Crashworthiness Optimization Using a Surrogate Approach by Stochastic Response Surface

Soulier, Bruno; Richard, Lionel; Hazet, Bruno; Braibant, V.

doi:10.1007/978-94-017-0161-7_16

Cited by 2 publications

(3 citation statements)

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“…Many types of metamodels can be used to find an approximate solution: they can be based on polynomial regression [23,24], on neural networks [25,26], on radial basis functions [27], on proper orthogonal decomposition [28], on cumulative interpolation [29], etc... We chose to use a particular class of metamodels called kriging approximations [30] and, more precisely, a cokriging metamodel [31] using derivatives [32]. These approximations are presented in Section 4.…”

Section: Fig 1: Multilevel Optimizationmentioning

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: Fig 1: Multilevel Optimizationmentioning

confidence: 99%

Generation of a cokriging metamodel using a multiparametric strategy

2012

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“…Here, we use an interpolation model based on a cumulative approach. This cumulative interpolation model (Soulier et al, 2003), which can be called diffuse interpolation, is defined by:…”

Section: Construction Of the Metamodel By Cumulative Approximationmentioning

confidence: 99%

“…Besides the construction of continuous and differentiable global approximations over the whole design space, the metamodel should enable updating the approximation iteratively throughout the optimization procedure. Among many kind of metamodels with these properties like polynomial regression (Box and Wilson, 1951), neural networks (Haykin, 1994), radial basis functions (Hardy, 1971), kriging (Cressie, 1990), this paper focuses on cumulative interpolation (Soulier et al, 2003). The second part reviews the fundamentals of the multiparametric method based on the LATIN approach and emphasizes its advantages in the optimization context.…”

Section: Introductionmentioning

confidence: 99%

A multiparametric strategy for the two step optimization of structural assemblies

Soulier

Boucard

2013

Struct Multidisc Optim

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International audienceGenerally speaking, the objective and constraint functions of a structural optimization problem are implicit with respect to the design variables; their evaluation requires finite element analyses which constitute the most expensive steps of the optimization algorithm. The work presented in this paper concerns the implementation of a two step optimization strategy which consists in optimizing first an empirical model (metamodel), then the full model. In the framework of multilevel model optimization, the computation costs are related, on the one hand, to the construction of global approximations and, on the other hand, to the optimization of the full model. Thus, many numerical simulations are required in order to perform a multilevel optimization. In this context, the objective of associating a multiparametric strategy based on the nonincremental LATIN method with the two step optimization process is to reduce these computation costs. The performance gains thus achieved will be illustrated through the optimization of structural assemblies involving contact with friction. The results obtained will show that the savings associated with the multiparametric procedure can reach a factor of 30

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Crashworthiness Optimization Using a Surrogate Approach by Stochastic Response Surface

Cited by 2 publications

References 4 publications

Generation of a cokriging metamodel using a multiparametric strategy

Generation of a cokriging metamodel using a multiparametric strategy

A multiparametric strategy for the two step optimization of structural assemblies

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