Robust aerodynamic shape design based on an adaptive stochastic optimization framework

Xiaojing Wu, Weiwei Zhang, Shufang Song

科研成果: 期刊稿件文章同行评审

33 引用 (Scopus)

摘要

Optimization techniques combined with uncertainty quantification are computationally expensive for robust aerodynamic optimization due to expensive CFD costs. Surrogate model technology can be used to improve the efficiency of robust optimization. In this paper, non-intrusive polynomial chaos method and Kriging model are used to construct a surrogate model that associate stochastic aerodynamic statistics with airfoil shapes. Then, global search algorithm is used to optimize the model to obtain optimal airfoil fast. However, optimization results always depend on the approximation accuracy of the surrogate model. Actually, it is difficult to achieve a high accuracy of the model in the whole design space. Therefore, we introduce the idea of adaptive strategy to robust aerodynamic optimization and propose an adaptive stochastic optimization framework. The surrogate model is updated adaptively by increasing training airfoils according to historical optimization results to guarantee the accuracy near the optimal design point, which can greatly reduce the number of training airfoils. The proposed method is applied to a robust aerodynamic shape optimization for drag minimization considering uncertainty of Mach number in transonic region. It can be concluded that the proposed method can obtain better optimal results more efficiently than the traditional robust optimization method and global surrogate model method.

源语言英语
页(从-至)639-651
页数13
期刊Structural and Multidisciplinary Optimization
57
2
DOI
出版状态已出版 - 1 2月 2018

指纹

探究 'Robust aerodynamic shape design based on an adaptive stochastic optimization framework' 的科研主题。它们共同构成独一无二的指纹。

引用此