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  4. 2023
  5. VOL 45, NO 6 (2023)
  6. pp. 85-101

PARALLELIZATION OF THE FLUID BEHAVIOR MODELING ALGORITHM IN REAL TIME

L.I. Mochurad, A.A. Dereviannyi, O.R. Tkachuk

Èlektron. model. 2023, 45(6):85-101

https://doi.org/10.15407/emodel.45.06.085

ABSTRACT

A parallel algorithm based on Compute Unified Device Architecture (CUDA) technology is proposed to accelerate fluid behavior simulation and real-time decision making capability. Three main steps were highlighted: implementation of the fluid flow simulation method, distribution of work between CUDA threads, and collection of results. A software product was developed to analyze the obtained results. As a result, it was found that the minimum acceptable refresh rate of the simulation environment is achieved on an environment with a size of 512 ´ 512 and is an average of 51.54 FPS (number of frames per second) for both states (quiet and active simulation). An analysis of literary sources was carried out, where the current state of this scientific problem is outlined and the advantages of the proposed approach are indicated. Among the simulation methods, the method using the Navier―Strokes equation for the flow of incompressible matter was chosen because it is simple and has good possibilities for parallelization.

KEYWORDS

рівняннями Нав’є―Стокса, метод частинок, графічний процесор, прискорення, realtime системи, модель SIMD.

REFERENCES

  1. Bernatovych, A., & Stetsenko, I. (2023). Physical modeling methods and software. Adaptive automatic control systems, 1(42), 130-140. https://doi.org/10.20535/1560-8956.42.2023.279104.
  2. Marion, M., & Temam, R. (1998). Navier-stokes equations: Theory and approximation, in Handbook of Numerical Analysis, in Numerical Methods for Solids (Part 3) Numerical Methods for Fluids (Part 1), (6), 503–689. https://doi.org/10.1016/S1570-8659(98)80010-0.
  3. Michael Thambynayagam, R.K. (2023). A class of exact solutions of the Navier-Stokes equations in three and four dimensions, General Mathematics, 1-19.
  4. Lind Steven, J., Rogers Benedict D., & Stansby Peter, K. (2020). Review of smoothed particle hydrodynamics: towards converged Lagrangian flow modelling, R. Soc. A., 476:20190801.20190801. https://doi.org/10.1098/rspa.2019.0801.
  5. Romain Noël, Laurent Navarro, & Guy Courbebaisse, (2021). Lattice Boltzmann Method for Heterogeneous Multi-Class Traffic Flow, Journal of Computational and Theoretical Transport, 50(1), 27-51.
    https://doi.org/10.1080/23324309.2020.1828468.
  6. Heinz, S., Mokhtarpoor, R., & Stoellinger, M. (2020). Theory-based Reynolds-averaged Navier-Stokesequations with large eddy simulation capabilityfor separated turbulent flow simulations, Fluids, 32(6), 1-21. https://doi.org/10.1063/5.0006660.
  7. Brennen, C.E. (2005). Fundamentals of Multiphase Flows. Cambridge University Press. https://doi.org/10.1017/CBO9780511807169.
  8. Modeling of fluid-structure interaction. (2023, 13 May). Annual Review of Fluid Mecha­nics. https://www.annualreviews.org/doi/abs/10.1146/annurev.fluid.33.1.445
  9. Zeng, Y., Wang, Y., Yang, D., & Chen, Q. (2023). Immersed Boundary Methods for Simu­lations of Biological Flows in Swimming and Flying Bio-Locomotion: A Review. Applied Sciences, 13(7), 4208. https://doi.org/10.3390/app13074208.
  10. Lenaerts, T., & Dutré, P. (2009). Mixing Fluids and Granular Materials, Computer Graphics Forum, 28(2), 213- https://doi.org/10.1111/j.1467-8659.2009.01360.x.
  11. Mokhtar, M.K., Mohamed, F., Zamri, M.N., Sunar, M.S., & Chand, S.J.H. (2015). A Review on Fluid Simulation Method for Blood Flow Representation, Medical Imaging Technology: Reviews and Computational Applications, 129- https://doi.org/10.1007/978-981-287-540-2_6.
  12. Xu, Ao, & Li, Bo-Tao. (2023). Multi-GPU thermal lattice Boltzmann simulations using OpenACC and MPI, International Journal of Heat and Mass Transfer, 201(2). https://doi.org/10.1016/j.ijheatmasstransfer.2022.123649.
  13. Mochurad, , & Bliakhar, R. (2022). Comparison of the Efficiency of Parallel Algorithms KNN and NLM Based on CUDA for Large Image Processing, CEUR Workshop Procee­dings, (3137), 238-249. https://doi.org/10.32782/cmis/3137-20.
  14. Mochurad, L.I. (2022). Canny Edge Detection Analysis Based on Parallel Algorithm, Constructed Complexity Scale and CUDA, Computing and Informatics, 41(4), 957- https://doi.org/10.31577/cai_2022_4_957.
  15. Mochurad, L., Kotsiumbas, O., & Protsyk, I. (2022). A Model for Weather Forecasting Based on Parallel Calculations, In: Hu, Z., Ye, Z., He, M. (eds) Advances in Artificial Systems for Medicine and Education VI. AIMEE 2022. Lecture Notes on Data Engineering and Communications Technologies, (159). https://doi.org/10.1007/978-3-031-24468-1_4.
  16. Nakayama, Ya. (2018). Introduction to Fluid Mechanics (2nd Edition). Butterworth-Heinemann.
    https://doi.org/10.1016/B978-0-08-102437-9.00001-2
  17. Flegar, G., Anzt, H., Cojean, T., & Quintana-Ortí, E. S. (2021). Adaptive Precision Block-Jacobi for High Performance Preconditioning in the Ginkgo Linear Algebra Software, ACM Trans. Math. Softw., 47(2), 1- https://doi.org/10.1145/3441850.
  18. Bolz, J., Farmer, I., Grinspun, E., & Schröder, (2003). Sparse matrix solvers on the GPU: conjugate gradients and multigrid, ACM Trans. Graph. 22(3), 917-924. https://doi.org/10.1145/882262.882364.
  19. Bhatti, M.M., Marin, M., Zeeshan, A., &. Abdelsalam, S.I. (2020). Editorial: Recent Trends in Computational Fluid Dynamics, Phys., (8), 593111. https://doi.org/10.3389/fphy.2020.593111.
  20. Stam, J. (2019). Stable fluids, Revised, Proceedings of the 46th annual conference on Computer graphics and interactive techniques, 121- https://doi.org/10.1145/311535.311548.
  21. Sheng, W., (2020). A revisit of Navier-Stokes equation, Journal of Mechanics - B/Fluids, (80), 60-71. https://doi.org/10.1016/j.euromechflu.2019.12.005.
  22. Bistafa, S.R. (2018). On the development of the Navier–Stokes equation by Navier, Brasileira de Ensino de F´ısica, 40(2). https://doi.org/10.1590/1806-9126-rbef-2017-0239.

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