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

Stefan Kerkemeier, Christos E. Frouzakis, Ananias G. Tomboulides, Paul Fischer, Mathis Bode

TL;DR

NekCRF, a high-order reactive low Mach flow solver specifically designed for exascale computing, is introduced and its capabilities and efficiency are showcased on the pre-exascale system JUWELS Booster.

Abstract

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 Jülich Supercomputing Centre including a validation across diverse cases of varying complexity.

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

TL;DR

NekCRF, a high-order reactive low Mach flow solver specifically designed for exascale computing, is introduced and its capabilities and efficiency are showcased on the pre-exascale system JUWELS Booster.

Abstract

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 Jülich Supercomputing Centre including a validation across diverse cases of varying complexity.
Paper Structure (14 sections, 4 equations, 5 figures, 4 tables)

This paper contains 14 sections, 4 equations, 5 figures, 4 tables.

Table of Contents

  1. Introduction
  2. Numerical method and code description
  3. Validation
  4. Homogeneous variable-volume autoignition
  5. Laminar planar premixed flame
  6. Early flame kernel development
  7. Performance
  8. Comparison to LAVp
  9. Strong scaling
  10. Compute kernels
  11. Conclusions and outlook
  12. Availability
  13. CRediT authorship contribution statement
  14. Acknowledgements

Figures (5)

  • Figure 1: Comparison of the time histories of temperature and $Y_{\rm OH}$ (symbols) against the Chemkin solution (lines).
  • Figure 2: Comparison of temperature and selected species mass fraction profiles in the flame normal direction against Cantera (black line) and LAVp ($\times$ symbol) solutions.
  • Figure 3: (a) Slice of a quarter of the mesh (b) flame kernels defined by the $T=1600$ K isotherm and colored by the flow velocity magnitude at four time instants.
  • Figure 4: Comparison of the time histories of the integral heat release rate and instantaneous isocontours of temperature. Insets: $T=1600$K isotherms on an $x-z$ slice at four time instants.
  • Figure 5: Strong scalability of the different solver components.