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nekCRF: A next generation high-order reactive low Mach flow solver for direct numerical simulations

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arxiv 2409.06404 v1 pith:IHVUGRXL submitted 2024-09-10 physics.comp-ph cs.PFphysics.flu-dyn

Stefan Kerkemeier , Christos E. Frouzakis , Ananias G. Tomboulides , Paul Fischer , Mathis Bode This is my paper · ORCID
classification physics.comp-phcs.PFphysics.flu-dyn
keywords reactivecapabilitiescombustioncomputingefficiencyflowhigh-ordermach
verification ladder T0 review T1 audit T2 compute T3 formal

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Exascale computing enables high-fidelity simulations of chemically reactive flows in practical geometries and conditions, and paves the way for valuable insights that can optimize combustion processes, ultimately reducing emissions and improving fuel combustion efficiency. However, this requires software that can fully leverage the capabilities of current high performance computing systems. The paper introduces nekCRF, a high-order reactive low Mach flow solver specifically designed for this purpose. Its capabilities and efficiency are showcased on the pre-exascale system JUWELS Booster, a GPU-based supercomputer at the J\"{u}lich Supercomputing Centre including a validation across diverse cases of varying complexity.

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Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Deciphering boundary layer dynamics in high-Rayleigh-number convection using 3360 GPUs and a high-scaling in-situ workflow

    physics.flu-dyn 2025-01 conditional novelty 6.0 of 10

    Direct numerical simulations of Rayleigh-Benard convection at Ra=10^12 on 3,360 GPUs, combined with an ASCENT-based in-situ workflow, deliver time-resolved boundary-layer visualization at manageable overhead.

  2. Flow reconstruction in time-varying geometries using graph neural networks

    cs.LG 2024-11 conditional novelty 6.0 of 10

    A graph attention convolutional network with feature propagation and a validity mask reconstructs turbulent engine flows from 1% of the data, generalizing to domains 14 times larger than those seen in training.

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