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contributor authorZhao, Liang
contributor authorChoi, K. K.
contributor authorLee, Ikjin
contributor authorGorsich, David
date accessioned2017-05-09T01:00:59Z
date available2017-05-09T01:00:59Z
date issued2013
identifier issn1050-0472
identifier othermd_135_09_091003.pdf
identifier urihttp://yetl.yabesh.ir/yetl/handle/yetl/152542
description abstractIn samplingbased reliabilitybased design optimization (RBDO) of largescale engineering applications, the Monte Carlo simulation (MCS) is often used for the probability of failure calculation and probabilistic sensitivity analysis using the prediction from the surrogate model for the performance function evaluations. When the number of samples used to construct the surrogate model is not enough, the prediction from the surrogate model becomes inaccurate and thus the Monte Carlo simulation results as well. Therefore, to count in the prediction error from the surrogate model and assure the obtained optimum design from samplingbased RBDO satisfies the probabilistic constraints, a conservative surrogate model, which is not overly conservative, needs to be developed. In this paper, a conservative surrogate model is constructed using the weighted Kriging variance where the weight is determined by the relative change in the corrected Akaike Information Criterion (AICc) of the dynamic Kriging model. The proposed conservative surrogate model performs better than the traditional Kriging prediction interval approach because it reduces fluctuation in the Kriging prediction bound and it performs better than the constant safety margin approach because it adaptively accounts large uncertainty of the surrogate model in the region where samples are sparse. Numerical examples show that using the proposed conservative surrogate model for samplingbased RBDO is necessary to have confidence that the optimum design satisfies the probabilistic constraints when the number of samples is limited, while it does not lead to overly conservative designs like the constant safety margin approach.
publisherThe American Society of Mechanical Engineers (ASME)
titleConservative Surrogate Model Using Weighted Kriging Variance for Sampling Based RBDO
typeJournal Paper
journal volume135
journal issue9
journal titleJournal of Mechanical Design
identifier doi10.1115/1.4024731
journal fristpage91003
journal lastpage91003
identifier eissn1528-9001
treeJournal of Mechanical Design:;2013:;volume( 135 ):;issue: 009
contenttypeFulltext


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