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dc.contributor.authorMira, Daniel
dc.contributor.authorPérez Sánchez, Eduardo J.
dc.contributor.authorBorrell Pol, Ricard
dc.contributor.authorHouzeaux, Guillaume
dc.contributor.otherBarcelona Supercomputing Center
dc.date.accessioned2022-09-23T13:07:25Z
dc.date.available2022-09-23T13:07:25Z
dc.date.issued2022
dc.identifier.citationMira, D. [et al.]. HPC-enabling technologies for high-fidelity combustion simulations. "Proceedings of the Combustion Institute", 2022,
dc.identifier.issn1540-7489
dc.identifier.urihttp://hdl.handle.net/2117/373453
dc.description.abstractWith the increase in computational power in the last decade and the forthcoming Exascale supercomputers, a new horizon in computational modelling and simulation is envisioned in combustion science. Considering the multiscale and multiphysics characteristics of turbulent reacting flows, combustion simulations are considered as one of the most computationally demanding applications running on cutting-edge supercomputers. Exascale computing opens new frontiers for the simulation of combustion systems as more realistic conditions can be achieved with high-fidelity methods. However, an efficient use of these computing architectures requires methodologies that can exploit all levels of parallelism. The efficient utilization of the next generation of supercomputers needs to be considered from a global perspective, that is, involving physical modelling and numerical methods with methodologies based on High-Performance Computing (HPC) and hardware architectures. This review introduces recent developments in numerical methods for large-eddy simulations (LES) and direct-numerical simulations (DNS) to simulate combustion systems, with focus on the computational performance and algorithmic capabilities. Due to the broad scope, a first section is devoted to describe the fundamentals of turbulent combustion, which is followed by a general description of state-of-the-art computational strategies for solving these problems. These applications require advanced HPC approaches to exploit modern supercomputers, which is addressed in the third section. The increasing complexity of new computing architectures, with tightly coupled CPUs and GPUs, as well as high levels of parallelism, requires new parallel models and algorithms exposing the required level of concurrency. Advances in terms of dynamic load balancing, vectorization, GPU acceleration and mesh adaptation have permitted to achieve highly-efficient combustion simulations with data-driven methods in HPC environments. Therefore, dedicated sections covering the use of high-order methods for reacting flows, integration of detailed chemistry and two-phase flows are addressed. Final remarks and directions of future work are given at the end. }
dc.description.sponsorshipThe research leading to these results has received funding from the European Union’s Horizon 2020 Programme under the CoEC project, grant agreement No. 952181 and the CoE RAISE project grant agreement no. 951733.
dc.format.extent35 p.
dc.language.isoeng
dc.publisherElsevier
dc.rightsAttribution 4.0 International
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/
dc.subjectÀrees temàtiques de la UPC::Informàtica::Aplicacions de la informàtica::Aplicacions informàtiques a la física i l‘enginyeria
dc.subject.lcshSupercomputers
dc.subject.lcshCombustion
dc.subject.otherHigh-Performance Computing (HPC)
dc.subject.otherExascale
dc.subject.otherCombustion
dc.subject.otherMultiphase flow
dc.subject.otherChemistry
dc.subject.otherHigh-order methods
dc.subject.otherGraphics Processing Unit (GPU)
dc.titleHPC-enabling technologies for high-fidelity combustion simulations
dc.typeArticle
dc.subject.lemacSimulació per ordinador
dc.identifier.doi10.1016/j.proci.2022.07.222
dc.description.peerreviewedPeer Reviewed
dc.relation.publisherversionhttps://www.sciencedirect.com/science/article/pii/S1540748922002516
dc.rights.accessOpen Access
dc.description.versionPostprint (published version)
dc.relation.projectidinfo:eu-repo/grantAgreement/EC/H2020/952181/EU/Center of Excellence in Combustion/CoEC
dc.relation.projectidinfo:eu-repo/grantAgreement/EC/H2020/951733/EU/Research on AI- and Simulation-Based Engineering at Exascale/RAISE
local.citation.publicationNameProceedings of the Combustion Institute


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