Aerodynamic and stealthy performance optimization of airfoil based on adaptive surrogate model

被引：0
|
作者：
Long T. [1 ]
Li X. [1 ]
Huang B. [2 ]
Jiang M. [1 ]
机构：
[1] School of Aerospace Engineering, Beijing Institute of Technology, Beijing
[2] Jiangnan Design Institute of Machinery & Electricity, Guiyang
来源：
Long, Teng (tenglong@bit.edu.cn) | 1600年 / Chinese Mechanical Engineering Society卷 / 52期
关键词：
Adaptive surrogate model; Airfoil aerodynamic-stealthy optimization; Augmented lagrange multiplier method; Physical programming method; Radial basis function;
D O I：
10.3901/JME.2016.22.101
中图分类号：
学科分类号：
摘要：
To solve the airfoil aerodynamical and stealthy optimization problems about large computational cost and weights are ususlly inappropriate, a multi-objective optimization strategy using adaptive radial basis function and physic programming(ARBF-PP) is proposed. Multi-objective optimization problem is transformed by physical programming method into single objective optimization problem that reflects design preference, then the radial basis function model is created to replace aggregate preference function and constraints. Augmented Lagrange multiplier method is used to solve the constraint problem, and use genetic algorithm(GA) to obtain current optimal solution. In the process of optimization, new sampling points are added and surrogate model is updated according to all the samples and their responses to improve the approximation accuracy around the optimal solution until the convergence of optimization. The multi-objective optimization strategy is validated by using numerical test and the problem of optimization of the aerodynamical and stealthy performance of airfoil to prove the efficiency of ARBF-PP. As the optimization results shown: Compared to the initial data, lift-to-drag ratio increases 34.28% and the average of radar cross section(RCS) in the key azimuth decreases 24.19%. Furthermore, compared to the traditional optimization method using static radial basis function surrogate model, when the amounts of samples are same, the lift-to-drag ratio increases 11% and the RCS decreases 25.6%; And compared to GA without surrogate model, the number of function evaluation(Nfe) decreases 93.5%. © 2016 Journal of Mechanical Engineering.
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页码：101 / 111
页数：10
相关论文
共 26 条
  • [1] Yang H., Song W., Han Z., Et al., Multi-objective and multi-constrained optimiza-tion design for a helicopter rotor airfoil, Acta Aeronoutica et Astronautica Sinica, 33, 7, pp. 1218-1226, (2012)
  • [2] Zhang B., Luo L., Chen Z., Et al., On stealth airfoil optimization design for flying wing configuration, Acta Aeronoutica et Astronautica Sinica, 35, 4, pp. 957-967, (2014)
  • [3] Liao Y., Liu L., Long T., Multi-objective aerodynamic and stealthy performance optimization based on multi-attribute decision making, Journal of Mechanical Engineering, 48, 13, pp. 132-140, (2012)
  • [4] Duan Y., Cai J., Liu Q., Surrogate Model based optimization for airfoil design, Acta Aeronoutica et Astronautica Sinica, 32, 4, pp. 617-627, (2011)
  • [5] Kanazaki M., Tanaka K., Jeong S., Et al., Multi-objective aerodynamic optimization of elements' setting for high-lift airfoil using Kriging model, 44th AIAA Aerospace Sciences Meeting and Exhibit. Reno: American Institute of Aeronautics and Astronautics, pp. 1-11, (2006)
  • [6] Peng L., Liu L., Long T., Optimization strategy using dynamic radial basis function metamodel, Journal of Mechanical Engineering, 47, 7, pp. 154-170, (2011)
  • [7] Forrester A.I.J., Keane A.J., Recent advances in surrogate-based optimization, Progress in Aerospace Sciences, 45, 1-3, pp. 50-79, (2009)
  • [8] Wang G.G., Adaptive response surface method using inherited Latin hypercube design points, Journal of Mechanical Design, 125, 2, pp. 210-220, (2003)
  • [9] Peng L., Liu L., Long T., Optimization strategy using dynamic radial basis function metamodel, Journal of Mechanical Engineering, 47, 7, pp. 164-170, (2011)
  • [10] Long T., Wu D., Guo X., Et al., Efficient adaptive response surface method using intelligent space exploration strategy, Structural and Multidisciplinary Optimization, 51, 6, pp. 1335-1362, (2015)
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