Variable-fidelity optimization with design space reduction

作     者：Mohammad Kashif Zahir Gao Zhenghong 

作者机构：School of Aeronautics Northwestern Polytechnical University 

出 版 物：《Chinese Journal of Aeronautics》 (中国航空学报（英文版）)

年 卷 期：2013年第26卷第4期

页      码：841-849页

摘      要：Advanced engineering systems, like aircraft, are defined by tens or even hundreds of design variables. Building an accurate surrogate model for use in such high-dimensional optimization problems is a difficult task owing to the curse of dimensionality. This paper presents a new algorithm to reduce the size of a design space to a smaller region of interest allowing a more accurate surrogate model to be generated. The framework requires a set of models of different physical or numerical fidelities. The low-fidelity （LF） model provides physics-based approximation of the high-fidelity （HF） model at a fraction of the computational cost. It is also instrumental in identifying the small region of interest in the design space that encloses the high-fidelity optimum. A surrogate model is then constructed to match the low-fidelity model to the high-fidelity model in the identified region of interest. The optimization process is managed by an update strategy to prevent convergence to false optima. The algorithm is applied on mathematical problems and a two-dimen-sional aerodynamic shape optimization problem in a variable-fidelity context. Results obtained are in excellent agreement with high-fidelity results, even with lower-fidelity flow solvers, while showing up to 39% time savings.

主 题 词：Airfoil optimization Curse of dimensionality Design space reduction Genetic algorithms Kriging Surrogate models Surrogate update strategies Variable fidelity 

学科分类：08[工学] 082501[082501] 0825[工学-环境科学与工程类] 

核心收录：

D　O　I：10.1016/j.cja.2013.06.002

馆 藏 号：203119524...