一种适用于气动优化的高效自适应全局优化方法

doi:10.7527/S1000-6893.2019.23352

飞行器气动外形数值优化与设计专栏

本期目录 | 过刊浏览 | 高级检索

前一篇 | 后一篇

一种适用于气动优化的高效自适应全局优化方法

李春娜, 张阳康

西北工业大学航天学院空天飞行技术研究所, 西安 710072

收稿日期:2019-08-08 修回日期:2019-08-25 出版日期:2020-05-15 发布日期:2019-09-27
通讯作者: 李春娜 E-mail:chunnali@nwpu.edu.cn
基金资助:
国家自然科学基金（11502209）

An efficient adaptive global optimization method suitable for aerodynamic optimization

LI Chunna, ZHANG Yangkang

Aerospace Flight Vehicle Design Key Laboratory, School of Astronautics, Northwestern Polytechnical University, Xi'an 710072, China

Received:2019-08-08 Revised:2019-08-25 Online:2020-05-15 Published:2019-09-27
Supported by:
National Natural Science Foundation of China (11502209)

摘要/Abstract

摘要： 随着设计空间的增大和优化问题非线性程度的提高，基于代理模型的优化（SBO）过程收敛越来越慢，并且在局部勘测上呈现不足。本文发展了一种高效自适应全局优化方法，在整个样本细化迭代过程中采用变设计空间取样：即在每一步样本细化迭代过程中，利用当前设计空间中的样本建立代理模型，并且根据样本的内部特征，利用模糊聚类算法将该设计空间分割成几个子空间，然后在每个子空间内通过最大化目标函数的期望提高函数和最小化模型预测目标来增加新的样本，之后对子空间进行融合更新设计空间。6个解析测试算例的结果表明，所发展的方法相比于一般的代理模型优化方法，具有更好的鲁棒性以及全局探索和局部勘测能力，更适用于具有强非线性和多极值的优化问题。RAE2822气动优化实例表明，所发展的方法在处理工程实际问题时，仍然能够保持很好的效率、鲁棒性和自适应性。

关键词: 气动优化, 变设计空间, 模糊聚类算法, 代理模型, 自适应

Abstract: With the increase of design space and nonlinearity, the Surrogate-Based Optimization (SBO) process converges more slowly, and shows deficiency in local exploitation. This paper proposes an efficient adaptive global optimization method, of which infill samples are selected within a variable design space. In each refinement cycle, the current design space is divided into several subspaces by a fuzzy clustering algorithm, with respect to the inherent characteristics of samples in the current design space. Thus new infill samples are generated in each of the subspaces by maximizing expected improvement function and minimizing surrogate prediction, and the subspaces are then merged to form a new design space. The proposed method is validated by six analytical tests. In comparison with general SBO method, the proposed method shows better robustness and performance in global exploration and local exploitation, which is suitable for optimization problems with strong nonlinearity and many optima. The application by minimizing drag of RAE2822 airfoil indicates the proposed method performs well in solving engineering problems, and can maintain good efficiency, robustness and adaptability.

Key words: aerodynamic optimization, variable design space, fussy clustering algorithm, surrogate model, adaptive

中图分类号:

V221.3

李春娜, 张阳康. 一种适用于气动优化的高效自适应全局优化方法[J]. 航空学报, 2020, 41(5): 623352-623352.

LI Chunna, ZHANG Yangkang. An efficient adaptive global optimization method suitable for aerodynamic optimization[J]. ACTA AERONAUTICAET ASTRONAUTICA SINICA, 2020, 41(5): 623352-623352.

参考文献 29

[1]	MORRIS A J. Structural optimization by geometric programming[J]. International Journal of Solids & Structures, 1972, 8(7):847-864.
[2]	SACKS J, WELCH W J, MITCHELL T J, et al. Design and analysis of computer experiments[J]. Statistical Science, 1989, 4(4):409-423.
[3]	韩忠华. Kriging模型及代理优化算法研究进展[J]. 航空学报, 2016, 37(11):3197-3225. HAN Z H. Kriging surrogate model and its application to design optimization:Review of recent progress[J]. Acta Aeronautica et Astronautica Sinica, 2016, 37(11):3197-3225(in Chinese).
[4]	乔建领, 韩忠华, 宋文萍,等. 基于代理模型的高效全局低音爆优化设计方法[J]. 航空学报, 2018, 39(5):67-80. QIAO J L, HAN Z H, SONG W P, et al. An efficient surrogate-based global optimization for low boom design[J]. Acta Aeronautica et Astronautica Sinica, 2018, 39(5):67-80(in Chinese).
[5]	韩忠华,张瑜,许晨舟,等. 基于代理模型的大型民机机翼气动优化设计[J]. 航空学报, 2019, 40(1):522398. HAN Z H, ZHANG Y, XU C Z, et al. Aerodynamic optimization design of large civil aircraft wings using surrogate-based model[J]. Acta Aeronautica et Astronautica Sinica, 2019, 40(1):522398(in Chinese).
[6]	周旺仪,白俊强,乔磊,等. 变弯翼型与增升装置多目标气动优化设计研究[J]. 西北工业大学学报, 2018, 36(1):83-90. ZHOU W Y, BAI J Q, QIAO L, et al. A study of multi-objective aerodynamic optimization design for variable camber airfoils and high lift devices[J]. Journal of Northwestern Polytechnical University, 2018, 36(1):83-90(in Chinese).
[7]	LIU B, GROUT V, NIKOLAEVA A. Efficient global optimization of actuator based on a surrogate model assisted hybrid algorithm[J]. IEEE Transactions on Industrial Electronics, 2018, 65(7):5712-5721.
[8]	IULIANA E. Global optimization of benchmark aerodynamic cases using physics-based surrogate models[J]. Aerospace Science & Technology, 2017, 67:273-286.
[9]	FORRESTER A I J, SÓBESTER A, KEANE A J. Engineering design via surrogate modeling:A practical guide[M]. Reston, VA:AIAA, 2008:77-106.
[10]	LIU H, ONG Y S, CAI J. A survey of adaptive sampling for global metamodeling in support of simulation-based complex engineering design[J]. Structural and Multidisciplinary Optimization, 2018, 57(1):393-416.
[11]	LI C N, BREZILLON J, GÖRTZ S. A hybrid approach for surrogate-based aerodynamic optimization with constraints[C]//2011 EUROGEN:Eccomas Thematic Conference, 2011:84-87.
[12]	MACKMAN T J, ALLEN C B, GHOREYSHI M, et al. Comparison of adaptive sampling methods for generation of surrogate aerodynamic models[J]. AIAA Journal, 2013, 51(4):797-808.
[13]	CHAUDHURI A, HAFTKA R T. Efficient global optimization with adaptive target setting[J]. AIAA Journal, 2014; 52(7):1573-1577.
[14]	LIU H, ONG Y S, CAI J. A Survey of adaptive sampling for global metamodeling in support of simulation-based complex engineering design[J]. Structural and Multidisciplinary Optimization, 2017(6):1-24.
[15]	KAMINSKY A L, WANG Y, PANT K, et al. Adaptive sampling techniques for surrogate modeling to create high-dimension aerodynamic loading response surfaces[C]//2018 Applied Aerodynamics Conference. Reston:AIAA, 2018
[16]	CHENG G H, WANG G G. Trust region based MPS method for global optimization of high dimensional design problems:AIAA-2012-1590[R]. Reston:AIAA, 2012.
[17]	GUO X S, LONG T,WU D, et al. RBF metamodel assisted global optimization method using particle swarm evolution and fuzzy clustering for sequential sampling:AIAA-2014-2305[R]. Reston:AIAA, 2014.
[18]	QIU H, XU Y J, GAO L, et al. Multi-stage design space reduction and metamodeling optimization method based on self-organizing maps and fuzzy clustering[J]. Expert Systems with Applications, 2016, 46:180-195.
[19]	SHI R H, LIU L, LONG T, et al. Sequential radial basis function using support vector machine for expensive design optimization[J]. AIAA Journal, 2017, 55(1):214-227.
[20]	DONG H C, SONG B W, WANG P, et al. Surrogate-based optimization with clustering-based space-exploration for expensive multimodal problems[J]. Structural and Multidisciplinary Optimization, 2018, 57(4):1553-1577.
[21]	王超,高正红,张伟,等. 自适应设计空间扩展的高效代理模型气动优化设计方法[J]. 航空学报,2018, 39(7):121745. WANG C, GAO Z H, ZHANG W, et al. Efficient surrogate-based aerodynamic design optimization method with adaptive design space expansion[J]. Acta Aeronautica et Astronautica Sinica, 2018, 39(7):121745(in Chinese).
[22]	LI C N, PAN Q F. Adaptive optimization methodology based on Kriging modeling and a trust region method[J]. Chinese Journal of Aeronautics, 2019, 32(2):281-295.
[23]	LI C N. A surrogate-based framework with hybrid refinement strategies for aerodynamic shape optimization[D]. Cologne:Library and Information Base of German Aerospace Center, 2013:37-52.
[24]	BEZDEK J C, CHRISTIAN J. Fuzzy mathematics in pattern classification[D]. New York:Cornell University, 1973:142-147.
[25]	WU D, LONG T, WANG Y, et al. A sequential maximin latin hypercube sampling method and its application to aircraft design:AIAA-2015-3095[R]. Reston:AIAA, 2015.
[26]	JONES D R, SCHONLAU M, WELCH W J. Efficient global optimization of expensive black-box functions[J]. Journal of Global Optimization, 1998, 13(4):455-492.
[27]	LI Y, LIU L, LONG T, et al. Metamodel-based global optimization using fuzzy clustering for design space reduction[J]. Chinese Journal of Mechanical Engineering, 2013, 26(5):928-939.
[28]	PALACIOS F, ECONOMON T D, ALONSO J J. Large-scale aircraft design using SU2[C]//AIAA Aerospace Sciences Meeting. Reston:AIAA, 2015.
[29]	QIN A K, HUANG V L, SUGANTHAN P N. Differential evolution algorithm with strategy adaptation for global numerical optimization[J]. IEEE Transactions on Evolutionary Computation, 2009, 13(2):398-417.

E-mail：hkxb@buaa.edu.cn

关于我们

期刊社服务

专业学科

封面文章

友情链接

主管单位：中国科学技术协会主办单位：中国航空学会北京航空航天大学

一种适用于气动优化的高效自适应全局优化方法

An efficient adaptive global optimization method suitable for aerodynamic optimization

PDF (PC)

可视化

摘要/Abstract

引用本文

使用本文

参考文献 29

相关文章 15

编辑推荐

Metrics

本文评价

[1]	于功也, 蔡伟东, 胡明辉, 刘文才, 马波. 故障机理与领域自适应混合驱动的机械故障智能迁移诊断[J]. 航空学报, 2023, 44(2): 426800-426800.
[2]	杨倩, 郭晓峰, 李芹, 董威. 基于POD和代理模型的热气防冰性能预测方法[J]. 航空学报, 2023, 44(1): 626992-626992.
[3]	牛俊杰, 桑为民, 李栋, 郝莲, 王泽林. 一种基于水滴收集量代理模型的结冰试飞空域确定方法[J]. 航空学报, 2023, 44(1): 627697-627697.
[4]	郭琪磊, 桑为民, 牛俊杰, 袁烨. 复杂气象条件下考虑结冰风险的无人机飞行策略[J]. 航空学报, 2023, 44(1): 627518-627518.
[5]	董青, 郑建飞, 胡昌华, 余铜辉, 牟含笑. 考虑随机冲击影响的自适应Wiener过程剩余寿命预测方法[J]. 航空学报, 2022, 43(9): 225914-225914.
[6]	杨晓伟, 葛曜文, 邓文翔, 姚建勇, 周宁. 航空发动机导叶控制机构作动筒主动容错控制[J]. 航空学报, 2022, 43(9): 625464-625464.
[7]	韩淞宇, 邵海东, 姜洪开, 张笑阳. 基于提升卷积神经网络的航空发动机高速轴承智能故障诊断[J]. 航空学报, 2022, 43(9): 625479-625479.
[8]	孙洪驰, 穆荣军, 龙腾, 李寿鹏, 崔乃刚. 临近空间飞行器北斗/INS高动态深组合导航方法[J]. 航空学报, 2022, 43(9): 325672-325672.
[9]	李辉, 龙腾, 孙景亮, 徐广通. 基于自适应视线法的无人机三维航迹跟踪方法[J]. 航空学报, 2022, 43(9): 326105-326105.
[10]	陈浩, 华如豪, 袁先旭, 唐志共, 毕林. 基于自适应笛卡尔网格的飞翼布局流动模拟[J]. 航空学报, 2022, 43(8): 125674-125674.
[11]	罗佳杰, 宋文滨. 层流机翼及其增升装置嵌套协同优化方法[J]. 航空学报, 2022, 43(8): 125377-125377.
[12]	王育鹏, 吕帅帅, 杨宇, 李嘉欣, 王叶子. 基于域自适应的复合材料结构损伤识别方法[J]. 航空学报, 2022, 43(6): 526752-526752.
[13]	张钧铎, 左青海, 林孟达, 黄伟希, 潘卫军, 崔桂香. ARJ21飞机尾涡在侧风条件下的近地演化数值模拟[J]. 航空学报, 2022, 43(5): 125043-125043.
[14]	胡嘉欣, 芮姝, 高瑞朝, 苟建军, 龚春林. 飞行器结构布局与尺寸混合优化方法[J]. 航空学报, 2022, 43(5): 225363-225363.
[15]	陈浩, 毕林, 华如豪, 周清清, 唐志共, 袁先旭. 基于全线程树数据结构的笛卡尔网格高效生成技术[J]. 航空学报, 2022, 43(5): 125170-125170.