ISSN: 1543-1649 Print

ISSN: 1940-4352 Online

	You can order a single issue or an individual article, as well as view the table of contents or article abstract by clicking on the volume number, then the issue number in the right sidebar.
Institutional price: $1197.00
Online subscription

Year 2004, Volume 2 / Issue 3

DOI: 10.1615/IntJMultCompEng.v2.i3

Pages: 180

DOI: 10.1615/IntJMultCompEng.v2.i3.50

Article price - $35.00

Multilevel Parallel Programming for Multiscale Modeling of Composite Materials

Paul M. Eder
Department of Mechanical Engineering, Ohio State University, Columbus, Ohio 43210

James E. Giuliani
Science and Technology Support Group, The Ohio Supercomputer Center 1224 Kinnear Rd., Columbus, Ohio, 43210

Somnath Ghosh
Ohio State University

This paper presents work aimed at implementing an efficient multilevel parallel model for an adaptive multiscale finite element model. The FEM model combines a traditional displacement based finite element model with a microstructural Voronoi cell finite element method (VCFEM) for multiscale modeling of heterogeneous microstructures with nonuniform microstructural heterogeneities. Three levels of hierarchy are used in the model, including (a) level-0 of pure macroscopic analysis; (b) level-1 of macro-micro coupling, used for signaling the switch over from macroscopic analyses to pure microscopic analyses; and (c) level-2 regions of pure microscopic modeling. A distributed/shared memory (DSM) cluster system is used for code development and execution, where multiprocessor nodes offer shared memory on the node and distributed memory between the nodes. The approach uses multiple parallel models to efficiently distribute the level-1 and level-2 workloads across multiple workstations based on computational requirements. The Message Passing Interface (MPI) library is used for distributed memory decomposition between nodes, and multithreading using the OpenMP (OMP) library is used for shared memory decomposition on each node. An efficient iterative multigrid solver is also integrated. The details of these implementations are discussed and numerical results, which demonstrate the ability of the parallel model to solve problems in a fast and efficient manner, are provided.

pages 23

<< Previous article   Next article >>