基于新型高维代理模型的气动外形设计方法研究

赵欢; 高正红; 夏露

doi:10.7527/S1000-6893.2022.26924

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DOI: https://doi.org/10.7527/S1000-6893.2022.26924

基于新型高维代理模型的气动外形设计方法研究

赵欢 ,
高正红 ,
夏露

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1. 中山大学
2. 西北工业大学
3. 西北工业大学航空学院

收稿日期: 2022-01-10

修回日期: 2022-02-21

网络出版日期: 2022-02-28

基金资助

国家自然科学青年基金

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Research on Novel High-Dimensional Surrogate Model-Based Aerodynamic Shape Design Optimization Method

ZHAO Huan ,
GAO Zheng-Hong ,
XIA Lu

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Received date: 2022-01-10

Revised date: 2022-02-21

Online published: 2022-02-28

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摘要

随着现代飞行器性能需求的不断提高，飞行器精细化气动优化设计要求更高可信度的CFD数值分析以及更多的独立设计变量个数，使得基于代理模型的全局优化算法在超过一定设计变量个数后效率显著降低，难以满足复杂工程设计需求。而目前的高维代理模型过程复杂和计算花费高，缺乏对广泛工程问题的适应性。针对以上难题，本文提出了监督式非线性降维代理建模方法用于缓解代理优化过程中的高维变量设计难题。该方法将核主成分分析（非线性）降维模型与高斯回归过程模型统一训练，自适应构建新型高维代理模型，并随着优化过程不断学习和改进模型，从而建立了从高维输入到输出的准确映射，有效解决了传统高维代理模型训练花费高和适应性差等难题。然后基于该新型代理模型发展了适用于飞行器复杂气动设计的高维全局优化设计方法，并将其应用到AIAA优化小组发布的两个复杂跨音速优化算例中。通过与传统代理优化方法进行全面比较，证实了新方法能大幅提高飞行器高维变量全局优化效率和全局寻优能力

关键词： 精细化气动优化; 基于代理模型的优化设计; 全局优化; 高维变量; 非线性降维代理模型; 高维全局优化设计

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赵欢 , 高正红 , 夏露 . 基于新型高维代理模型的气动外形设计方法研究[J]. 航空学报, 0 : 0 -0 . DOI: 10.7527/S1000-6893.2022.26924

Abstract

With the ever-increasing demands for the performance of modern aircrafts, the refined aerodynamic shape de-sign optimization of aircraft requires higher-fidelity computational fluid dynamics (CFD) numerical analysis and more independent design variables, causing the efficiency of surrogate-based global optimization algorithm significantly decreases after exceeding a certain number of design variables, which makes it difficult to meet the advanced demands for complex engineering problems. Besides, popular high-dimensional surrogate models encounter complex modeling process and prohibitive computational cost, and meantime they lack good adapta-bility to a wide range of engineering problems. To solve the above issues, a supervised nonlinear dimension-reduction surrogate modeling (SN-DRSM) method is proposed to alleviate the problem of high-dimensional variable in the process of surrogate-based design optimization. In this method, as the kernel principal compo-nent analysis (KPCA) nonlinear dimension-reduction model and Gaussian regression process model are inte-grated and trained as a whole, a new high-dimensional surrogate model is constructed adaptively, with continu-ously being studied in depth and improved with the optimization process, so as to establish an accurate mapping from high-dimensional inputs to outputs, which effectively solves the problems of high training cost and poor adaptability of traditional high-dimensional surrogate models. Then, an efficient high-dimensional global design optimization platform for complex aerodynamic configuration of aircraft is developed based on this novel sur-rogate model, and it is applied to two standard transonic optimization cases defined by AIAA aerodynamic op-timization group. Through a comprehensive comparison with the traditional surrogate optimization methods, it is proved that the new method can greatly improve the global optimization efficiency and ability of high-dimensional variables of aircraft

Key words： refined aerodynamic optimization; surrogate-based design optimization (SBDO); global optimization; high-dimensional variable; supervised nonlinear dimension-reduction surrogate model (SN-DRSM); high-dimension global design optimization

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