CFD-modeling of fluid flow in Ansys Fluent using Python-based code for automation of repeating calculations

Vyacheslav Papkov (), Nikita Shadymov () and Dmitry Pashchenko
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Vyacheslav Papkov: Samara State Technical University, Molodogvardeiskaya 244 St, Samara 443100, Russia
Nikita Shadymov: Samara State Technical University, Molodogvardeiskaya 244 St, Samara 443100, Russia
Dmitry Pashchenko: Samara State Technical University, Molodogvardeiskaya 244 St, Samara 443100, Russia

International Journal of Modern Physics C (IJMPC), 2023, vol. 34, issue 09, 1-15

Abstract: CFD-modeling for numerical investigation is used in a wide range of applied tasks, e.g. in fluid mechanics. To better understand the effect of operational parameters on the final results, some tasks are associated with carrying out monotonous, repetitive calculations for a wide range of operational parameters such as velocity, flow direction and temperature. In this paper, a Python-based code for automation of the repeating calculations in CFD-modeling was developed and described. The automation code was tested for CFD-modeling in Ansys Fluent for two flow dynamic tasks: a simple 2D-geometry — NACA0018 airfoil, and a complex 3D-geometry — packed bed with heat transfer. Three different computers with various computational power were used for the comparison. The results of CFD-modeling were compared with the experimental data. The efficiency of using Python-based code was evaluated through comparison with the results of manual (without automation) calculation. It was established that the application of the Python-based code does not affect the accuracy of numerical results. At the same time, utilization of the Python-based code can save up to 25% of computation time for the simple 2D-geometries with a moderately low number of elements in the mesh, and up to 15% for the complex 3D-geometries with a number of elements in several millions. The compiled Python-based code is attached as supplementary material to this paper.

Keywords: CFD; Python; Ansys Fluent; automation (search for similar items in EconPapers)
Date: 2023
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DOI: 10.1142/S0129183123501140

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