article · 2007
Parallel Stochastic Finite Element Analysis on Distributed Memory Multiprocessors
Chinchalkar And, S. Chinchalkar, D. L. Taylor
Cited by 0Abstract and details only
Abstract
This paper describes methods for parallel stochastic finite element analysis using distributed memory multiprocessors. The conventional finite element method is deterministic and fails to account for uncertainties in loads, material properties, geometry and boundary conditions. Stochastic fem can be used to compute the variability in the structural response. It requires considerable more computational effort than the deterministic method, making it necessary to exploit parallelism in order to solve large, realistic structural problems. This paper describes parallel versions of the First Order Second Moment method as well as Monte Carlo simulation for analyzing structures with random material properties. Stochastic fem is composed of several sub-problems such as solution of a banded system of equations, the real symmetric eigenvalue problem, sensitivity analysis and structural reanalysis. Parallelism can be exploited at all stages of the solution process. These methods were implemented ...
Cite this paper
And, C., Chinchalkar, S., & Taylor, D. L. (2007). Parallel Stochastic Finite Element Analysis on Distributed Memory Multiprocessors.
And, Chinchalkar, et al. “Parallel Stochastic Finite Element Analysis on Distributed Memory Multiprocessors.” 2007.
And, Chinchalkar, S. Chinchalkar, and D. L. Taylor. 2007. “Parallel Stochastic Finite Element Analysis on Distributed Memory Multiprocessors.”
And, C., Chinchalkar, S. and Taylor, D.L. (2007) ‘Parallel Stochastic Finite Element Analysis on Distributed Memory Multiprocessors’.
@article{and2007parallel,
title = {{Parallel Stochastic Finite Element Analysis on Distributed Memory Multiprocessors}},
author = {And, Chinchalkar and Chinchalkar, S. and Taylor, D. L.},
year = {2007}
}Made from the index record. Check names, capitalisation and page numbers against the paper. Need another style? AI for Research has over 10,000.