多变量气动设计问题分层协同优化

李焦赞; 高正红

doi:10.7527/S1000-6893.2013.0008

航空学报 >

2013 , Vol. 34 >Issue 1: 58 - 65

DOI: https://doi.org/10.7527/S1000-6893.2013.0008

流体力学、飞行力学与发动机

多变量气动设计问题分层协同优化

李焦赞 ,
高正红

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西北工业大学翼型叶栅空气动力学国家重点实验室, 陕西西安 710072

李焦赞,男,博士研究生。主要研究方向:飞行器气动优化设计。Tel:029-88495971,E-mail:jiaozanli@163.com;高正红,女,博士,教授,博士生导师。主要研究方向:飞行器设计、流体力学、飞行力学与飞行控制等。Tel:029-88495971,E-mail:zgao@nwpu.edu.cn

收稿日期: 2012-01-04

修回日期: 2012-02-13

网络出版日期: 2013-01-19

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Multivariable Aerodynamic Design Based on Multilevel Collaborative Optimization

LI Jiaozan ,
GAO Zhenghong

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National Key Laboratory of Science and Technology on Aerodynamic Design and Research, Northwestern Polytechnical University, Xi’an 710072, China

Received date: 2012-01-04

Revised date: 2012-02-13

Online published: 2013-01-19

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摘要

通过验证实例分析,气动设计中精细化优化模型对设计结果收敛精度的提高有很大帮助,但同时也带来优化算法搜索困难的问题,并且由于不同类型设计变量之间的相互耦合干扰使优化难以收敛到全局最优解。于是提出基于响应均值灵敏度的概念对大规模的设计变量进行重要性分组的策略,依据设计变量分组情况应用系统分解思想对多变量设计问题进行分层协同优化来降低系统的复杂度,这既保证了精细化设计的要求,又缓解了优化算法对大规模问题搜索困难的问题。与传统气动优化方法相比,基于系统分解的分层协同优化算例有较大寻优效率和性能提升,证明了该方法的可行性。

关键词： 多变量系统; 气动设计; 灵敏度分析; 系统分解; 分层协同优化

本文引用格式

李焦赞 , 高正红 . 多变量气动设计问题分层协同优化[J]. 航空学报, 2013 , 34(1) : 58 -65 . DOI: 10.7527/S1000-6893.2013.0008

Abstract

In future aircraft design, the desired performance indexes are not only more rigorous, but also more numerous. Therefore, aerodynamic design should achieve high definition shape design and satisfy the multiple design requirements. It is consequently necessary to establish a multivariable optimization model in aerodynamic design. In this paper, a test example is provided to show the advantage and disadvantage of the multivariable model in aerodynamic optimization. While the optimized results are significantly heightened, searching difficulty also increases for the optimization algorithm. Meanwhile, because of the coupling disturbance among different design parameters, it is difficult to achieve global optimized results. So in this paper a sampling mean-response sensitivity analysis is carried out to measure the importance of design parameters, which are then grouped based on their importance level. Subsequently the multilevel collaborative optimization design method based on system decomposition is used to reduce the system complicacy. It ensures the precision of the multivariable optimization model and resolves the searching difficulty of the optimization algorithm. An example for a wing-body optimization is carried out using the above method and the result shows its feasibility and advantage as compared with the traditional aerodynamic optimization method.

Key words： multivariable system; aerodynamic design; sensitivity analysis; system decomposition; multilevel collaborative optimization

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