基于新型高维代理模型的气动外形设计方法

doi:10.7527/S10006893.2022.26924

Abstract

Abstract:

With the ever-increasing demands for the performance of modern aircraft，the refined aerodynamic shape design optimization of aircraft requires higher-fidelity CFD numerical analysis and more independent design variables，thus significantly reducing the efficiency of surrogate-based global optimization algorithm，particularly with an excessive number of design variables，Therefore，meeting the advanced demands for complex engineering problems becomes challenging. Furthermore，with complex modeling process and prohibitive computational costs，popular high-dimensional surrogate models，lack good adaptability to a wide range of engineering problems，This paper proposes a Supervised Nonlinear Dimension-Reduction Surrogate Modeling（SN-DRSM）method to alleviate the problem of high-dimensional variables in the process of surrogate-based design optimization. This method， integrates and trains the Kernel Principal Component Analysis（KPCA）nonlinear dimension-reduction model and the Gaussian regression process model as a whole，A new high-dimensional surrogate model is adaptively constructed，continuously studied in depth and improved during the optimization process，to establish an accurate mapping from high-dimensional inputs to outputs，thereby effectively solving the problems of high training cost and poor adaptability of traditional high-dimensional surrogate models. Then，an efficient high-dimensional global design optimization platform for complex aerodynamic configuration of aircraft is developed based on this novel surrogate model，and applied to two standard transonic optimization cases defined by AIAA aerodynamic optimization group. A comprehensive comparison with the traditional surrogate optimization methods， proves that the new method can significantly improve the global optimization efficiency and ability of high-dimensional aircraft variables.

Key words: refined aerodynamic optimization, Surrogate-Based Design Optimization(SBDO), global optimization, high-dimensional variable, Supervised Nonlinear Dimension-Reduction Surrogate Model(SN-DRSM), high-dimension design optimization

CLC Number:

V211

Huan ZHAO, Zhenghong GAO, Lu XIA. Aerodynamic shape design optimization method based on novel high⁃dimensional surrogate model[J]. ACTA AERONAUTICAET ASTRONAUTICA SINICA, 2023, 44(5): 126924-126924.

Figures/Tables 31

Fig. 1

Table 1

Test conditions of RAE2822 airfoil

设计状态	$M a = 0.734, C L = 0.824, R e = 6.5 × 106$
空间维度n	｛16，32，48，64｝
训练样本m	｛4n，8n，16n，24n，32n，40n｝
验证样本	1 000

Table 1

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Table 2

Comparison of drag coefficients of baseline and optimized airfoils （whole model）

翼型	$C D$	所需样本数
NACA0012	0.047 1
优化翼型（Kriging）	0.017 5	246
优化翼型（ADE-Kriging）	0.005 4	246
优化翼型（ADE-KPCA-Kriging）	0.004 2	246

Table 2

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Table 3

Comparison of aerodynamic force coefficients of baseline and optimized wings

机翼	$C L$	$C D$	$C m$	α/（°）	所需样本数
CRM	0.5	0.023 41	-0.016 29	3.288
优化机翼（Kriging）	0.5	0.021 94	-0.016 32	3.030	436
优化机翼（KPCA-Kriging）	0.5	0.020 52	-0.016 89	3.006	436

Table 3

Fig. 22

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Aerodynamic shape design optimization method based on novel high⁃dimensional surrogate model

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