Publication Cover
Mathematical and Computer Modelling of Dynamical Systems
Methods, Tools and Applications in Engineering and Related Sciences
Volume 29, 2023 - Issue 1
2,342
Views
0
CrossRef citations to date
0
Altmetric
Research Article

Towards real-time fluid dynamics simulation: a data-driven NN-MPS method and its implementation

, , , &
Pages 95-115 | Received 18 Jul 2022, Accepted 21 Feb 2023, Published online: 06 Mar 2023

References

  • ] S. Koshizuka, Y. Oka, Moving-particlesemi-implicit method for fragmentation of incompressible fluid, Nucl. Sci. Eng. 123, 3 (1996), pp. 421–434. doi:10.13182/NSE96-A24205
  • J.J. Monaghan, Smoothed particle hydrodynamics, Annu. Rev. Astron. Astrophys. 30 (1) (1992), pp. 543–574. doi:10.1146/annurev.aa.30.090192.002551.
  • A.M. Gambaruto, Computational haemodynamics of small vessels using the moving particle semi-implicit (mps) method, J. Comput. Phys. 302 (2015), pp. 68–96. doi:10.1016/j.jcp.2015.08.039.
  • K. Murotani, S. Koshizuka, T. Tamai, K. Shibata, N. Mitsume, S. Yoshimura, S. Tanaka, K. Hasegawa, E. Nagai, and T. Fujisawa, Development of hierarchical domain decomposition explicit mps method and application to large-scale tsunami analysis with floating objects, Adv. Model. Simul. Eng. Sci. 1 (1) (2014), pp. 16–35. doi:10.15748/jasse.1.16.
  • T. Okabe, H. Matsutani, T. Honda, and S. Yashiro, Numerical simulation of microscopic flow in a fiber bundle using the moving particle semi-implicit method, composites part A, Appl. Sci. Manuf. 43 (10) (2012), pp. 1765–1774. doi:10.1016/j.compositesa.2012.05.003.
  • G. Duan and B. Chen, Comparison of parallel solvers for moving particle semi-implicit method, Eng. Comput. 32 (3) (2015), pp. 834–862. doi:10.1108/EC-02-2014-0029.
  • S. Koshizuka, K. Shibata, M. Kondo, and T. Matsunaga, Moving Particle Semi-Implicit Method: A Meshfree Particle Method for Fluid Dynamics, Cambridge, Massachusetts: Academic Press, 2018.
  • B.R. Noack and H. Eckelmann, A low-dimensional galerkin method for the three-dimensional flow around a circular cylinder, Phys. Fluids 6 (1) (1994), pp. 124–143. doi:10.1063/1.868433.
  • A. Treuille, A. Lewis, and Z. Popović, Model reduction for real-time fluids, ACM Trans. Graph. 25 (3) (2006), pp. 826–834. doi:10.1145/1141911.1141962.
  • R. Ando, N. Thürey, and C. Wojtan, A dimension-reduced pressure solver for liquid simulations, in Computer Graphics Forum, Vol. 34, Hoboken, New Jersey, U.S: Wiley Online Library, 2015, pp. 473–480.
  • T. De Witt, C. Lessig, and E. Fiume, Fluid simulation using Laplacian eigenfunctions, ACM Trans. Graph. 31 (1) (2012), pp. 1–11. doi:10.1145/2077341.2077351.
  • S. Li, G. Duan, and M. Sakai, Development of a reduced-order model for large-scale Eulerian–Lagrangian simulations, Adv. Powder Technol. 33 (8) (2022), pp. 103632. doi:10.1016/j.apt.2022.103632.
  • L. Ladickỳ, S. Jeong, B. Solenthaler, M. Pollefeys, and M. Gross, Data-driven fluid simulations using regression forests, ACM Trans. Graph. 34 (6) (2015), pp. 1–9. doi:10.1145/2816795.2818129.
  • Z. Li and A.B. Farimani, Graph neural network-accelerated Lagrangian fluid simulation, Comput. Graph 103 (2022), pp. 201–211. doi:10.1016/j.cag.2022.02.004.
  • J. Bai, Y. Zhou, Y. Ma, H. Jeong, H. Zhan, C. Rathnayaka, E. Sauret, and Y. Gu, A general neural particle method for hydrodynamics modeling, Comput. Methods Appl. Mech. Eng. 393 (2022), pp. 114740. doi:10.1016/j.cma.2022.114740.
  • C. Yang, X. Yang, and X. Xiao, Data-driven projection method in fluid simulation, Comput. Animat Virtual. Worlds 27 (3–4) (2016), pp. 415–424. doi:10.1002/cav.1695.
  • J. Tompson, K. Schlachter, P. Sprechmann, and K. Perlin, Accelerating Eulerian fluid simulation with convolutional networks, in: International Conference on Machine Learning, PMLR, Sydney, Australia, 11-15 Aug. 2017, pp. 3424–3433.
  • J.C. Martin, W.J. Moyce, J. Martin, W. Moyce, W.G. Penney, A. Price, and C. Thornhill, Part iv. an experimental study of the collapse of liquid columns on a rigid horizontal plane, Philos Trans. R. Soc. Lond A 244 (1952), pp. 312–324.
  • L. Hernquist and N. Katz, Treesph-a unification of sph with the hierarchical tree method, Astrophys. J., Suppl. Ser. 70 (1989), pp. 419–446. doi:10.1086/191344.
  • B.-H. Lee, J.-C. Park, M.-H. Kim, and S.-C. Hwang, Step-by-step improvement of mps method in simulating violent free-surface motions and impact-loads, Comput. Methods Appl. Mech. Eng. 200 (9–12) (2011), pp. 9–12. doi:10.1016/j.cma.2010.12.001.
  • V. Nair and G.E. Hinton, Rectified linear units improve restricted Boltzmann machines, ICML'10: Proceedings of the 27th International Conference on International Conference on Machine Learning. icml10 (2010), pp. 807–814.
  • D.E. Rumelhart, G.E. Hinton, and R.J. WilliamsLearning representations by back-propagating errors, nature32360881986533–536.10.1038/323533a0.
  • D.P. Kingma and J. Ba. Adam: A method for stochastic optimization. In International Conference on Learning Representations, San Diego, USA, 7-9. May. 2015, ICLR (Poster) 2015 Ithaca, NY: arXiv.org, 2015. https://arxiv.org/pdf/1412.6980.pdf
  • N. Srivastava, G. Hinton, A. Krizhevsky, I. Sutskever, and R. Salakhutdinov, Dropout: A simple way to prevent neural networks from overfitting, J. Mach. Learn. Res. 15 (2014), pp. 1929–1958.
  • M. Abadi, P. Barham, J. Chen, Z. Chen, A. Davis, J. Dean, M. Devin, S. Ghemawat, G. Irving, M. Isard, Manjunath, Kudlur, Josh, Levenberg, Rajat, Monga, Sherry, Moore, Derek, G., Murray, Benoit, Steiner, Paul, Tucker, Vijay, Vasudevan, Pete, Warden, Martin Wicke, Yuan Yu, Xiaoqiang Zheng. TensorFlow: A system for large-scale machine learning. In 12th USENIX Symposium on Operating Systems Design and Implementation (OSDI '16), NOVEMBER 2–4, 2016, SAVANNAH, GA, USA, Vol. 16. 2016, pp. 265–283.
  • L. Wang, A. Khayyer, H. Gotoh, Q. Jiang, and C. Zhang, Enhancement of pressure calculation in projection-based particle methods by incorporation of background mesh scheme, Appl. Ocean Res. 86 (2019), pp. 320–339. doi:10.1016/j.apor.2019.01.017.
  • G. Duan, S. Koshizuka, A. Yamaji, B. Chen, X. Li, and T. Tamai, An accurate and stable multiphase moving particle semi-implicit method based on a corrective matrix for all particle interaction models, Int. J. Numer Methods Eng. 115 (10) (2018), pp. 1287–1314. doi:10.1002/nme.5844.
  • G. Duan, T. Matsunaga, S. Koshizuka, A. Yamaguchi, and M. Sakai, New insights into error accumulation due to biased particle distribution in semi-implicit particle methods, Comput. Methods Appl. Mech. Eng. 388 (2022), pp. 114219. doi:10.1016/j.cma.2021.114219.