Parametric Analysis of Reynolds Number Effects on Wake Region Characteristics in LBM-Based Fluid Flow Simulations
DOI:
https://doi.org/10.71131/j7cd2273Abstract
This study investigates the fundamental fluid dynamics of steady-state laminar flow past a circular cylinder using the Lattice Boltzmann Method (LBM) with a D2Q9 lattice arrangement. Developed using a vectorized Pythonic approach, the numerical solver employs the Bhatnagar-Gross-Krook (BGK) collision operator to resolve the mesoscopic particle distribution functions. The research focuses on the flow characteristics at low Reynolds numbers (Re) ranging from 20 to 70 to ensure stable convergence toward steady-state conditions. Results demonstrate that the LBM effectively captures critical hydrodynamic phenomena, including stagnation points, flow acceleration due to blockage effects, and the development of symmetric wake regions. Analysis reveals a linear correlation between increasing Reynolds numbers and the longitudinal elongation of the recirculation zone, confirming the solver's ability to maintain numerical stability and momentum conservation without the computational expense of conventional pressure-velocity coupling. These findings validate the use of open-source computational tools for rigorous fluid flow analysis and provide a robust dataset for future integration with multi-scale modeling and machine learning frameworks, such as Physics-Informed Neural Networks (PINNs).
Keywords:
Lattice Boltzmann Method , CFD, Python, Laminar Flow, Reynolds NumberDownloads
References
Ataei, M., & Salehipour, H. (2024). XLB: A differentiable massively parallel lattice Boltzmann library in Python. Computer Physics Communications, 300. https://doi.org/10.1016/j.cpc.2024.109187
Bahiraei, M., Naseri, M., & Monavari, A. (2022). Thermal-hydraulic performance of a nanofluid in a shell-and-tube heat exchanger equipped with new trapezoidal inclined baffles: Nanoparticle shape effect. Powder Technology, 395, 348–359. https://doi.org/10.1016/j.powtec.2021.09.009
Banh, Q. N., Truong, P. D., & Ho, M. T. (2025). Multi-objective optimization of water-assisted injection molding using machine learning and CFD simulations. Results in Engineering, 28. https://doi.org/10.1016/j.rineng.2025.107981
Benemerito, I., McCullough, J., Narracott, A., Coveney, P. V., & Marzo, A. (2025). Comparison of Navier-Stokes and lattice Boltzmann solvers for subject-specific modelling of intracranial aneurysms. Computers in Biology and Medicine, 197. https://doi.org/10.1016/j.compbiomed.2025.111050
da Silva, M. V. S. (2026). Performance evaluation of commercial pumps in turbine mode: A hybrid experimental and numerical approach in python. Flow Measurement and Instrumentation, 107. https://doi.org/10.1016/j.flowmeasinst.2025.103098
Dadda, A., Boujoudar, M., Houran, N., Messaoudi, H., Asbik, M., Haddou, A., & Arshad, A. (2025). CFD-ANN-based model for parametric analysis of segmented plate-fin heat sinks: Exploring heat transfer and pressure drop trade-offs. International Journal of Thermal Sciences, 218. https://doi.org/10.1016/j.ijthermalsci.2025.110109
Fox, B., Wu, K. C. H., Ma, S., & Wan, S. Y. M. (2024). A CFD simulation platform for surface finishing processes in advanced manufacturing. Advances in Engineering Software, 196. https://doi.org/10.1016/j.advengsoft.2024.103716
Ghafori, H. (2026). Hybrid ANN-CFD modeling of powder sedimentation in pneumatic conveying systems. Powder Technology, 471. https://doi.org/10.1016/j.powtec.2025.122054
Ha, T. J., Kang, D. K., Min, J. H., Park, J. W., & Shin, J.-H. (2026). Optimization of heater configurations in single-phase immersion cooling systems using CFD-validated artificial neural networks. Case Studies in Thermal Engineering, 82, 108122. https://doi.org/10.1016/j.csite.2026.108122
Ho, M. T., Dang, M. S., Truong, P. D., Vo, T. N. A., Tran, A. S., & Banh, Q. N. (2026). CFD-based and machine learning-assisted optimization of screw geometry in food extrusion processes. Results in Engineering, 30. https://doi.org/10.1016/j.rineng.2026.109975
Kummerländer, A., Bukreev, F., Teutscher, D., Dorn, M., & Krause, M. J. (2025). Optimization of single node load balancing for lattice Boltzmann method on heterogeneous high performance computers. Journal of Parallel and Distributed Computing, 206. https://doi.org/10.1016/j.jpdc.2025.105169
Kummerländer, A., Tur, B., Haase, M., Bukreev, F., Döllinger, M., Krause, M. J., & Kniesburges, S. (2026). Efficient fluid structure interaction simulation of vocal fold oscillations using a homogenized Lattice Boltzmann Method. Computer Methods in Applied Mechanics and Engineering, 457, 119009. https://doi.org/10.1016/j.cma.2026.119009
Mansouri, K., Zobiri, O., Atia, A., & Arıcı, M. (2024). A python implementation based lattice Boltzmann method for thermal behavior analysis in silicon carbide MOSFET. Micro and Nanostructures, 187. https://doi.org/10.1016/j.micrna.2024.207769
Najafi Khaboshan, H., Jaliliantabar, F., Abdullah, A. A., Panchal, S., & Azarinia, A. (2024). Parametric investigation of battery thermal management system with phase change material, metal foam, and fins; utilizing CFD and ANN models. Applied Thermal Engineering, 247. https://doi.org/10.1016/j.applthermaleng.2024.123080
Oubnaki, H., Mounir, I., Mounir, B., Farchi, A., & Sak, A. (2026). Integration of CFD and machine learning for vehicle cabin thermal management using innovative materials. Results in Engineering, 30. https://doi.org/10.1016/j.rineng.2026.110214
Pasa, D., Ranjan, K., Rout, S. K., Biswal, G., & Meshram, G. S. (2026). CFD-driven machine learning models for predicting flow and thermal characteristics in brazed plate heat exchanger. International Communications in Heat and Mass Transfer, 175(P3). https://doi.org/10.1016/j.icheatmasstransfer.2026.111252
Santana, H. M., Vaz, J. R. P., & Lins, E. F. (2026). Python-based GUI for automated geometry generation and CFD simulation setup of Wageningen B-series propellers. Ocean Engineering, 356(P1). https://doi.org/10.1016/j.oceaneng.2026.125217
Wang, Q. W., Chen, G. D., Xu, J., & Ji, Y. P. (2010). Second-law thermodynamic comparison and maximal velocity ratio design of shell-and-tube heat exchangers with continuous helical baffles. Journal of Heat Transfer, 132(10), 101801. https://doi.org/10.1115/1.4001755
Wang, S., Wen, J., Yang, H., Xue, Y., & Tuo, H. (2014). Experimental investigation on heat transfer enhancement of a heat exchanger with helical baffles through blockage of triangle leakage zones. Applied Thermal Engineering, 67(1–2), 122–130. https://doi.org/10.1016/j.applthermaleng.2014.03.017
Wen, J., Gu, X., Wang, M., Wang, S., & Tu, J. (2017). Numerical investigation on the multi-objective optimization of a shell-and-tube heat exchanger with helical baffles. International Communications in Heat and Mass Transfer, 89, 91–97. https://doi.org/10.1016/j.icheatmasstransfer.2017.09.014
Zhai, Y., Yang, X., & Chen, S. (2026). A CFD-informed machine learning framework for generalized ship resistance prediction under wind and wave conditions. Ocean Engineering, 353. https://doi.org/10.1016/j.oceaneng.2026.124759