Scalable Flow Simulations with the Lattice Boltzmann Method

ABSTRACT

The primary goal of the EuroHPC JU project SCALABLE is to develop an industrial Lattice Boltzmann Method (LBM)-based computational fluid dynamics (CFD) solver capable of exploiting current and future extreme scale architectures, expanding current capabilities of existing industrial LBM solvers by at least two orders of magnitude in terms of processor cores and lattice cells, while preserving its accessibility from both the end-user and software developer's point of view. This is accomplished by transferring technology and knowledge between an academic code (waLBerla) and an industrial code (LaBS). This paper briefly introduces the characteristics and main features of both software packages involved in the process. We also highlight some of the performance achievements in scales of up to tens of thousand of cores presented on one academic and one industrial benchmark case.

References

1. Amati, G., Succi, S., Fanelli, P., Krastev, V. K., and Falcucci, G. Projecting LBM performance on Exascale class Architectures: A tentative outlook. Journal of Computational Science (2021).
2. Bauer, M., Eibl, S., Godenschwager, C., Kohl, N., Kuron, M., Rettinger, C., Schornbaum, F., Schwarzmeier, C., Thönnes, D., Köstler, H., and Rüde, U. waLBerla: A block-structured high-performance framework for multiphysics simulations. Comput. Math. Appl. 81 (jan 2021), 478--501.
3. Bauer, M., Hötzer, J., Ernst, D., Hammer, J., Seiz, M., Hierl, H., Hönig, J., Köstler, H., Wellein, G., Nestler, B., and Rüde, U. Code generation for massively parallel phase-field simulations. In Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis (nov 2019), ACM.
4. Bauer, M., Köstler, H., and Rüde, U. lbmpy: Automatic code generation for efficient parallel lattice boltzmann methods. Journal of Computational Science 49 (feb 2021).
5. Bauer, M., Schornbaum, F., Godenschwager, C., Markl, M., Anderl, D., Köstler, H., and Rüde, U. A python extension for the massively parallel multiphysics simulation framework waLBerla. International Journal of Parallel, Emergent and Distributed Systems (2015).
6. Geier, M., and Schönherr, M. Esoteric twist: An efficient in-place streaming algorithmus for the lattice boltzmann method on massively parallel hardware. Computation 5, 4 (mar 2017), 19.
7. Godenschwager, C., Schornbaum, F., Bauer, M., Köstler, H., and Rüde, U. A framework for hybrid parallel flow simulations with a trillion cells in complex geometries. In Proceedings of the International Conference on High Performance Computing, Networking, Storage and Analysis (New York, NY, USA, 2013), SC '13, Association for Computing Machinery.
8. Holzer, M., Bauer, M., Köstler, H., and Rüde, U. Highly efficient lattice boltzmann multiphase simulations of immiscible fluids at high-density ratios on CPUs and GPUs through code generation. The International Journal of High Performance Computing Applications 35, 4 (may 2021), 413--427.
9. Hoyas, S., and Jiménez, J. Scaling of the velocity fluctuations in turbulent channels up to re τ= 2003. Physics of fluids 18, 1 (2006), 011702.
10. Krüger, T., Kusumaatmaja, H., Kuzmin, A., Shardt, O., Silva, G., and Viggen, E. M. The Lattice Boltzmann Method. Springer International Publishing, 2017.
11. Manoha, E., Bulte, J., Ciobaca, V., and Caruelle, B. Lagoon: Further analysis of aerodynamic experiments and early aeroacoustics results. In 15th AIAA/CEAS Aeroacoustics Conference (30th AIAA Aeroacoustics Conference) (2009).
12. Manoha, E., Bulté, J., and Caruelle, B. Lagoon: An experimental database for the validation of cfd/caa methods for landing gear noise prediction. In 14th AIAA/CEAS Aeroacoustics Conference (29th AIAA Aeroacoustics Conference) (2008).
13. Meurer, A., Smith, C. P., Paprocki, M., Čertík, O., Kirpichev, S. B., Rocklin, M., Kumar, A., Ivanov, S., Moore, J. K., Singh, S., Rathnayake, T., Vig, S., Granger, B. E., Muller, R. P., Bonazzi, F., Gupta, H., Vats, S., Johansson, F., Pedregosa, F., Curry, M. J., Terrel, A. R., Roučka, v., Saboo, A., Fernando, I., Kulal, S., Cimrman, R., and Scopatz, A. SymPy: symbolic computing in Python. PeerJ Computer Science (1 2017).
14. Mohrhard, M., Thäter, G., Bludau, J., Horvat, B., and Krause, M. J. Autovectorization friendly parallel lattice Boltzmann streaming scheme for direct addressing. Computers & Fluids 181 (2019), 1--7.
15. Performance Optimisation and Productivity. A centre of excellence in hpc. https://pop-coe.eu. "[Online; accessed 2023-02-08]".
16. Rettinger, C., Eibl, S., Rüde, U., and Vowinckel, B. Rheology of mobile sediment beds in laminar shear flow: effects of creep and polydispersity. Journal of Fluid Mechanics 932 (2022), A1.
17. SCALABLE. Scalable lattice boltzmann leaps to exascale. https://scalable-hpc.eu/. "[Online; accessed 2023-02-08]".
18. Schornbaum, F., and Rüde, U. Massively Parallel Algorithms for the Lattice Boltzmann Method on NonUniform Grids. SIAM Journal on Scientific Computing 38, 2 (2016), C96--C126.
19. Schornbaum, F., and Rüde, U. Extreme-Scale Block-Structured Adaptive Mesh Refinement. SIAM Journal on Scientific Computing 40, 3 (2018), C358--C387.
20. Sengissen, A., Giret, J.-C., Coreixas, C., and Boussuge, J.-F. Simulations of LAGOON landing-gear noise using Lattice Boltzmann solver. In 21st AIAA/CEAS Aeroacoustics Conference (2015).
21. Vysocky, O., Beseda, M., Riha, L., Zapletal, J., Nikl, V., Lysaght, M., and Kannan, V. Evaluation of the HPC applications dynamic behavior in terms of energy consumption. In Proceedings of the Fifth International Conference on Parallel, Distributed, Grid and Cloud Computing for Engineering (2017), pp. 1--19. Paper 3, 2017. doi:10.4203/ccp.111.3.

Index Terms

• Computing methodologies

  • Modeling and simulation

    • Simulation types and techniques

      • Massively parallel and high-performance simulations