000875379 001__ 875379
000875379 005__ 20220930130238.0
000875379 0247_ $$2doi$$a10.1007/s13272-020-00450-1
000875379 0247_ $$2ISSN$$a1869-5582
000875379 0247_ $$2ISSN$$a1869-5590
000875379 0247_ $$2Handle$$a2128/25529
000875379 0247_ $$2altmetric$$aaltmetric:82010743
000875379 037__ $$aFZJ-2020-01990
000875379 041__ $$aEnglish
000875379 082__ $$a620
000875379 1001_ $$0P:(DE-Juel1)165948$$aLintermann, Andreas$$b0$$eCorresponding author$$ufzj
000875379 245__ $$aLattice–Boltzmann simulations for complex geometries on high-performance computers
000875379 260__ $$aWien [u.a.]$$bSpringer$$c2020
000875379 3367_ $$2DRIVER$$aarticle
000875379 3367_ $$2DataCite$$aOutput Types/Journal article
000875379 3367_ $$0PUB:(DE-HGF)16$$2PUB:(DE-HGF)$$aJournal Article$$bjournal$$mjournal$$s1597735746_12991
000875379 3367_ $$2BibTeX$$aARTICLE
000875379 3367_ $$2ORCID$$aJOURNAL_ARTICLE
000875379 3367_ $$00$$2EndNote$$aJournal Article
000875379 520__ $$aComplex geometries pose multiple challenges to the field of computational fluid dynamics. Grid generation for intricate objects is often difficult and requires accurate and scalable geometrical methods to generate meshes for large-scale computations. Such simulations, furthermore, presume optimized scalability on high-performance computers to solve high-dimensional physical problems in an adequate time. Accurate boundary treatment for complex shapes is another issue and influences parallel load-balance. In addition, large serial geometries prevent efficient computations due to their increased memory footprint, which leads to reduced memory availability for computations. In this paper, a framework is presented that is able to address the aforementioned problems. Hierarchical Cartesian boundary-refined meshes for complex geometries are obtained by a massively parallel grid generator. In this process, the geometry is parallelized for efficient computation. Simulations on large-scale meshes are performed by a high-scaling lattice–Boltzmann method using the second-order accurate interpolated bounce-back boundary conditions for no-slip walls. The method employs Hilbert decompositioning for parallel distribution and is hybrid MPI/OpenMP parallelized. The parallel geometry allows to speed up the pre-processing of the solver and massively reduces the local memory footprint. The efficiency of the computational framework, the application of which to, e.g., subsonic aerodynamic problems is straightforward, is shown by simulating clearly different flow problems such as the flow in the human airways, in gas diffusion layers of fuel cells, and around an airplane landing gear configuration
000875379 536__ $$0G:(DE-HGF)POF3-511$$a511 - Computational Science and Mathematical Methods (POF3-511)$$cPOF3-511$$fPOF III$$x0
000875379 536__ $$0G:(DE-Juel1)jhpc54_20180501$$aRhinodiagnost (jhpc54_20180501)$$cjhpc54_20180501$$fRhinodiagnost$$x1
000875379 588__ $$aDataset connected to CrossRef
000875379 7001_ $$0P:(DE-HGF)0$$aSchröder, Wolfgang$$b1
000875379 773__ $$0PERI:(DE-600)2610302-3$$a10.1007/s13272-020-00450-1$$p745-766$$tCEAS Aeronautical Journal$$v11$$x1869-5590$$y2020
000875379 8564_ $$uhttps://juser.fz-juelich.de/record/875379/files/Lintermann-Schr%C3%B6der2020_Article_LatticeBoltzmannSimulationsFor.pdf$$yOpenAccess
000875379 8564_ $$uhttps://juser.fz-juelich.de/record/875379/files/Lintermann-Schr%C3%B6der2020_Article_LatticeBoltzmannSimulationsFor.pdf?subformat=pdfa$$xpdfa$$yOpenAccess
000875379 8767_ $$92020-04-03$$d2020-05-06$$eHybrid-OA$$jDEAL$$lDEAL: Springer$$pCANJ-D-18-00167R3$$zApproved durch MPDL
000875379 909CO $$ooai:juser.fz-juelich.de:875379$$pVDB$$pdriver$$pOpenAPC_DEAL$$popen_access$$popenaire$$popenCost$$pdnbdelivery
000875379 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)165948$$aForschungszentrum Jülich$$b0$$kFZJ
000875379 9101_ $$0I:(DE-588b)36225-6$$6P:(DE-HGF)0$$aRWTH Aachen$$b1$$kRWTH
000875379 9131_ $$0G:(DE-HGF)POF3-511$$1G:(DE-HGF)POF3-510$$2G:(DE-HGF)POF3-500$$3G:(DE-HGF)POF3$$4G:(DE-HGF)POF$$aDE-HGF$$bKey Technologies$$lSupercomputing & Big Data$$vComputational Science and Mathematical Methods$$x0
000875379 9141_ $$y2020
000875379 915__ $$0StatID:(DE-HGF)0200$$2StatID$$aDBCoverage$$bSCOPUS
000875379 915__ $$0StatID:(DE-HGF)0510$$2StatID$$aOpenAccess
000875379 915__ $$0LIC:(DE-HGF)CCBY4$$2HGFVOC$$aCreative Commons Attribution CC BY 4.0
000875379 9201_ $$0I:(DE-Juel1)JSC-20090406$$kJSC$$lJülich Supercomputing Center$$x0
000875379 980__ $$ajournal
000875379 980__ $$aVDB
000875379 980__ $$aUNRESTRICTED
000875379 980__ $$aI:(DE-Juel1)JSC-20090406
000875379 980__ $$aAPC
000875379 9801_ $$aAPC
000875379 9801_ $$aFullTexts