Parallel computation of meshless methods for explicit dynamic analysis

A parallel computational implementation of modern meshless methods is presented for explicit dynamic analysis. The procedures are demonstrated by application of the Reproducing Kernel Particle Method (RKPM). Aspects of a coarse grain parallel paradigm are detailed for a Lagrangian formulation using...

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Veröffentlicht in:International journal for numerical methods in engineering 2000-03, Vol.47 (7), p.1323-1341
Hauptverfasser: Danielson, Kent T., Hao, Su, Liu, Wing Kam, Uras, R. Aziz, Li, Shoafan
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container_issue 7
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container_title International journal for numerical methods in engineering
container_volume 47
creator Danielson, Kent T.
Hao, Su
Liu, Wing Kam
Uras, R. Aziz
Li, Shoafan
description A parallel computational implementation of modern meshless methods is presented for explicit dynamic analysis. The procedures are demonstrated by application of the Reproducing Kernel Particle Method (RKPM). Aspects of a coarse grain parallel paradigm are detailed for a Lagrangian formulation using model partitioning. Integration points are uniquely defined on separate processors and particle definitions are duplicated, as necessary, so that all support particles for each point are defined locally on the corresponding processor. Several partitioning schemes are considered and a reduced graph‐based procedure is presented. Partitioning issues are discussed and procedures to accommodate essential boundary conditions in parallel are presented. Explicit MPI message passing statements are used for all communications among partitions on different processors. The effectiveness of the procedure is demonstrated by highly deformable inelastic example problems. Copyright © 2000 John Wiley & Sons, Ltd.
doi_str_mv 10.1002/(SICI)1097-0207(20000310)47:7<1323::AID-NME827>3.0.CO;2-0
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subjects explicit dynamics
inelasticity
large deformation
meshless methods
parallel computing
reproducing kernel particle methods
title Parallel computation of meshless methods for explicit dynamic analysis
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