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

被引:8
|
作者
Gao, Yisheng [1 ]
Wu, Yizhao [1 ]
Xia, Jian [1 ]
机构
[1] Nanjing Univ Aeronaut & Astronaut, Coll Aerosp Engn, Nanjing 210016, Peoples R China
关键词
Automatic differentiation (AD); Discrete adjoint; Navier-Stokes equations; Optimization; Unstructured meshes; NAVIER-STOKES EQUATIONS; EFFICIENT; ACCURATE; IMPLICIT;
D O I
10.1016/j.cja.2017.01.009
中图分类号
V [航空、航天];
学科分类号
08 ; 0825 ;
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. (C) 2017 Chinese Society of Aeronautics and Astronautics. Production and hosting by Elsevier Ltd.
引用
收藏
页码:611 / 627
页数:17
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