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All Journal ⁠International Journal of Sustainable Social Culture, Science Technology, Management, and Law Humanities
Reinaldo Evan Audrey
Universitas Katolik Widya Karya
Humanities Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Engineering Environmental Science

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Parametric Analysis of Reynolds Number Effects on Wake Region Characteristics in LBM-Based Fluid Flow Simulations Aulia Rahman; Putra Dwiantoko; Novan Habiburrahman; Djarot Winoto; Ahmad Robittah; Reinaldo Evan Audrey
⁠International Journal of Sustainable Social Culture, Science Technology, Management, and Law Humanities Vol. 3 No. 1 (2026)
Publisher : Universitas Kristen Cipta Wacana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.71131/j7cd2273

Abstract

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).
Co-Authors Ahmad Robittah Aulia Rahman Djarot Winoto Novan Habiburrahman Putra Dwiantoko