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Simulation of Particulate Flow Using HPC Systems

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High Performance Computing in Science and Engineering '19

Abstract

A standard strategy to predict the modulation of turbulence by the presence of particles is the two-way coupling approach, where the solid phase is approximated by point particles, which introduce sources in the momentum conservation equation. A validation of this approach is presented for isotropic decaying turbulence laden with prolate and oblate particles of Kolmogorov-length-scale size by generating highly accurate reference results via direct particle-fluid simulations, where all turbulent scales and the complete flow field in the vicinity of the particles are resolved. About 30,000 oblate and prolate particles with aspect ratios raging from 0.25 to 4 are released into the flow field. The simulation using the two-way coupled spherical and ellipsoidal Lagrangian model is compared against the reference results. The analysis of turbulent kinetic energy budgets reveals that the particles release kinetic energy into the flow field and simultaneously enhance the dissipation rate. This behavior is correctly predicted by both point-particle models. The kinetic energy of the particles, however, is significantly overestimated by the point-particle models. Moreover, the ellipsoidal Lagrangian model fails to predict the angular velocity of the particles due to the missing correlation terms for finite fluid inertia.

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Acknowledgements

This work has been financed by the German Research Foundation (DFG) within the framework of the SFB/Transregio ’Oxyflame’ (subproject B2). The support is gratefully acknowledged. Computing resources were provided by the High Performance Computing Center Stuttgart (HLRS) and by the Jülich Supercomputing Center (JSC) within a Large-Scale Project of the Gauss Center for Supercomputing (GCS).

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Correspondence to K. Fröhlich .

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Fröhlich, K., Meinke, M., Schröder, W. (2021). Simulation of Particulate Flow Using HPC Systems. In: Nagel, W.E., Kröner, D.H., Resch, M.M. (eds) High Performance Computing in Science and Engineering '19. Springer, Cham. https://doi.org/10.1007/978-3-030-66792-4_21

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