Analysis of Key Parameters and Mesh Optimization in Computational Fluid Dynamics Using Open FOAM

Syed Muhammad  Asif

Authors

Syed Muhammad Asif Queen Mary University of London

Keywords:

Open FOAM, Computational Fluid Dynamics (CFD), mesh quality, mesh size, total pressure, static pressure, lift coefficient, drag coefficient, simulation validation, mesh optimization.

Abstract

The aim of this report is to display and explain the results obtained using open source Computational Fluid Dynamics solver i.e. Open FOAM, when few key parameters were computed and an in-depth comparison is made between different mesh sizes and their quality. Few key parameters that were examined included total pressure, static pressure, lift coefficients and drag coefficients. This report also discusses the validity of the results that were obtained and goes on to deduce the mesh that is favorable for this particular simulation.

References

An introduction to Computational Fluid Dynamics, The Finite Volume Method, Second edition, by H K Versteeg & W Malalasekera.

Queen Mary University of London, DEN331 Computer Aided Engineering in Fluids and Solids, Computational lab assignment on CFD hand-out.

Hoppe, H., DeRose, T., Duchamp, T., McDonald, J., & Stuetzle, W. (1993, September). Mesh optimization. In Proceedings of the 20th annual conference on Computer graphics and interactive techniques (pp. 19-26).

Cignoni, P., Montani, C., & Scopigno, R. (1998). A comparison of mesh simplification algorithms. Computers & Graphics, 22(1), 37-54.

Wang, Y., Pang, Q., Li, M., & Liu, Y. (2024). Comparative Study of Algorithms for the Conversion From Surface Mesh Models to Volume Mesh Models. Advances in Civil Engineering, 2024(1), 4669855.

Allaire, G., Dapogny, C., & Frey, P. (2014). Shape optimization with a level set based mesh evolution method. Computer Methods in Applied Mechanics and Engineering, 282, 22-53.

Secco, N. R., Kenway, G. K., He, P., Mader, C., & Martins, J. R. (2021). Efficient mesh generation and deformation for aerodynamic shape optimization. AIAA Journal, 59(4), 1151-1168.

Wang, C., Zhao, Z., Zhou, M., Sigmund, O., & Zhang, X. S. (2021). A comprehensive review of educational articles on structural and multidisciplinary optimization. Structural and Multidisciplinary Optimization, 1-54.

Keshavarzzadeh, V., Alirezaei, M., Tasdizen, T., & Kirby, R. M. (2021). Image-based multiresolution topology optimization using deep disjunctive normal shape model. Computer-Aided Design, 130, 102947.

Sushma, M. B., Roy, S., & Maji, A. (2022). Exploring and exploiting ant colony optimization algorithm for vertical highway alignment development. Computer‐Aided Civil and Infrastructure Engineering, 37(12), 1582-1601.

Lin, B., Chen, H., Yu, Q., Zhou, X., Lv, S., He, Q., & Li, Z. (2021). MOOSAS–A systematic solution for multiple objective building performance optimization in the early design stage. Building and Environment, 200, 107929.

Sanil, G., Prakash, K., Prabhu, S., Nayak, V. C., & Sengupta, S. (2023). 2d-3d facial image analysis for identification of facial features using machine learning algorithms with hyper-parameter optimization for forensics applications. IEEE Access, 11, 82521-82538.

Martins, J. R. (2022). Aerodynamic design optimization: Challenges and perspectives. Computers & Fluids, 239, 105391.

Mei, B., Barnoon, P., Toghraie, D., Su, C. H., Nguyen, H. C., & Khan, A. (2022). Energy, exergy, environmental and economic analyzes (4E) and multi-objective optimization of a PEM fuel cell equipped with coolant channels. Renewable and Sustainable Energy Reviews, 157, 112021.

Ma, X., Su, J., Wang, C., Zhu, W., & Wang, Y. (2023). 3d human mesh estimation from virtual markers. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (pp. 534-543).

Vidakis, N., David, C., Petousis, M., Sagris, D., & Mountakis, N. (2023). Optimization of key quality indicators in material extrusion 3D printing of acrylonitrile butadiene styrene: The impact of critical process control parameters on the surface roughness, dimensional accuracy, and porosity. Materials Today Communications, 34, 105171.

Marinić-Kragić, I., Vučina, D., & Milas, Z. (2022). Global optimization of Savonius-type vertical axis wind turbine with multiple circular-arc blades using validated 3D CFD model. Energy, 241, 122841.

Guan, S., Xu, J., He, M. Z., Wang, Y., Ni, B., & Yang, X. (2022). Out-of-domain human mesh reconstruction via dynamic bilevel online adaptation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 45(4), 5070-5086.

Nayak, J., Swapnarekha, H., Naik, B., Dhiman, G., & Vimal, S. (2023). 25 years of particle swarm optimization: Flourishing voyage of two decades. Archives of Computational Methods in Engineering, 30(3), 1663-1725.

Ghorbani, E., Moosavi, M., Hossaini, M. F., Assary, M., & Golabchi, Y. (2021). Determination of initial stress state and rock mass deformation modulus at Lavarak HEPP by back analysis using ant colony optimization and multivariable regression analysis. Bulletin of Engineering Geology and the Environment, 80, 429-442.