A local sampling method with variable radius for RBDO using Kriging. Issue 7 (5th October 2015)

• Record Type: Journal Article
• Authors: Li, Xiaoke; Qiu, Haobo; Chen, Zhenzhong; Gao, Liang; Shao, Xinyu
• Is Part Of: Engineering computations. Volume 32:Issue 7(2015)
• Page Start: 1908
• Page End: 1933
• Publication Date: 2015-10-05
• Subjects: Computer-aided engineering -- Periodicals; Computer graphics -- Periodicals; 620.00285
• DOI: 10.1108/EC-09-2014-0188
• Languages: English
• ISSNs: 0264-4401

Abstract

<abstract> <title> <x content-type="archive" xml:space="preserve">Abstract</x> </title> <sec> <title content-type="abstract-heading">Purpose</title> <p> – Kriging model has been widely adopted to reduce the high computational costs of simulations in Reliability-based design optimization (RBDO). To construct the Kriging model accurately and efficiently in the region of significance, a local sampling method with variable radius (LSVR) is proposed. The paper aims to discuss these issues. </p> </sec> <sec> <title content-type="abstract-heading">Design/methodology/approach</title> <p> – In LSVR, the sequential sampling points are mainly selected within the local region around the current design point. The size of the local region is adaptively defined according to the target reliability and the nonlinearity of the probabilistic constraint. Every probabilistic constraint has its own local region instead of all constraints sharing one local region. In the local sampling region, the points located on the constraint boundary and the points with high uncertainty are considered simultaneously. </p> </sec> <sec> <title content-type="abstract-heading">Findings</title> <p> – The computational capability of the proposed method is demonstrated using two mathematical problems, a reducer design and a box girder design of a super heavy machine tool. The comparison results show that the proposed method is very efficient and accurate. </p> </sec> <sec> <title content-type="abstract-heading">Originality/value</title> <p> – The main contribution of this paper lies in: a new local sampling region computational criterion is proposed for Kriging. The originality of this paper is using expected feasible function (EFF) criterion and the shortest distance to the existing sample points instead of the other types of sequential sampling criterion to deal with the low efficiency problem.</p> </sec> </abstract>