Optimum structural design with parallel finite element analysis

Elsayed, Mohamed; Hsiung, Ching-Kuo

doi:10.1016/0045-7949(91)90417-k

Cited by 17 publications

(6 citation statements)

References 4 publications

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“…Similar strategies have also been used in the context of multidisciplinary optimization where each level of the optimization process concerns a specific discipline [9,16]. Parallel multilevel model optimization [17,18] can also be used in order to reduce the optimization cost and take maximum advantage of parallel computing architectures. This type of multilevel strategy calls for specific optimization algorithms, such as genetic algorithms [19][20][21].…”

Section: The Optimization Processmentioning

confidence: 99%

Generation of a cokriging metamodel using a multiparametric strategy

2012

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In the course of designing structural assemblies, performing a full optimization is very expensive in terms of computation time. In order or reduce this cost, we propose a multilevel model optimization approach. This paper lays the foundations of this strategy by presenting a method for constructing an approximation of an objective function. This approach consists in coupling a multiparametric mechanical strategy based on the LATIN method with a gradientbased metamodel called a cokriging metamodel. The main difficulty is to build an accurate approximation while keeping the computation cost low. Following an introduction to multiparametric and cokriging strategies, the performance of kriging and cokriging models is studied using one-and two-dimensional analytical functions; then, the performance of metamodels built from mechanical responses provided by the multiparametric strategy is analyzed based on two mechanical test examples.

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Section: The Optimization Processmentioning

confidence: 99%

Generation of a cokriging metamodel using a multiparametric strategy

2012

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“…However, the research on parallelized methods for eigenvalue problems is still active (Hwang and Parsons, 1994;Katagiri and Kanada, 2001;Lang, 1999;Lui, 1998;Watkins et al, 1996). With regard to structural design optimization problems, various parallel processing ideas such as the parallelization of an optimization algorithm (Padula and Stone, 1998), the combination of the parallel finite element analysis with a serial optimization algorithm (El-Sayed and Hsiung, 1991), structural optimization based on the domain decomposition (Wang et al, 1996) have been proposed. Recently, parallel processing techniques have been applied to structural optimization problems using the zeroth-order optimizer such as genetic algorithms and evolutionary strategies (Thierauf and Cai, 1997;Topping and Leite, 1999).…”

Section: Introductionmentioning

confidence: 99%

Parallelized structural topology optimization for eigenvalue problems

Kim

2004

International Journal of Solids and Structures

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“…As recalled in [1], the most efficient optimization methods rely on multilevel optimization concepts, which can be divided into three categories: parallel model optimization based on domain decomposition methods [2,3], multilevel parameter optimization, which consists in replacing an optimization problem by several subproblems, each with a reduced set of parameters [4][5][6][7], and multilevel model optimization, which introduces several modeling levels [8][9][10].…”

Section: Introductionmentioning

confidence: 99%