Hauptseite > Publikationsdatenbank > Acceleration of complex high-performance computing ensemble simulations with super-resolution-based subfilter models > print

001     1021623
005     20250204113755.0
024 7 _ |a 10.1016/j.compfluid.2023.106150
|2 doi
024 7 _ |a 0045-7930
|2 ISSN
024 7 _ |a 1879-0747
|2 ISSN
024 7 _ |a 10.34734/FZJ-2024-00886
|2 datacite_doi
024 7 _ |a WOS:001164689600001
|2 WOS
037 _ _ |a FZJ-2024-00886
082 _ _ |a 004
100 1 _ |a Bode, Mathis
|0 P:(DE-Juel1)192255
|b 0
|e Corresponding author
245 _ _ |a Acceleration of complex high-performance computing ensemble simulations with super-resolution-based subfilter models
260 _ _ |a Amsterdam [u.a.]
|c 2024
|b Elsevier Science
336 7 _ |a article
|2 DRIVER
336 7 _ |a Output Types/Journal article
|2 DataCite
336 7 _ |a Journal Article
|b journal
|m journal
|0 PUB:(DE-HGF)16
|s 1706699533_5586
|2 PUB:(DE-HGF)
336 7 _ |a ARTICLE
|2 BibTeX
336 7 _ |a JOURNAL_ARTICLE
|2 ORCID
336 7 _ |a Journal Article
|0 0
|2 EndNote
520 _ _ |a Direct numerical simulation (DNS) of fluid flow problems has been one of the most important applications of high-performance computing (HPC) in the last decades. For example, turbulent flows require the simultaneous resolution of multiple spatial and temporal scales as all scales are coupled, resulting in very large simulations with enormous degrees of freedom. Another example is reactive flows, which typically result in a large system of coupled differential equations and multiple transport equations that must be solved simultaneously. In addition, many flows exhibit chaotic behavior, meaning that only statistical ensembles of results can be compared, further increasing the computational time. In this work, a combined HPC/deep learning (DL) workflow is presented that drastically reduces the overall computational time required while still providing acceptable accuracy.Traditionally, all the simulations required to compute ensemble statistics are performed using expensive DNS. The idea behind the combined HPC/DL workflow is to reduce the number of expensive DNSs by developing a DL-assisted large-eddy simulation (LES) approach that uses a sophisticated DL network, called PIESRGAN, as a subfilter model for all unclosed terms and is accurate enough to substitute DNSs. The remaining DNSs are thus used in two ways: first, as data contributing to the ensemble statistics, and second, as data used to train the DL network. It was found that in many cases two remaining DNSs are sufficient for training the LES approach. The cost of the DL-supported LES is usually more than one order of magnitude cheaper than the DNS, which drastically speeds up the workflow, even considering the overhead for training the DL network.
536 _ _ |a 5112 - Cross-Domain Algorithms, Tools, Methods Labs (ATMLs) and Research Groups (POF4-511)
|0 G:(DE-HGF)POF4-5112
|c POF4-511
|f POF IV
|x 0
536 _ _ |a CoEC - Center of Excellence in Combustion (952181)
|0 G:(EU-Grant)952181
|c 952181
|f H2020-INFRAEDI-2019-1
|x 1
588 _ _ |a Dataset connected to CrossRef, Journals: juser.fz-juelich.de
700 1 _ |a Göbbert, Jens Henrik
|0 P:(DE-Juel1)168541
|b 1
|u fzj
773 _ _ |a 10.1016/j.compfluid.2023.106150
|g Vol. 271, p. 106150 -
|0 PERI:(DE-600)1499975-4
|p 106150
|t Computers & fluids
|v 271
|y 2024
|x 0045-7930
856 4 _ |y OpenAccess
|u https://juser.fz-juelich.de/record/1021623/files/FZJ-2024-00886.pdf
856 4 _ |y OpenAccess
|x icon
|u https://juser.fz-juelich.de/record/1021623/files/FZJ-2024-00886.gif?subformat=icon
856 4 _ |y OpenAccess
|x icon-1440
|u https://juser.fz-juelich.de/record/1021623/files/FZJ-2024-00886.jpg?subformat=icon-1440
856 4 _ |y OpenAccess
|x icon-180
|u https://juser.fz-juelich.de/record/1021623/files/FZJ-2024-00886.jpg?subformat=icon-180
856 4 _ |y OpenAccess
|x icon-640
|u https://juser.fz-juelich.de/record/1021623/files/FZJ-2024-00886.jpg?subformat=icon-640
909 C O |o oai:juser.fz-juelich.de:1021623
|p openaire
|p open_access
|p driver
|p VDB
|p ec_fundedresources
|p dnbdelivery
910 1 _ |a Forschungszentrum Jülich
|0 I:(DE-588b)5008462-8
|k FZJ
|b 0
|6 P:(DE-Juel1)192255
910 1 _ |a Forschungszentrum Jülich
|0 I:(DE-588b)5008462-8
|k FZJ
|b 1
|6 P:(DE-Juel1)168541
913 1 _ |a DE-HGF
|b Key Technologies
|l Engineering Digital Futures – Supercomputing, Data Management and Information Security for Knowledge and Action
|1 G:(DE-HGF)POF4-510
|0 G:(DE-HGF)POF4-511
|3 G:(DE-HGF)POF4
|2 G:(DE-HGF)POF4-500
|4 G:(DE-HGF)POF
|v Enabling Computational- & Data-Intensive Science and Engineering
|9 G:(DE-HGF)POF4-5112
|x 0
914 1 _ |y 2024
915 _ _ |a OpenAccess
|0 StatID:(DE-HGF)0510
|2 StatID
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)0300
|2 StatID
|b Medline
|d 2024-12-17
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)0199
|2 StatID
|b Clarivate Analytics Master Journal List
|d 2024-12-17
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)1160
|2 StatID
|b Current Contents - Engineering, Computing and Technology
|d 2024-12-17
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)0150
|2 StatID
|b Web of Science Core Collection
|d 2024-12-17
915 _ _ |a JCR
|0 StatID:(DE-HGF)0100
|2 StatID
|b COMPUT FLUIDS : 2022
|d 2024-12-17
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)0200
|2 StatID
|b SCOPUS
|d 2024-12-17
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)0600
|2 StatID
|b Ebsco Academic Search
|d 2024-12-17
915 _ _ |a No Peer Review
|0 StatID:(DE-HGF)0020
|2 StatID
|b ASC
|d 2024-12-17
915 _ _ |a IF < 5
|0 StatID:(DE-HGF)9900
|2 StatID
|d 2024-12-17
920 1 _ |0 I:(DE-Juel1)JSC-20090406
|k JSC
|l Jülich Supercomputing Center
|x 0
980 _ _ |a journal
980 _ _ |a VDB
980 _ _ |a UNRESTRICTED
980 _ _ |a I:(DE-Juel1)JSC-20090406
980 1 _ |a FullTexts

LibraryCollectionCLSMajorCLSMinorLanguageAuthor
Marc 21