A new adaptive response surface method for reliability analysis

Roussouly, Nicolas; Petitjean, Frank; Salaün, Michel

doi:10.1016/j.probengmech.2012.10.001

Cited by 154 publications

(84 citation statements)

References 34 publications

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“…The method of the response surface approach leads to the construction of an algebraic expression η: ℝ n → ℝ, most often a polynomial, that is able to approximate the function of failure H(u) or G(x) through its assessment of at different points in the region followed by a regression over these points (Roussouly et al 2013;Gayton et al 2003). This method can be inadequate, though, for a completely unknown failure function.…”

Section: Response Surface Methodsmentioning

confidence: 99%

Adaptive response surface method supporting finite element calculations: an application to power electronic module reliability assessment

Martín¹,

Micol²,

Pérès³

2016

Struct Multidisc Optim

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To cite this version:Carmen Martin, Alexandre Micol, François Pérès. Adaptive response surface method supporting finite element calculations: an application to power electronic module reliability assessment. Structural and Multidisciplinary Optimization, Springer Verlag (Germany), 2016, 54 ( 6) Abstract In this paper a method is proposed, introducing an adaptive response surface allowing, on the one hand, to minimize the number of calls to finite element codes for the assessment of the parameters of the response surface and, on the other hand, to refine the solution around the design point by iterating through the procedure. This method is implemented in a parallel environment to optimize the calculation time with respect of the architecture of a computational cluster.

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Section: Response Surface Methodsmentioning

confidence: 99%

Adaptive response surface method supporting finite element calculations: an application to power electronic module reliability assessment

Martín¹,

Micol²,

Pérès³

2016

Struct Multidisc Optim

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“…Liu and Li presented an improved adaptive RSM based on uniform design and double weighted regression [16]. Roussouly et al developed a new adaptive response surface method using a sparse response surface and a relevant criterion for result accuracy [17]. Overall, the RSM has been the subject of extensive studies because of its simple form and strong operability.…”

Section: Advances In Mechanical Engineeringmentioning

confidence: 99%

A New Sampling Approach for Response Surface Method Based Reliability Analysis and Its Application

Meng

Jing

Zhang

et al. 2014

Advances in Mechanical Engineering

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A response surface method based on the all sample point interpolation approach (ASPIA) is proposed to improve the efficiency of reliability computation. ASPIA obtains new sample points through linear interpolation. These new sample points are occasionally extremely dense, thus easily generating an ill-conditioned problem for approximation functions. A mobile most probable failure point strategy is used to solve this problem. The advantage of the proposed method is proven by two numerical examples. With the ASPIA, the approximated process can rapidly approach the actual limit equation with accuracy. Additionally, there are no difficulties in applying the proposed ASPIA to the response surface model with or without cross terms. Solution results for the numerical examples indicate that the use of the response surface function with cross terms increases time cost, but result accuracy cannot be improved significantly. Finally, the proposed method is successfully used to analyze the reliability of the hydraulic cylinder of a forging hydraulic press by combining MATLAB and ANSYS software. The engineering example confirms the practicality of this method.

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“…For structural reliability assessment with implicit limit state functions, surrogate model-based approaches such as polynomial response surfaces [22], artificial neural network (ANN) [23], support vector machine (SVM) [24] and Kriging model [12], are solution to substantially reduce the numerical cost of the reliability assessment, compared …”

Section: Introductionmentioning

confidence: 99%

“…Recently, Roussouly et al[22] proposed a new adaptive response surface method for reliability analysis. It consists in an adaptive scheme where the response surface is iteratively refined in the region which mainly contributes to the probability of failure, i.e.…”

mentioning

confidence: 99%

Probabilistic life assessment on fatigue crack growth in mixed-mode by coupling of Kriging model and finite element analysis

Liu

Xie

2015

Engineering Fracture Mechanics

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A new adaptive response surface method for reliability analysis

Cited by 154 publications

References 34 publications

Adaptive response surface method supporting finite element calculations: an application to power electronic module reliability assessment

Adaptive response surface method supporting finite element calculations: an application to power electronic module reliability assessment

A New Sampling Approach for Response Surface Method Based Reliability Analysis and Its Application

Probabilistic life assessment on fatigue crack growth in mixed-mode by coupling of Kriging model and finite element analysis

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