Optimization design on the wing of a general aviation airplane based on deep belief network and multi-objective particle swarm optimization algorithm

WU Yongchuan; SUN Gang; TAO Jun

doi:10.7638/kqdlxxb-2023.0005

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Optimization design on the wing of a general aviation airplane based on deep belief network and multi-objective particle swarm optimization algorithm

更新时间：2026-05-28

- Optimization design on the wing of a general aviation airplane based on deep belief network and multi-objective particle swarm optimization algorithm
- Vol. 41, Issue 12, Pages: 16-27(2023)
- 作者机构：
- 作者简介：
- 基金信息：
- DOI：10.7638/kqdlxxb-2023.0005
  CLC： V244
- Published：2023
- 稿件说明：
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吴涌钏, 孙刚, 陶俊. 基于深度置信网络与多目标粒子群算法的通用飞机机翼优化设计[J]. 空气动力学学报, 2023, 41(12): 16−27.

WU Yongchuan, SUN Gang, TAO Jun. Optimization design on the wing of a general aviation airplane based on deep belief network and multi-objective particle swarm optimization algorithm[J]. 2023, 41(12): 16-27.
吴涌钏, 孙刚, 陶俊. 基于深度置信网络与多目标粒子群算法的通用飞机机翼优化设计[J]. 空气动力学学报, 2023, 41(12): 16−27. DOI： 10.7638/kqdlxxb-2023.0005

WU Yongchuan, SUN Gang, TAO Jun. Optimization design on the wing of a general aviation airplane based on deep belief network and multi-objective particle swarm optimization algorithm[J]. 2023, 41(12): 16-27. DOI： 10.7638/kqdlxxb-2023.0005.

摘要

采用主成分分析技术对基于型函数/类函数变换（CST）参数化方法进行改进，以减少机翼的设计变量。建立了机翼的数据库，以数据库中机翼的几何参数和气动参数为训练样本，建立并训练了深度置信网络作为气动优化设计的代理模型。为了提高标准多目标粒子群算法的收敛速度和全局搜索能力，发展了基于

-stable分布函数的改进型多目标粒子群算法。将深度置信网络代理模型嵌入改进的多目标粒子群算法，开展了某通用飞机机翼的多目标气动优化设计。优化结果表明，采用主成分分析技术可大幅减少计算量。对优化所得机翼构型开展数值模拟验证，结果表明，在阻力系数不增加的条件下，优化机翼构型在多个工作状态下的升力系数显著提升。

Abstract

For the purpose of reducing the number of the design variables of the wing

the PCA (principal component analysis) technique is employed to improve the CST parameterization method. A database of the wings is established

including geometrical parameters and aerodynamic parameters. Based on the database

the DBN (deep belief network) is established and trained as the surrogate model in the aerodynamic optimization. In order to improve the convergence rate and global searching ability of the standard MOPSO (multi-objective particle swarm optimization) algorithm

an improved MOPSO algorithm is developed based on the

-stable distribution functions. By embedding the DBN surrogate model into the improved MOPSO algorithm

the multi-objective aerodynamic optimizations are performed for the wing of a general aviation airplane. The optimization results indicate that

the computation amount is decreased dramatically by introducing the PCA technique. Numerical validation is conducted for the designed wing configuration

and the results indicate that

comparing with the baseline configuration

the lift coefficients of the designed configuration are increased obviously under multiple states with the drag coefficients not increased.

关键词

Keywords

references

BENAOUALI A, KACHEL S. A surrogate-based integrated framework for the aerodynamic design optimization of a subsonic wing planform shape[J]. Proceedings of the Institution of Mechanical Engineers, Part G: Journal of Aerospace Engineering, 2018, 232(5): 872-883. DOI: 10.1177/0954410017699007

MA X Y, WU J Q, XIAO Y L, ETT AL. Study on Aerodynamic Shape Optimization for Wing of Business Jet[J]. Advances in Aeronautical Science and Engineer, 2022, 13(2): 27-44.

ZHOU Z Q, HU Z H, XING B D, et al. Topology optimization design method of aircraft wing structure based on genetic algorithm[J]. Internal Combustion Engine Parts, 2021(24): 16-18. (in Chinese)

HAO X, ZHANG Q Q, SU C, et al. Research on variable camber wing optimization design of passenger aircraft[J]. Advances in Aeronautical Science and Engineering, 2022, 13(4): 25-36. (in Chinese)

FENG C, GOWRISANKAR A C, SMITH A E. . Practical guidelines for developing BP neural network models of measurement uncertainty data[J]. Journal of Manufacturing Systems, 2006, 25(4): 239-250. DOI: 10.1016/S0278-6125(08)00006-X

LYU X L, HUANG D, JIANG D J. Iterative updating inversion method based on POD-RBF surrogate model[J]. Chinese Journal of Computational Mechanics, 2022, 39(4): 506-511. (in Chinese)

HAN S Q, SONG W P, HAN Z H, et al. Aerodynamic inverse design method based on gradient-enhanced Kriging model[J]. Acta Aeronautica et Astronautica Sinica, 2017, 38(7): 120817. (in Chinese)

KWON H, YI S, CHOI S. Numerical investigation for erratic behavior of Kriging surrogate model[J]. Journal of Mechanical Science and Technology, 2014, 28(9): 3697-3707. DOI: 10.1007/s12206-014-0831-x

DUAN Y H, WU W H, FAN Z L, et al. Proper orthogonal decomposition-based data mining of aerodynamic shape for design optimization[J]. Acta Physica Sinica, 2017, 66(22): 138-147. (in Chinese)

ZHENG C Y, HUANG J T, ZHOU Z, et al. Multidisciplinary optimization design of high dimensional target space for flying wing airfoil[J]. Acta Aerodynamica Sinica, 2017, 35(4): 587-597. (in Chinese)

LI G Q, YUAN C, WANG S, et al. Structural design of compound hydraulic swing cylinder based on co-evolution multi-objective genetic algorithm[J]. Journal of Ship Mechanics, 2022, 26(11): 1694-1704. (in Chinese)

WU K Z, LIU X J, LYU H Q. A supercritical airfoil design based on multi-output surrogate model[J]. Acta Aerodynamica Sinica, 2015, 33(6): 728-732, 746. (in Chinese)

GAO M H, WU C B, LI B C, et al. Design and implementation of a Placido disk-based corneal topographer optical system based on aberration theory and simulated annealing algorithm[J]. Optics Communications, 2020, 475: 126097. DOI: 10.1016/j.optcom.2020.126097

CHENG Y, WEI C, YOU B D, et al. Multi-objective optimization algorithm of space-based early warning based on ant colony[J]. Journal of Harbin Engineering University, 2021, 42(10): 1428-1438. (in Chinese)

WANG Z, HU Z Y, YANG X F. Multi-agent and ant colony optimization for ship integrated power system network reconfiguration[J]. Journal of Systems Engineering and Electronics, 2022, 33(2): 489-496. DOI: 10.23919/JSEE.2022.000048

TAO, JUN, . An artificial neural network approach for aerodynamic performance retention in airframe noise reduction design of a 3D swept wing model[J]. Chinese Journal of Aeronautics, 2016, 29(5): 1213-1225. DOI: 10.1016/j.cja.2016.08.008

KIM T, KIM S, LIM J, et al. Numerical investigation of compressibility effect on dynamic stall[J]. Aerospace Science and Technology, 2020, 105: 105918. DOI: 10.1016/j.ast.2020.105918

LI J, GAO Z H, HUANG J T, et al. Aerodynamic optimization system based on CST technique[J]. Acta Aerodynamica Sinica, 2012, 30(4): 443-449. (in Chinese)

ZHAO Z Z, SHKOLNISKY Y, SINGER A. Fast steerable principal component analysis[J]. IEEE Transactions on Computational Imaging, 2016, 2(1): 1-12. DOI: 10.1109/TCI.2016.2514700

PINAYA W H L, GADELHA A, DOYLE O M, et al. Using deep belief network modelling to characterize differences in brain morphometry in schizophrenia[J]. Scientific Reports, 2016, 6: 38897. DOI: 10.1038/srep38897

GHAHABI O, HERNANDO J. Restricted Boltzmann machines for vector representation of speech in speaker recognition[J]. Computer Speech Language, 2018, 47: 16-29. DOI: 10.1016/j.csl.2017.06.007

BAGHAEE H R, MIRSALIM M, GHAREHPETIAN G B. Multi-objective optimal power management and sizing of a reliable wind/PV microgrid with hydrogen energy storage using MOPSO[J]. Journal of Intelligent Fuzzy Systems, 2017, 32(3): 1753-1773. DOI: 10.3233/jifs-152372

LI G S, CHOU W S. Path planning for mobile robot using self-adaptive learning particle swarm optimization[J]. Science China Information Sciences, 2018, 61(5): 052204. DOI: 10.1007/s11432-016-9115-2

Raquel C R, Jr P C N. An effective use of crowding distance in multiobjective particle swarm optimization[C ] // Genetic and Evolutionary Computation Conference, Washington DC, USA, 2005. doi: 10.1145/1068009.1068047 10.1145/1068009.1068047

JESÚS, MOLINA-MUÑOZ. Market-crash forecasting based on the dynamics of the alpha-stable distribution[J]. Physica A: Statistical Mechanics and Its Applications, 2020, 557: 124876. DOI: 10.1016/j.physa.2020.124876

LI J W, QUAN H D, CUI P Z. Parameter estimation for skewed α-stable distribution based on PSO[J]. Chinese Journal of Radio Science, 2013, 28(1): 163-168.(in Chinese)

CHEN L, XIN B, CHEN J. Interactive multiobjective evolutionary algorithm based on decomposition and compression[J]. Science China (Information Sciences), 2021, 64(10): 166-181.

ZHAN D W, CHENG Y S, LIU J. Expected improvement matrix-based infill criteria for expensive multiobjective optimization[J]. IEEE Transactions on Evolutionary Computation, 2017, 21(6): 956-975. DOI: 10.1109/TEVC.2017.2697503

CHEN Y, WEN J Y, CHENG S J. Probabilistic load flow method based on nataf transformation and Latin hypercube sampling[J]. IEEE Transactions on Sustainable Energy, 2013, 4(2): 294-301. DOI: 10.1109/TSTE.2012.2222680

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