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dc.contributor.authorDesai, Swapnil
dc.contributor.authorKim, Yu Jeong
dc.contributor.authorSong, Wonsik
dc.contributor.authorLuong, Minh Bau
dc.contributor.authorHernandez Perez, Francisco
dc.contributor.authorSankaran, Ramanan
dc.contributor.authorIm, Hong G.
dc.date.accessioned2020-12-22T10:47:33Z
dc.date.available2020-12-22T10:47:33Z
dc.date.issued2020-11-30
dc.date.submitted2019-07-14
dc.identifier.citationDesai, S., Kim, Y. J., Song, W., Luong, M. B., Hernández Pérez, F. E., Sankaran, R., & Im, H. G. (2021). Direct numerical simulations of turbulent reacting flows with shock waves and stiff chemistry using many-core/GPU acceleration. Computers & Fluids, 215, 104787. doi:10.1016/j.compfluid.2020.104787
dc.identifier.issn0045-7930
dc.identifier.doi10.1016/j.compfluid.2020.104787
dc.identifier.urihttp://hdl.handle.net/10754/666589
dc.description.abstractCompressible 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.
dc.description.sponsorshipThis 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.
dc.publisherElsevier BV
dc.relation.urlhttps://linkinghub.elsevier.com/retrieve/pii/S0045793020303571
dc.rightsNOTICE: this is the author’s version of a work that was accepted for publication in Computers and Fluids. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Computers and Fluids, [215, , (2020-11-30)] DOI: 10.1016/j.compfluid.2020.104787 . © 2020. This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/
dc.titleDirect numerical simulations of turbulent reacting flows with shock waves and stiff chemistry using many-core/GPU acceleration
dc.typeArticle
dc.contributor.departmentMechanical Engineering Program
dc.contributor.departmentPhysical Science and Engineering (PSE) Division
dc.contributor.departmentClean Combustion Research Center
dc.identifier.journalComputers and Fluids
dc.rights.embargodate2022-12-11
dc.eprint.versionPost-print
dc.contributor.institutionBredesen Center for Interdisciplinary Research and Graduate Education, University of Tennessee, Knoxville, TN 37996-3394, USA
dc.contributor.institutionOak Ridge National Laboratory, Oak Ridge, TN 37831-6008, USA
dc.identifier.volume215
dc.identifier.pages104787
kaust.personKim, Yu Jeong
kaust.personSong, Wonsik
kaust.personLuong, Minh Bau
kaust.personHernandez Perez, Francisco
kaust.personIm, Hong G.
dc.date.accepted2020-11-04
dc.identifier.eid2-s2.0-85097574480
refterms.dateFOA2020-12-22T12:00:20Z
kaust.acknowledged.supportUnitcomputer clusters at KAUST
kaust.acknowledged.supportUnitKAUST Supercomputing Laboratory (KSL)


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