius Vehicles by Matlab/Simulink

C

Collision Low Latency Extended Dijkstra Algorithm with Multiple Linear Regression for Optimal Path Planning of Multiple AGVs Network

Copper Oxide Nanofluid The Impact of Gas-Liquid Dispersed Flow on Heat Exchanger Performance with Improvement Using CuO Nanofluid

Creations An Example of the Use of Photogrammetry for Archiving the Shapes of Natural Materials and Creations

D

Data Privacy Smart Contracts in Construction from Management to Materials: Acting as a Bridge between the Two

Deadlock Low Latency Extended Dijkstra Algorithm with Multiple Linear Regression for Optimal Path Planning of Multiple AGVs Network

Decentralised Construction Smart Contracts in Construction from Management to Materials: Acting as a Bridge between the Two

Dijkstra Algorithm Low Latency Extended Dijkstra Algorithm with Multiple Linear Regression for Optimal Path Planning of Multiple AGVs Network

Dispersion The Impact of Gas-Liquid Dispersed Flow on Heat Exchanger Performance with Improvement Using CuO Nanofluid

Drone An Example of the Use of Photogrammetry for Archiving the Shapes of Natural Materials and Creations

Dynamic Recovery Research Effectiveness of Regeneral Brake Energy on Toyota Prius Vehicles by Matlab/Simulink

G

Green Vehicle Research Effectiveness of Regeneral Brake Energy on Toyota Prius Vehicles by Matlab/Simulink

H

Heat Transfer The Impact of Gas-Liquid Dispersed Flow on Heat Exchanger Performance with Improvement Using CuO Nanofluid

Hybrid Vehicle Research Effectiveness of Regeneral Brake Energy on Toyota Prius Vehicles by Matlab/Simulink

L

Linear Regression Low Latency Extended Dijkstra Algorithm with Multiple Linear Regression for Optimal Path Planning of Multiple AGVs Network

M

Material Overview Smart Contracts in Construction from Management to Materials: Acting as a Bridge between the Two

Material Quality Smart Contracts in Construction from Management to Materials: Acting as a Bridge between the Two

N

Natural Materials An Example of the Use of Photogrammetry for Archiving the Shapes of Natural Materials and Creations

P

Photogrammetry An Example of the Use of Photogrammetry for Archiving the Shapes of Natural Materials and Creations

S

Smart Contract Smart Contracts in Construction from Management to Materials: Acting as a Bridge between the Two

Authors Index

C

Chek, L. Low Latency Extended Dijkstra Algorithm with Multiple Linear Regression for Optimal Path Planning of Multiple AGVs Network

F

Forman, N. Smart Contracts in Construction from Management to Materials: Acting as a Bridge between the Two

H

Hasan, B. The Impact of Gas-Liquid Dispersed Flow on Heat Exchanger Performance with Improvement Using CuO Nanofluid

Hathal, M. The Impact of Gas-Liquid Dispersed Flow on Heat Exchanger Performance with Improvement Using CuO Nanofluid

L

Le, M. Research Effectiveness of Regeneral Brake Energy on Toyota Prius Vehicles by Matlab/Simulink

M

Majdi, H. The Impact of Gas-Liquid Dispersed Flow on Heat Exchanger Performance with Improvement Using CuO Nanofluid

T

Takáč, O. An Example of the Use of Photogrammetry for Archiving the Shapes of Natural Materials and Creations

U

Udvaros, J. An Example of the Use of Photogrammetry for Archiving the Shapes of Natural Materials and Creations; Smart Contracts in Construction from Management to Materials: Acting as a Bridge between the Two

V

Van, T. Research Effectiveness of Regeneral Brake Energy on Toyota Prius Vehicles by Matlab/Simulink