飞行器气动外形数值优化与设计专栏

基于代理模型的高效全局气动优化设计方法研究进展

  • 韩忠华 ,
  • 许晨舟 ,
  • 乔建领 ,
  • 柳斐 ,
  • 池江波 ,
  • 孟冠宇 ,
  • 张科施 ,
  • 宋文萍
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  • 西北工业大学 航空学院 气动与多学科优化设计研究所 翼型、叶栅空气动力学重点实验室, 西安 710072

收稿日期: 2019-08-06

  修回日期: 2019-08-25

  网络出版日期: 2019-10-17

基金资助

国家自然科学基金(11772261,11972305);翼型、叶栅空气动力学重点实验室基金(61XXX01010020117)

Recent progress of efficient global aerodynamic shape optimization using surrogate-based approach

  • HAN Zhonghua ,
  • XU Chenzhou ,
  • QIAO Jianling ,
  • LIU Fei ,
  • CHI Jiangbo ,
  • MENG Guanyu ,
  • ZHANG Keshi ,
  • SONG Wenping
Expand
  • National Key Laboratory of Science and Technology on Aerodynamic Design and Research, Institute of Aerodynamic and Multidisciplinary Design Optimization, School of Aeronautics, Northwestern Polytechnical University, Xi'an 710072, China

Received date: 2019-08-06

  Revised date: 2019-08-25

  Online published: 2019-10-17

Supported by

National Natural Science Foundation of China (11772261, 11972305); Foundation of National Key Laboratory of Science and Technology on Aerodynamic Design and Research (61XXX01010020117)

摘要

基于高可信度计算流体力学的数值优化设计方法,在提高飞行器气动与综合性能方面正发挥着越来越重要的作用。基于代理模型的优化算法(SBO),由于能够实现高效全局优化,逐渐成为了气动优化设计领域的研究热点之一。近20年来,代理优化算法研究已取得了长足进步,多种先进的新型代理模型被提出,优化理论和算法也不断完善和发展。以飞行器精细化气动优化设计为背景,综述了基于代理模型的高效全局气动优化设计方法研究进展。首先,介绍了基于变可信度代理模型的气动优化设计方法、结合代理模型和伴随方法的气动优化设计方法以及基于非生物进化的并行气动优化设计方法的研究现状和最新进展。然后,针对飞行器气动优化设计学科领域的前沿问题,介绍了基于代理模型的多目标气动优化设计方法、混合反设计/优化设计方法、稳健气动优化设计方法的研究进展,以及基于代理模型的多学科优化设计方法的研究进展。文献综述表明,代理优化算法在设计效率、全局性以及鲁棒性等方面性能优良,已经发展到可以解决100维(100个设计变量)以内的气动优化设计问题,具有良好的工程应用前景。最后,探讨了基于代理模型的高效全局气动优化设计在理论、方法及飞行器设计应用方面所面临的问题和挑战,给出了未来研究方向的建议。

本文引用格式

韩忠华 , 许晨舟 , 乔建领 , 柳斐 , 池江波 , 孟冠宇 , 张科施 , 宋文萍 . 基于代理模型的高效全局气动优化设计方法研究进展[J]. 航空学报, 2020 , 41(5) : 623344 -623344 . DOI: 10.7527/S1000-6893.2019.23344

Abstract

Aerodynamic shape optimization based on high-fidelity computational fluid dynamics plays an increasingly important role in improving aerodynamic and overall performance of an aircraft. Surrogate-Based Optimization (SBO), a genetic efficient global optimization, has become a hot topic in this area. During the past two decades, a great progress has been made. Various advanced new surrogate modelling techniques have been proposed, and optimization theory and algorithm are constantly improved. In this article, recent progress of efficient global aerodynamic shape optimization using SBO is reviewed. First, the state of the art of optimizations with variable-fidelity surrogate models, gradient-enhanced models, and a parallel optimization method based on none-bio-inspired evolutionary mechanism are reviewed. Second, in terms of frontier issues, recent progress of multi-objective design optimization, hybrid inverse/optimization design method, robust design optimization, as well as multidisciplinary design optimization are discussed. Literature review shows that SBO has significant superiority in efficiency, robustness, and global search. In addition, it enables efficient aerodynamic shape optimizations with number of design variables up to 100, showing huge potential in engineering applications. Finally, some key issues and challenges relevant to the theory, method, and applications of SBO are presented, and future research directions are suggested.

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