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:Journal Papers
First Author:Luo, Yangjun
Correspondence Author:Luo, YJ (reprint author), Dalian Univ Technol, Sch Aeronaut & Astronaut, State Key Lab Struct Anal Ind Equipment, Dalian 116024, Peoples R China.
Co-author:Xing, Jian,Kang, Zhan
Date of Publication:2020-06-01
Journal:COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING
Included Journals:SCIE
Volume:364
ISSN No.:0045-7825
Key Words:Topology optimization; Non-gradient; Kriging surrogate model; Material-field series expansion
Abstract:Topology optimization is now a very effective and important tool for designing the layouts of various structural and multidisciplinary problems, but most existing methods require information about the sensitivity of the performance function with respect to an enormous number of design variables. This paper presents an efficient non-gradient approach to the topology optimization of structures when no information is available about design sensitivity. Based on the material-field series expansion (MFSE), the problem of topology optimization is constructed as a constrained minimization model with the series expansion coefficients as the design variables, thereby involving a considerable reduction of design variables. The Kriging-based optimization algorithm incorporating two infill criteria is used to solve the optimization problem. A special strategy of (i) using a self-adjusting design domain and (ii) remodeling the surrogate function is proposed to improve the searching efficiency of the Kriging-based algorithm. Several examples are given in the form of linear, nonlinear, and fluid topology optimization problems to demonstrate the effectiveness and applicability of the proposed Kriging-based MFSE method. (C) 2020 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
