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dc.contributor.authorNaseri, Alireza
dc.contributor.authorTotounferoush, Amin
dc.contributor.authorGonzález Acedo, Ignacio
dc.contributor.authorMehl, Miriam
dc.contributor.authorPérez Segarra, Carlos David
dc.contributor.otherUniversitat Politècnica de Catalunya. Doctorat en Enginyeria Tèrmica
dc.contributor.otherUniversitat Politècnica de Catalunya. Departament de Màquines i Motors Tèrmics
dc.identifier.citationNaseri, A. [et al.]. A scalable framework for the partitioned solution of fluid-structure interaction problems. "Computational mechanics", Agost 2020, vol. 66, núm. 2, p. 471-489.
dc.description.abstractIn this work, we present a scalable and efficient parallel solver for the partitioned solution of fluid–structure interaction problems through multi-code coupling. Two instances of an in-house parallel software, TermoFluids, are used to solve the fluid and the structural sub-problems, coupled together on the interface via the preCICE coupling library. For fluid flow, the Arbitrary Lagrangian–Eulerian form of the Navier–Stokes equations is solved on an unstructured conforming grid using a second-order finite-volume discretization. A parallel dynamic mesh method for unstructured meshes is used to track the moving boundary. For the structural problem, the nonlinear elastodynamics equations are solved on an unstructured grid using a second-order finite-volume method. A semi-implicit FSI coupling method is used which segregates the fluid pressure term and couples it strongly to the structure, while the remaining fluid terms and the geometrical nonlinearities are only loosely coupled. A robust and advanced multi-vector quasi-Newton method is used for the coupling iterations between the solvers. Both the fluid and the structural solver use distributed-memory parallelism. The intra-solver communication required for data update in the solution process is carried out using non-blocking point-to-point communicators. The inter-code communication is fully parallel and point-to-point, avoiding any central communication unit. Inside each single-physics solver, the load is balanced by dividing the computational domain into fairly equal blocks for each process. Additionally, a load balancing model is used at the inter-code level to minimize the overall idle time of the processes. Two practical test cases in the context of hemodynamics are studied, demonstrating the accuracy and computational efficiency of the coupled solver. Strong scalability test results show a parallel efficiency of 83% on 10,080 CPU cores.
dc.description.sponsorshipThis work was financially supported by—Ministerio de Economía y Competitividad, Secretaría de Estado de Investigación, Desarrollo e Innovación, Spain (ENE2017-88697-R),—priority program 1648—Software for Exascale Computing 214 (ExaFSA - Exascale Simulation of Fluid–Structure–Acoustics Interactions) of the German Research Foundation,—and a FI Ph.D. scholarship by the Agència de Gestió d’Ajuts Universitaris i de Recerca (AGAUR) of Generalitat de Catalunya (Spain). The performance measurements were carried out on the SuperMUC supercomputer at Leibniz Rechenzentrum (LRZ) der Bayerischen Akademie der Wissenschaften. The authors wish to thank LRZ for the computing time and the technical support.
dc.format.extent19 p.
dc.subjectÀrees temàtiques de la UPC::Enginyeria mecànica::Mecànica de fluids
dc.subject.lcshFluid-structure interaction
dc.subject.lcshHigh performance computing
dc.subject.otherFluid-structure interaction
dc.subject.otherPartitioned method
dc.subject.otherMulti-code coupling
dc.subject.otherHigh performance computing
dc.titleA scalable framework for the partitioned solution of fluid-structure interaction problems
dc.subject.lemacInteracció fluid-estructura
dc.subject.lemacCàlcul intensiu (Informàtica)
dc.contributor.groupUniversitat Politècnica de Catalunya. CTTC - Centre Tecnològic de la Transferència de Calor
dc.description.peerreviewedPeer Reviewed
dc.rights.accessOpen Access
dc.description.versionPostprint (author's final draft)
local.citation.authorNaseri, A.; Totounferoush, A.; Gonzalez, I.; Mehl, M.; Perez, C.
local.citation.publicationNameComputational mechanics

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