克里金
空气动力学
多学科设计优化
计算机科学
数学优化
忠诚
元建模
高保真
数学
工程类
航空航天工程
机器学习
多学科方法
电信
社会科学
电气工程
社会学
程序设计语言
作者
Yu Zhang,Zhonghua Han,Wenping Song
标识
DOI:10.1080/0305215x.2024.2310182
摘要
To reduce the computational burden of aerodynamic design optimization, a multi-fidelity expected improvement (MFEI) method is developed, based on the error analysis of a multi-level hierarchical kriging (MHK) model for accelerating optimization convergence. By maximizing the MFEI function, a new sample of arbitrary fidelity level is determined to ameliorate the accuracy of the MHK model, and convergence to the high-fidelity optimum is ensured. The proposed optimization method based on MFEI and MHK is demonstrated by two analytical function cases and verified by two aerodynamic design optimizations: drag minimizations of an RAE2822 aerofoil and an ONERA M6 wing in transonic flows. It is shown that the MFEI method tends to infill more gainful samples of lower fidelities during optimization, so fewer highest-fidelity samples are required. This confirms that the proposed method can obtain optimal results within a limited computational budget and is more efficient than the existing single-fidelity or two-fidelity methods.
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