飞行器气动外形数值优化与设计专栏

超临界机翼多目标气动优化设计的策略与方法

  • 李润泽 ,
  • 张宇飞 ,
  • 陈海昕
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  • 清华大学 航天航空学院, 北京 100084

收稿日期: 2019-08-27

  修回日期: 2019-09-23

  网络出版日期: 2019-10-10

基金资助

国家自然科学基金(11872230,91852108);清华大学自主创新科研基金(2015Z22003)

Strategies and methods for multi-objective aerodynamic optimization design for supercritical wings

  • LI Runze ,
  • ZHANG Yufei ,
  • CHEN Haixin
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  • School of Aerospace Engineering, Tsinghua University, Beijing 100084, China

Received date: 2019-08-27

  Revised date: 2019-09-23

  Online published: 2019-10-10

Supported by

National Natural Science Foundation of China (11872230, 91852108); Innovation Program of Tsinghua University (2015Z22003)

摘要

超临界机翼的气动设计十分复杂,往往需要借助于有效的优化手段。然而,优化方法本身往往无法直接表达真实完整的设计意图,因此需要将工程需求和约束转换为优化方法所能处理的数学形式。本文首先探讨了梯度优化方法和进化类优化方法在气动优化中的表现。之后简要总结了前人在飞机气动设计中提出的准则和要求,并基于压力分布形态定义了超临界机翼气动设计的关键特征及定量要求,探究其在超临界机翼气动优化设计中的应用效果。研究结果表明,在多目标优化中引入面向压力分布形态特征的目标和约束,能有效引导优化体现工程设计思想,进而提高优化效率。

本文引用格式

李润泽 , 张宇飞 , 陈海昕 . 超临界机翼多目标气动优化设计的策略与方法[J]. 航空学报, 2020 , 41(5) : 623409 -623409 . DOI: 10.7527/S1000-6893.2019.23409

Abstract

Optimization methods are often applied in aerodynamic design of modern civil aircraft supercritical wings. However, optimization algorithms cannot directly describe the industrial requirements, therefore, corresponding mathematical descriptions must be developed to tackle the problem. In the present paper, gradient algorithms and stochastic algorithms are compared in a supercritical wing optimization. Then, several requirements of aircraft aerodynamic design are summarized and mathematically described, and these criteria are applied to a multi-objective stochastic optimization method as objectives and constraints. The method is applied to a civil aircraft wing aerodynamic optimization, and the results show that better efficiency and result can be obtained by using pressure distribution guided objectives and constraints.

参考文献

[1] 李润泽, 张宇飞, 陈海昕. "人在回路"思想在飞机气动优化设计中演变与发展[J]. 空气动力学学报,2017, 35(4):529-543. LI R Z, ZHANG Y F, CHEN H X. Evolution and development of "man-in-loop" in aerodynamic optimization design[J]. Acta Aerodynamica Sinica, 2017, 35(4):529-543(in Chinese).
[2] LI R Z, DENG K W, ZHANG Y F, et al. Pressure distribution guided supercritical wing optimization[J]. Chinese Journal of Aeronautics, 2018, 31(9):1842-1854.
[3] DENG K W, CHEN H X. A hybrid aerodynamic optimization algorithm based on differential evolution and RBF response surface[C]//17th AIAA/ISSMO Multidisciplinary Analysis and Optimization Conference. Reston:AIAA, 2016:3671.
[4] SKINNER S N, ZARE-BEHTASH H. State-of-the-art in aerodynamic shape optimization methods[J]. Applied Soft Computing, 2018, 62(1):933-962.
[5] JAMESON A. Aerodynamic design via control theory[J]. Journal of Scientific Computing, 1988, 3(3):233-260.
[6] MADER C A, MARTINS J R, ALONSO J J, et al. ADjoint:An approach for the rapid development of discrete adjoint solvers[J]. AIAA Journal, 2008, 46(4):863-873.
[7] ZINGG D W, NEMEC M, PULLIAM T H. A comparative evaluation of genetic and gradient-based algorithms applied to aerodynamic optimization[J]. European Journal of Computational Mechanics, 2008, 17(1-2):103-126.
[8] BUCKLEY H P, ZHOU B Y, ZINGG D W. Airfoil optimization using practical aerodynamic design requirements[J]. Journal of Aircraft, 2010, 47(5):1707-1719.
[9] CHERNUKHIN O, ZINGG D W. Multimodality and global optimization in aerodynamic design[J]. AIAA Journal, 2013, 51(6):1342-1354.
[10] MUYL F, DUMAS L, HERBERT V. Hybrid method for aerodynamic shape optimization in automotive industry[J]. Computers & Fluids, 2004, 33(5-6):849-858.
[11] 陈海昕, 邓凯文, 李润泽. 机器学习技术在气动优化中的应用[J]. 航空学报, 2019, 40(1):522480. CHEN H X, DENG K W, LI R Z. Utilization of machine learning technology in aerodynamic optimization[J]. Acta Aeronautica et Astronautica Sinica, 2019, 40(1):522480(in Chinese).
[12] LI R Z, ZHANG M, ZHANG M H, et al. Multi-point aerodynamic optimization design of a dual-aisle airplane wing[C]//31st Congress of the International Council of the Aeronautical Sciences, 2018.
[13] GILL P E, MURRARY W, SAUNDERS M A. SNOPT:An SQP algorithm for large-scale constrained optimizat-ion[J]. SIAM Review, 2005, 47(1):99-131.
[14] NADARAJAH S, JAMESON A. A comparison of the continuous and discrete adjoint approach to automatic aerodynamic optimization[C]//38th Aerospace Sciences Meeting and Exhibit. Reaton:AIAA, 2000.
[15] OBERT E. Aerodynamic design of transport aircraft[M]. Amsterdam:IOS Press, 2009.
[16] KRENZ G. Transonic configuration design, 712. AGARD special course on subsonic/transonic aerodynamic interference for aircraft[R]. Washington,D.C:NASA Langley Research Center, 1983.
[17] TORENBEEK E. Airplane weight and balance, synthesis of subsonic airplane design.[M]. Dordrecht:Springer, 1982:263-302.
[18] WHITCOMB R T. Transonic airfoil development:19840004008[R]. Washington,D.C.:NASA Langley Research Center, 1983.
[19] MCLEAN D. Understanding aerodynamics:Arguing from the real physics[M]. Chichester:John Wiley & Sons, 2012.
[20] ZHANG, Y F, FANG X M, CHEN H X, et al. Supercritical natural laminar flow airfoil optimization for regional aircraft wing design[J] Aerospace Science and Technology, 2015, 43(1):152-164.
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