Zhan Kang
Professor Supervisor of Doctorate Candidates Supervisor of Master's Candidates
Main positions:Deputy Dean, Faculty of Vehicle Engineering and Mechanics
Other Post:Deputy Dean, Faculty of Vehicle Engineering and Mechanics
Gender:Male
Alma Mater:Stuttgart University, Germany
Degree:Doctoral Degree
School/Department:Department of Engineering Mechanics/ State Key Laboratory of Structural Analysis for Industrial Equimpment
Discipline:Engineering Mechanics. Computational Mechanics. Aerospace Mechanics and Engineering. Solid Mechanics
Business Address:https://orcid.org/0000-0001-6652-7831
http://www.ideasdut.com
https://scholar.google.com/citations?user=PwlauJAAAAAJ&hl=zh-CN&oi=ao
https://www.researchgate.net/profile/Zhan_Kang
Contact Information:zhankang#dlut.edu.cn 13190104312
E-Mail:zhankang@dlut.edu.cn
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Indexed by:期刊论文
Date of Publication:2016-03-01
Journal:COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING
Included Journals:SCIE、EI
Volume:300
Page Number:461-489
ISSN No.:0045-7825
Key Words:Bounded uncertainty; Non-probabilistic model; Convex model; SDP; Structural reliability; Reliability-based topology optimization
Abstract:As a set theory-based convex model, the ellipsoidal model provides an attractive framework for treating uncertain-but-bounded variations in the structural reliability analysis and design optimization. However, improper modeling of the uncertainties may give rise to misleading non-probabilistic reliability analysis, thus result in either unsafe or over-conservative designs. This paper presents a systematic study on the mathematical formulation for constructing the minimum-volume ellipsoidal convex model using a given set of sample data, and shows its application in existing methods of non-probabilistic reliability analysis and design optimization of structures with bounded uncertainties. In this method, the uncertain parameters are first divided into groups according to their sources. For each individual group of uncertainties, the minimum-volume ellipsoid problem is reformulated into a semi-definite programming (SDP) problem and thus can be efficiently solved to its global optimum. Further, a linear transformation based on the eigenvalue analysis is employed to map the ellipsoidal model into a standard uncertainty space. This uncertainty modeling technique enables a compact and differentiable bound description of the parameter variations. Moreover, it has another useful property, the affine invariance, which is shown to be necessary for meaningful definition of a non-probabilistic reliability index. The effectiveness and efficiency of the present techniques for convex model construction and the corresponding reliability analysis are demonstrated with numerical examples of structural topology optimization problems with bounded variations arising from different sources. (C) 2015 Elsevier B.V. All rights reserved.
Dr. Zhan Kang is a Changjiang Scholar Chair Professor of Dalian University of Technology. He graduated from Shanghai Jiaotong University in 1992, received his MEng in mechanics from Dalian University of Technology in 1995 and his Dr. –Ing. degree from Stuttgart University, Germany in 2005. His current research involves issues such as topology optimization, structural optimization under uncertainties, design optimization of smart structures and nanomechanics. Dr. Kang has published over 100 research papers in peer-reviewed international journals and one monograph. He has received 5500 citations and has an H-index of 39 (Google Scholar). Dr. Kang has been granted the Outstanding Youth Fund of Natural Science Foundation of China (NSFC). He has been principal investigator of 8 NSFC projects and a Key Project of Chinese National Programs for Fundamental Research and Development (973 Project). He has also conducted many consultancy projects.
Google Scholar Page: https://scholar.google.com/citations?user=PwlauJAAAAAJ&hl=zh-CN&oi=ao
https://orcid.org/0000-0001-6652-7831
http://www.ideasdut.com
