Direct numerical simulations of turbulent reacting flows with shock waves and stiff chemistry using many-core/GPU acceleration

Swapnil Desai, Yu Jeong Kim, Wonsik Song, Minh Bau Luong, Francisco Hernandez Perez, Ramanan Sankaran, Hong G. Im

Research output: Contribution to journalArticlepeer-review

27 Scopus citations


Compressible reacting flows may display sharp spatial variation related to shocks, contact discontinuities or reactive zones embedded within relatively smooth regions. The presence of such phenomena emphasizes the relevance of shock-capturing schemes such as the weighted essentially non-oscillatory (WENO) scheme as an essential ingredient of the numerical solver. However, these schemes are complex and have more computational cost than the simple high-order compact or non-compact schemes. In this paper, we present the implementation of a seventh-order, minimally-dissipative mapped WENO (WENO7M) scheme in a newly developed direct numerical simulation (DNS) code called KAUST Adaptive Reactive Flows Solver (KARFS). In order to make efficient use of the computer resources and reduce the solution time, without compromising the resolution requirement, the WENO routines are accelerated via graphics processing unit (GPU) computation. The performance characteristics and scalability of the code are studied using different grid sizes and block decomposition. The performance portability of KARFS is demonstrated on a variety of architectures including NVIDIA Tesla P100 GPUs and NVIDIA Kepler K20X GPUs. In addition, the capability and potential of the newly implemented WENO7M scheme in KARFS to perform DNS of compressible flows is also demonstrated with model problems involving shocks, isotropic turbulence, detonations and flame propagation into a stratified mixture with complex chemical kinetics.
Original languageEnglish (US)
Pages (from-to)104787
JournalComputers and Fluids
StatePublished - Nov 30 2020

Bibliographical note

KAUST Repository Item: Exported on 2020-12-23
Acknowledgements: This work was sponsored by competitive research funding from King Abdullah University of Science and Technology (KAUST). This research used resources of the computer clusters at KAUST Supercomputing Laboratory (KSL), the Oak Ridge Leadership Computing Facility and the Compute Data and Environment for Science (CADES) at ORNL, which is supported by the Office of Science of the U.S. Department of Energy under Contract No. DE-AC05-00OR22725.


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