Paper Titles
Competition between Formation and Decomposition of Solid Solution during High Pressure Torsion: A Review
p.15
Simplifying the Methodology to Estimate the Separation Time of a Particle at 1-g under Unsteady Conditions
p.29
Jump Behavior of Individual Atoms in Liquid Pb Using Molecular Dynamics Simulation
p.39
Linking Rheology to Morphology in Lava Flows: A Theoretical Framework for Power-Law and Prefractal Scaling
p.53
Design Automation and Optimization of Micro Cross-Junctions for Droplet Generation Using CFD and Machine Learning Approaches
p.67
Technology Roadmap: Trends in the Use of Bio-Oil in Catalytic Refining Processes
p.77
Thermohydraulic Analysis of a Flat-Plane Solar Collector with Fractal Patterns and Allometric Scaling Using CFD
p.97
Investigation of the Dielectric Properties of Cu Based Microwave Absorbers
p.113
Generation of Nano Phase in Al-Sr Eutectic Alloy
p.123

Design Automation and Optimization of Micro Cross-Junctions for Droplet Generation Using CFD and Machine Learning Approaches

Article Preview

Abstract:

This work numerically studies the water-in-oil (W/O) droplet formation inside a flow focusing on the micro junction formed by rectangular channels with dimensions of 390 × 190 μm2 using OpenFoam. An automatic algorithm was developed to assess the effect of key parameters such as water viscosity, restriction ratio and water mass flow rate ratio on the droplet size. A total of 96 simulations, with different parameter combinations, were conducted to train a Machine Learning (ML) algorithm capable of predicting the droplet dimensions based on the key parameters mentioned. The ML algorithm was also compared to a Newtonian-based optimization method, where the geometry is iteratively adjusted to produce droplets of a fixed size. Results reveal that both methods appear valid in the prediction of droplet dimensions.

You might also be interested in these eBooks
Advances in Mass and Thermal Transport in Engineering Materials VI View Preview

Info:

Periodical:

Defect and Diffusion Forum (Volume 445)

Pages:

67-76

DOI:

https://doi.org/10.4028/p-GYtH6k

Citation:

Cite this paper

Online since:

December 2025

Authors:

Filippo Azzini*, Beatrice Pulvirenti, Claudia Naldi, Giulia Martino, Gian Luca Morini

Keywords:

CFD, Drop Formation, Machine Learning, Microfluidic, Microjunction, Numerical Simulations

Export:

RIS, BibTeX

Price:

Permissions CCC:

Request Permissions

Permissions PLS:

Request Permissions

Сopyright:

© 2025 Trans Tech Publications Ltd. All Rights Reserved

Share:

Citation:

* - Corresponding Author

References

[1] S. Gimondi, H. Ferreira, R.L. Reis, N.M. Neves, Microfluidic devices: a tool for nanoparticle synthesis and performance evaluation. ACS Nano. 17 (2023) 14205–14228.

DOI: 10.1021/acsnano.3c01117

Google Scholar

[2] C. Qi, T. Zhou, X. Wu, K. Liu, L. Li, Z. Liu, et al., Micro-nano-fabrication of green functional materials by multiphase microfluidics for environmental and energy applications. Green Energy Environ. (2023).

DOI: 10.1016/j.gee.2023.05.012

Google Scholar

[3] Y. Zhang, Y. Song, Z. Weng, J. Yang, L. Avery, K.D. Dieckhaus, et al., A point- of-care microfluidic biosensing system for rapid and ultrasensitive nucleic acid detection from clinical samples. Lab Chip. 23 (2023) 3862–3873.

DOI: 10.1039/d3lc00372h

Google Scholar

[6] K. I. Sotowa et al. "Droplet formation by the collision of two aqueous solutions in a microchannel and application to particle synthesis". Chem. Eng. Technol. 30 (2007) 383–388.

DOI: 10.1002/ceat.200600345

Google Scholar

[7] B. Ahmed, D. Barrow, and T. Wirth. "Enhancement of reaction rates by segmented fluid flow in capillary scale reactors". Adv. Synth. Catal. 348 (2006) 1043–1048.

DOI: 10.1002/adsc.200505480

Google Scholar

[8] Zhun Lin et al. "Application of microfluidic technologies on COVID-19 diagnosis and drug discovery". Acta Pharmaceutica Sinica B. 13.7 (2023) 2877–2896.

DOI: 10.1016/j.apsb.2023.02.014

Google Scholar

[9] M. A. Northrup et al. "DNA amplification with a microfabricated reaction chamber". In: Transducers '93: Digest of Technical Papers, Proceedings of the 7th International Conference on Solid-State Sensors and Actuators. Yokohama, Japan, 1993.

Google Scholar

[10] S. Wiedemeier et al. "Parametric studies on droplet generation reproducibility for applications with biological relevant fluids". Engineering in Life Sciences 17.12 (2017) 1271–1280.

DOI: 10.1002/elsc.201700086

Google Scholar

[11] A. Lashkaripour, C. Rodriguez, N. Mehdipour, et al. "Machine learning enables design automation of microfluidic flow-focusing droplet generation". Nature Communications. 12 (2021) 25.

DOI: 10.1038/s41467-020-20284-z

Google Scholar

[14] P. Zhu, L. Wang, Passive and active droplet generation with microfluidics: a review. Lab on a Chip. 17 (2017) 34–75.

DOI: 10.1039/c6lc01018k

Google Scholar

[15] B. Rostami, G.L. Morini, Generation of Newtonian and non-Newtonian droplets in silicone oil flow by means of a micro cross-junction. International Journal of Multiphase Flow. 105 (2018) 202–216.

DOI: 10.1016/j.ijmultiphaseflow.2018.03.024

Google Scholar

[16] F. Azzini, B. Pulvirenti, M. Rossi, G.L. Morini, Squeezing Droplet Formation in a Flow-Focusing Micro Cross-Junction. Micromachines. 15 (2024) 339.

DOI: 10.3390/mi15030339

Google Scholar