Automatic differentiation based discrete adjoint method for aerodynamic design optimization on unstructured meshes

被引:0
|
作者
Yisheng Gao [1 ]
Yizhao Wu [1 ]
Jian Xia [1 ]
机构
[1] College of Aerospace Engineering,Nanjing University of Aeronautics and Astronautics
关键词
Automatic differentiation(AD); Discrete adjoint; Navier-Stokes equations; Optimization; Unstructured meshes;
D O I
暂无
中图分类号
V211 [空气动力学];
学科分类号
0801 ; 080103 ; 080104 ;
摘要
A systematic methodology for formulating,implementing,solving and verifying discrete adjoint of the compressible Reynolds-averaged Navier-Stokes(RANS) equations for aerodynamic design optimization on unstructured meshes is proposed.First,a general adjoint formulation is constructed for the entire optimization problem,including parameterization,mesh deformation,flow solution and computation of the objective function,which is followed by detailed formulations of matrix-vector products arising in the adjoint model.According to this formulation,procedural components of implementing the required matrix-vector products are generated by means of automatic differentiation(AD) in a structured and modular manner.Furthermore,a duality-preserving iterative algorithm is employed to solve flow adjoint equations arising in the adjoint model,ensuring identical convergence rates for the tangent and the adjoint models.A three-step strategy is adopted to verify the adjoint computation.The proposed method has several remarkable features:the use of AD techniques avoids tedious and error-prone manual derivation and programming;duality is strictly preserved so that consistent and highly accurate discrete sensitivities can be obtained;and comparable efficiency to hand-coded implementation can be achieved.Upon the current discrete adjoint method,a gradient-based optimization framework has been developed and applied to a drag reduction problem.
引用
收藏
页码:611 / 627
页数:17
相关论文
共 50 条
  • [11] Discrete adjoint-based approach for optimization problems on three-dimensional unstructured meshes
    Mavriplis, Dimitri J.
    AIAA Journal, 2007, 45 (04): : 740 - 750
  • [12] Aerodynamic design optimization on unstructured grids with a continuous adjoint formulation
    Anderson, WK
    Venkatakrishnan, V
    COMPUTERS & FLUIDS, 1999, 28 (4-5) : 443 - 480
  • [13] Implementation of a parallel framework for aerodynamic design optimization on unstructured meshes
    Nielsen, EJ
    Anderson, WK
    Kaushik, DK
    PARALLEL COMPUTATIONAL FLUID DYNAMICS: TOWARDS TERAFLOPS, OPTIMIZATION, AND NOVEL FORMULATIONS, 2000, : 313 - 320
  • [14] An Efficient and Accurate Discrete Adjoint Method for Aerodynamic Design Optimization of Turbomachinery Blades
    Wu, Hangkong
    Wang, Dingxi
    Huang, Xiuquan
    Kung Cheng Je Wu Li Hsueh Pao/Journal of Engineering Thermophysics, 2024, 45 (01): : 46 - 57
  • [15] Aerodynamic shape optimization using the discrete adjoint method
    Nemec, M
    Zingg, DW
    48TH ANNUAL CONFERENCE OF THE CANADIAN AERONAUTICS AND SPACE INSTITUTE, PROCEEDINGS: CANADIAN AERONAUTICS-STAYING COMPETITIVE IN GLOBAL MARKETS, 2001, : 203 - 213
  • [16] Aerodynamic optimization of supersonic transport wing using unstructured adjoint method
    Kim, HJ
    Sasaki, D
    Obayashi, S
    Nakahashi, K
    AIAA JOURNAL, 2001, 39 (06) : 1011 - 1020
  • [17] Aerodynamic optimization of supersonic transport wing using unstructured adjoint method
    Kim, HJ
    Sasaki, D
    Obayashi, S
    Nakahashi, K
    COMPUTATIONAL FLUID DYNAMICS 2000, 2001, : 581 - 586
  • [18] Aerodynamic optimization of supersonic transport wing using unstructured adjoint method
    Kim, H.-J.
    Sasaki, D.
    Obayashi, S.
    Nakahashi, K.
    1600, American Inst. Aeronautics and Astronautics Inc. (39):
  • [19] Optimization of Vehicle Aerodynamic Drag Based on Discrete Adjoint Method and Surrogate Model
    He Y.
    Cao L.
    Zhang Z.
    Li Y.
    Chen Z.
    Qiche Gongcheng/Automotive Engineering, 2020, 42 (11): : 1577 - 1584
  • [20] Aerodynamic shape optimization based on discrete adjoint and RBF
    Abergo, Luca
    Morelli, Myles
    Guardone, Alberto
    JOURNAL OF COMPUTATIONAL PHYSICS, 2023, 477