Nozzle design optimization for supersonic wind tunnel by using surrogate-assisted evolutionary algorithms

被引:7
|
作者
Matsunaga, Masanobu [1 ]
Fujio, Chihiro [1 ]
Ogawa, Hideaki [1 ]
Higa, Yoshitaka [2 ]
Handa, Taro [2 ]
机构
[1] Kyushu Univ, Dept Aeronaut & Astronaut, 744 Motooka,Nishi ku, Fukuoka 8190395, Japan
[2] Toyota Technol Inst, Dept Adv Sci & Technol, 2-12-1 Hisakata,Tempaku ku, Nagoya, Aichi 4688511, Japan
基金
日本学术振兴会;
关键词
Supersonic wind tunnel nozzles; Multi-objective design optimization; Evolutionary algorithms; Surrogate modeling; GENETIC ALGORITHM;
D O I
10.1016/j.ast.2022.107879
中图分类号
V [航空、航天];
学科分类号
08 ; 0825 ;
摘要
For high-precision measurement in supersonic wind tunnel experiments, it is of crucial importance to produce a uniform flow in the measurement section downstream of the nozzle. This paper proposes and verifies a new design methodology for supersonic wind tunnel nozzles that can generate highly uniform airstream at a design Mach number at the nozzle exit. Shape design optimization has been conducted by employing surrogate-assisted evolutionary algorithms coupled with computational fluid dynamics. This approach has yielded higher flow uniformity than that of the nozzle designed by using the method of characteristics in the inviscid regime. By applying boundary layer correction to the nozzle contour obtained from the inviscid optimization, nearly uniform core flow of Mach 2.5 has been achieved at the nozzle exit in the presence of viscosity. In the viscous optimization, nozzle shape optimization has been performed by incorporating viscous simulations without using boundary layer correction to evaluate its efficacy in comparison with the former approach. It has been found that the deviation from the design Mach number and the flow deflection from the horizontal direction cannot be minimized simultaneously. This has been attributed to the constraint associated with the nozzle length. It has also been found that the result of the former approach combining the inviscid optimization and boundary layer correction can be regarded as one of the results of the viscous optimization.(c) 2022 Elsevier Masson SAS. All rights reserved.
引用
收藏
页数:14
相关论文
共 50 条
  • [21] Surrogate-assisted evolutionary algorithm for expensive constrained multi-objective discrete optimization problems
    Gu, Qinghua
    Wang, Qian
    Xiong, Neal N.
    Jiang, Song
    Chen, Lu
    COMPLEX & INTELLIGENT SYSTEMS, 2022, 8 (04) : 2699 - 2718
  • [22] A pairwise comparison based surrogate-assisted evolutionary algorithm for expensive multi-objective optimization
    Tian, Ye
    Hu, Jiaxing
    He, Cheng
    Ma, Haiping
    Zhang, Limiao
    Zhang, Xingyi
    SWARM AND EVOLUTIONARY COMPUTATION, 2023, 80
  • [23] Optimization of a supersonic wind tunnel diffuser using genetic algorithm
    Farahat, Said
    Javadpour, Seyyed Morteza
    Hamidi, Hesamodin Ebnodin
    Kadivar, Ebrahim
    ENGINEERING COMPUTATIONS, 2015, 32 (06) : 1691 - 1707
  • [24] Enabling High-Dimensional Surrogate-Assisted Optimization by Using Sliding Windows
    Werth, Bernhard
    Pitzer, Erik
    Affenzeller, Michael
    PROCEEDINGS OF THE 2017 GENETIC AND EVOLUTIONARY COMPUTATION CONFERENCE COMPANION (GECCO'17 COMPANION), 2017, : 1630 - 1637
  • [25] Surrogate-assisted optimization under uncertainty for design for remanufacturing considering material price volatility
    Tabassum, Mehnuma
    De Brabanter, Kris
    Kremer, Gul E.
    SUSTAINABLE MATERIALS AND TECHNOLOGIES, 2024, 42
  • [26] A dynamic adaptive hybrid surrogate-assisted particle swarm optimization algorithm for complex system design optimization
    You, Xiongxiong
    Zhang, Mengya
    Niu, Zhanwen
    ENGINEERING COMPUTATIONS, 2022, 39 (07) : 2505 - 2531
  • [27] Predictability on Performance of Surrogate-assisted Evolutionary Algorithm According to Problem Dimension
    Yu, Dong-Pil
    Kim, Yong-Hyuk
    PROCEEDINGS OF THE 2019 GENETIC AND EVOLUTIONARY COMPUTATION CONFERENCE COMPANION (GECCCO'19 COMPANION), 2019, : 91 - 92
  • [28] An enhanced surrogate-assisted differential evolution for constrained optimization problems
    Rafael de Paula Garcia
    Beatriz Souza Leite Pires de Lima
    Afonso Celso de Castro Lemonge
    Breno Pinheiro Jacob
    Soft Computing, 2023, 27 : 6391 - 6414
  • [29] A surrogate assisted evolutionary optimization method with application to the transonic airfoil design
    Shahrokhi, Ava
    Jahangirian, Alireza
    ENGINEERING OPTIMIZATION, 2010, 42 (06) : 497 - 515
  • [30] An enhanced surrogate-assisted differential evolution for constrained optimization problems
    Garcia, Rafael de Paula
    de Lima, Beatriz Souza Leite Pires
    Lemonge, Afonso Celso de Castro
    Jacob, Breno Pinheiro
    SOFT COMPUTING, 2023, 27 (10) : 6391 - 6414