Show simple item record

contributor authorZhimin Xi
contributor authorByeng D. Youn
contributor authorChao Hu
date accessioned2017-05-09T00:39:31Z
date available2017-05-09T00:39:31Z
date copyrightOctober, 2010
date issued2010
identifier issn1050-0472
identifier otherJMDEDB-27932#101008_1.pdf
identifier urihttp://yetl.yabesh.ir/yetl/handle/yetl/144149
description abstractThe proper orthogonal decomposition method has been employed to extract the important field signatures of random field observed in an engineering product or process. Our preliminary study found that the coefficients of the signatures are statistically uncorrelated but may be dependent. To this point, the statistical dependence of the coefficients has been ignored in the random field characterization for probability analysis and design. This paper thus proposes an effective random field characterization method that can account for the statistical dependence among the coefficients for probability analysis and design. The proposed approach has two technical contributions. The first contribution is the development of a natural approximation scheme of random field while preserving prescribed approximation accuracy. The coefficients of the signatures can be modeled as random field variables, and their statistical properties are identified using the chi-square goodness-of-fit test. Then, as the paper’s second technical contribution, the Rosenblatt transformation is employed to transform the statistically dependent random field variables into statistically independent random field variables. The number of the transformation sequences exponentially increases as the number of random field variables becomes large. It was found that improper selection of a transformation sequence among many may introduce high nonlinearity into system responses, which may result in inaccuracy in probability analysis and design. Hence, this paper proposes a novel procedure of determining an optimal sequence of the Rosenblatt transformation that introduces the least degree of nonlinearity into the system response. The proposed random field characterization can be integrated with any advanced probability analysis method, such as the eigenvector dimension reduction method or polynomial chaos expansion method. Three structural examples, including a microelectromechanical system bistable mechanism, are used to demonstrate the effectiveness of the proposed approach. The results show that the statistical dependence in the random field characterization cannot be neglected during probability analysis and design. Moreover, it is shown that the proposed random field approach is very accurate and efficient.
publisherThe American Society of Mechanical Engineers (ASME)
titleRandom Field Characterization Considering Statistical Dependence for Probability Analysis and Design
typeJournal Paper
journal volume132
journal issue10
journal titleJournal of Mechanical Design
identifier doi10.1115/1.4002293
journal fristpage101008
identifier eissn1528-9001
keywordsProbability
keywordsDesign
keywordsModeling AND Materials properties
treeJournal of Mechanical Design:;2010:;volume( 132 ):;issue: 010
contenttypeFulltext


Files in this item

Thumbnail

This item appears in the following Collection(s)

Show simple item record