自适应设计空间扩展的高效代理模型气动优化设计方法

Translated title of the contribution: Efficient surrogate-based aerodynamic design optimization method with adaptive design space expansion

Chao Wang, Zhenghong Gao, Wei Zhang, Lu Xia, Jiangtao Huang

Research output: Contribution to journalArticlepeer-review

14 Scopus citations

Abstract

The infill criterion and design space construction in Kriging-based aerodynamic shape optimization are studied in this paper. A hybrid infill method is proposed, which combines the Expected Improvement (EI) criterion and the Minimum Prediction (MP) criterion using an EI threshold. Global exploration is first implemented by the IE criterion, and local exploitation is then implemented by the MP criterion. Consequently, the convergence rate of Efficient Global Optimization (EGO) is accelerated in a certain design space. To find the global optimum in aerodynamic shape optimization, expansion of the design variable range and multi-round method are employed. Influence of the variable range on the size of design space and density of samples are discussed. To improve the efficiency of samples, an adaptive design space expansion method is proposed. In this method, the design space is dynamic and the range of design variable is expanded in potential dimensions. Accordingly, the samples are allocated efficiently through adaptive expansion of design space boundaries. ADODG airfoil optimization cases show that the adaptive design space expansion method has remarkable superiority over the conventional fixed design space method.

Translated title of the contributionEfficient surrogate-based aerodynamic design optimization method with adaptive design space expansion
Original languageChinese (Traditional)
Article number121745
JournalHangkong Xuebao/Acta Aeronautica et Astronautica Sinica
Volume39
Issue number7
DOIs
StatePublished - 25 Jul 2018

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