Home > Publications database > Towards blood flow in the virtual human: efficient self-coupling of HemeLB > print |
001 | 888841 | ||
005 | 20250314084119.0 | ||
024 | 7 | _ | |a 10.1098/rsfs.2019.0119 |2 doi |
024 | 7 | _ | |a 2042-8898 |2 ISSN |
024 | 7 | _ | |a 2042-8901 |2 ISSN |
024 | 7 | _ | |a 2128/26553 |2 Handle |
024 | 7 | _ | |a 33335704 |2 pmid |
024 | 7 | _ | |a WOS:000600128700002 |2 WOS |
037 | _ | _ | |a FZJ-2020-05255 |
082 | _ | _ | |a 570 |
100 | 1 | _ | |a McCullough, J. W. S. |0 0000-0002-9606-0408 |b 0 |e Corresponding author |
245 | _ | _ | |a Towards blood flow in the virtual human: efficient self-coupling of HemeLB |
260 | _ | _ | |a London |c 2021 |b Royal Society Publishing |
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 1646033381_3166 |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 Many scientific and medical researchers areworking towards the creation of avirtual human—a personalized digital copy of an individual—that will assistin a patient’s diagnosis, treatment and recovery. The complex nature of livingsystems means that the development of this remains a major challenge. Wedescribe progress in enabling the HemeLB lattice Boltzmann code to simulate3D macroscopic blood flowon a full human scale. Significant developments inmemory management and load balancing allow near linear scaling performanceof the code on hundreds of thousands of computer cores. Integral tothe construction of a virtual human, we also outline the implementation of aself-coupling strategy for HemeLB. This allows simultaneous simulation ofarterial and venous vascular trees based on human-specific geometries. |
536 | _ | _ | |a 5111 - Domain-Specific Simulation & Data Life Cycle Labs (SDLs) and Research Groups (POF4-511) |0 G:(DE-HGF)POF4-5111 |c POF4-511 |f POF IV |x 0 |
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 1 |
536 | _ | _ | |a E-CAM - An e-infrastructure for software, training and consultancy in simulation and modelling (676531) |0 G:(EU-Grant)676531 |c 676531 |f H2020-EINFRA-2015-1 |x 2 |
536 | _ | _ | |a POP - Performance Optimisation and Productivity (676553) |0 G:(EU-Grant)676553 |c 676553 |f H2020-EINFRA-2015-1 |x 3 |
536 | _ | _ | |a POP2 - Performance Optimisation and Productivity 2 (824080) |0 G:(EU-Grant)824080 |c 824080 |f H2020-INFRAEDI-2018-1 |x 4 |
536 | _ | _ | |a CompBioMed - A Centre of Excellence in Computational Biomedicine (675451) |0 G:(EU-Grant)675451 |c 675451 |f H2020-EINFRA-2015-1 |x 5 |
536 | _ | _ | |a CompBioMed2 - A Centre of Excellence in Computational Biomedicine (823712) |0 G:(EU-Grant)823712 |c 823712 |f H2020-INFRAEDI-2018-1 |x 6 |
536 | _ | _ | |0 G:(DE-Juel-1)ATMLPP |a ATMLPP - ATML Parallel Performance (ATMLPP) |c ATMLPP |x 7 |
588 | _ | _ | |a Dataset connected to CrossRef |
700 | 1 | _ | |a Richardson, R. A. |0 0000-0002-9984-2720 |b 1 |
700 | 1 | _ | |a Patronis, A. |0 P:(DE-Juel1)179111 |b 2 |u fzj |
700 | 1 | _ | |a Halver, R. |0 P:(DE-Juel1)132124 |b 3 |u fzj |
700 | 1 | _ | |a Marshall, R. |0 P:(DE-HGF)0 |b 4 |
700 | 1 | _ | |a Ruefenacht, M. |0 P:(DE-HGF)0 |b 5 |
700 | 1 | _ | |a Wylie, B. J. N. |0 P:(DE-Juel1)132302 |b 6 |u fzj |
700 | 1 | _ | |a Odaker, T. |0 P:(DE-HGF)0 |b 7 |
700 | 1 | _ | |a Wiedemann, M. |0 P:(DE-Juel1)131906 |b 8 |
700 | 1 | _ | |a Lloyd, B. |0 P:(DE-HGF)0 |b 9 |
700 | 1 | _ | |a Neufeld, E. |0 P:(DE-HGF)0 |b 10 |
700 | 1 | _ | |a Sutmann, Godehard |0 P:(DE-Juel1)132274 |b 11 |
700 | 1 | _ | |a Skjellum, A. |0 P:(DE-HGF)0 |b 12 |
700 | 1 | _ | |a Kranzlmüller, D. |0 P:(DE-HGF)0 |b 13 |
700 | 1 | _ | |a Coveney, P. V. |0 0000-0002-8787-7256 |b 14 |e Corresponding author |
773 | _ | _ | |a 10.1098/rsfs.2019.0119 |g Vol. 11, no. 1, p. 20190119 - |0 PERI:(DE-600)2585655-8 |n 1 |p 20190119 - |t Interface focus |v 11 |y 2021 |x 2042-8901 |
856 | 4 | _ | |u https://juser.fz-juelich.de/record/888841/files/rsfs.2019.0119.pdf |y OpenAccess |
909 | C | O | |o oai:juser.fz-juelich.de:888841 |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 2 |6 P:(DE-Juel1)179111 |
910 | 1 | _ | |a Forschungszentrum Jülich |0 I:(DE-588b)5008462-8 |k FZJ |b 3 |6 P:(DE-Juel1)132124 |
910 | 1 | _ | |a Forschungszentrum Jülich |0 I:(DE-588b)5008462-8 |k FZJ |b 6 |6 P:(DE-Juel1)132302 |
910 | 1 | _ | |a Forschungszentrum Jülich |0 I:(DE-588b)5008462-8 |k FZJ |b 11 |6 P:(DE-Juel1)132274 |
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)POF4-5111 |x 0 |
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 1 |
914 | 1 | _ | |y 2021 |
915 | _ | _ | |a DBCoverage |0 StatID:(DE-HGF)0200 |2 StatID |b SCOPUS |d 2020-08-22 |
915 | _ | _ | |a DBCoverage |0 StatID:(DE-HGF)0160 |2 StatID |b Essential Science Indicators |d 2020-08-22 |
915 | _ | _ | |a DBCoverage |0 StatID:(DE-HGF)1050 |2 StatID |b BIOSIS Previews |d 2020-08-22 |
915 | _ | _ | |a DBCoverage |0 StatID:(DE-HGF)1190 |2 StatID |b Biological Abstracts |d 2020-08-22 |
915 | _ | _ | |a Creative Commons Attribution CC BY 4.0 |0 LIC:(DE-HGF)CCBY4 |2 HGFVOC |
915 | _ | _ | |a JCR |0 StatID:(DE-HGF)0100 |2 StatID |b INTERFACE FOCUS : 2018 |d 2020-08-22 |
915 | _ | _ | |a WoS |0 StatID:(DE-HGF)0113 |2 StatID |b Science Citation Index Expanded |d 2020-08-22 |
915 | _ | _ | |a DBCoverage |0 StatID:(DE-HGF)0150 |2 StatID |b Web of Science Core Collection |d 2020-08-22 |
915 | _ | _ | |a IF < 5 |0 StatID:(DE-HGF)9900 |2 StatID |d 2020-08-22 |
915 | _ | _ | |a OpenAccess |0 StatID:(DE-HGF)0510 |2 StatID |
915 | _ | _ | |a National-Konsortium |0 StatID:(DE-HGF)0430 |2 StatID |d 2020-08-22 |w ger |
915 | _ | _ | |a DBCoverage |0 StatID:(DE-HGF)0300 |2 StatID |b Medline |d 2020-08-22 |
915 | _ | _ | |a DBCoverage |0 StatID:(DE-HGF)0320 |2 StatID |b PubMed Central |d 2020-08-22 |
915 | _ | _ | |a DBCoverage |0 StatID:(DE-HGF)0199 |2 StatID |b Clarivate Analytics Master Journal List |d 2020-08-22 |
920 | _ | _ | |l yes |
920 | 1 | _ | |0 I:(DE-Juel1)JSC-20090406 |k JSC |l Jülich Supercomputing Center |x 0 |
980 | _ | _ | |a journal |
980 | _ | _ | |a VDB |
980 | _ | _ | |a I:(DE-Juel1)JSC-20090406 |
980 | _ | _ | |a UNRESTRICTED |
980 | 1 | _ | |a FullTexts |
Library | Collection | CLSMajor | CLSMinor | Language | Author |
---|