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    High-order accurate entropy-stable discontinuous collocated Galerkin methods with the summation-by-parts property for compressible CFD frameworks: Scalable SSDC algorithms and flow solver

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    High order_1-s2.0-S0021999120306185-main.pdf
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    56.89Mb
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    PDF
    Description:
    Accepted Article
    Embargo End Date:
    2022-09-22
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    Type
    Article
    Authors
    Parsani, Matteo cc
    Boukharfane, Radouan cc
    Nolasco, Irving Reyna
    Del Rey Fernández, David C.
    Zampini, Stefano cc
    Hadri, Bilel cc
    Dalcin, Lisandro cc
    KAUST Department
    Applied Mathematics and Computational Science Program
    Extreme Computing Research Center
    Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
    King Abdullah University of Science and Technology (KAUST), Computer Electrical and Mathematical Science and Engineering Division (CEMSE), Extreme Computing Research Center (ECRC), Thuwal, Saudi Arabia.
    Supercomputing, Computational Scientists
    Date
    2020-09-22
    Embargo End Date
    2022-09-22
    Submitted Date
    2020-02-08
    Permanent link to this record
    http://hdl.handle.net/10754/665342
    
    Metadata
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    Abstract
    This work reports on the performances of a fully-discrete hp-adaptive entropy stable discontinuous collocated Galerkin method for the compressible Naiver–Stokes equations. The resulting code framework is denoted by SSDC, the first S for entropy, the second for stable, and DC for discontinuous collocated. The method is endowed with the summation-by-parts property, allows for arbitrary spatial and temporal order, and is implemented in an unstructured high performance solver. The considered class of fully-discrete algorithms are systematically designed with mimetic and structure preserving properties that allow the transfer of continuous proofs to the fully discrete setting. Our goal is to provide numerical evidence of the adequacy and maturity of these high-order methods as potential base schemes for the next generation of unstructured computational fluid dynamics tools. We provide a series of test cases of increased difficulty, ranging from non-smooth to turbulent flows, in order to evaluate the numerical performance of the algorithms. Results on weak and strong scaling of the distributed memory implementation demonstrate that the parallel SSDC solver can scale efficiently over 100,000 processes.
    Citation
    Parsani, M., Boukharfane, R., Nolasco, I. R., Del Rey Fernández, D. C., Zampini, S., Hadri, B., & Dalcin, L. (2020). High-order accurate entropy-stable discontinuous collocated Galerkin methods with the summation-by-parts property for compressible CFD frameworks: Scalable SSDC algorithms and flow solver. Journal of Computational Physics, 109844. doi:10.1016/j.jcp.2020.109844
    Sponsors
    The research reported in this paper was funded by King Abdullah University of Science and Technology. We are thankful to the Supercomputing Laboratory and the Extreme Computing Research Center at King Abdullah University of Science and Technology for their computing resources. Special thanks are extended to the McLaren F1 racing Team for providing experimental data and CAD geometries for the delta wing test case.
    Publisher
    Elsevier BV
    Journal
    Journal of Computational Physics
    DOI
    10.1016/j.jcp.2020.109844
    Additional Links
    https://linkinghub.elsevier.com/retrieve/pii/S0021999120306185
    ae974a485f413a2113503eed53cd6c53
    10.1016/j.jcp.2020.109844
    Scopus Count
    Collections
    Articles; Applied Mathematics and Computational Science Program; Extreme Computing Research Center; Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division

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