Analysis and development of adjoint-based h-adaptive direct discontinuous Galerkin method for the compressible Navier-Stokes equations

被引:10
|
作者
Cheng, Jian [1 ,2 ]
Yue, Huiqiang [2 ]
Yu, Shengjiao [2 ]
Liu, Tiegang [2 ]
机构
[1] Inst Appl Phys & Computat Math, Beijing 100088, Peoples R China
[2] Beihang Univ, Sch Math & Syst Sci, LMIB, Beijing 100091, Peoples R China
基金
中国国家自然科学基金;
关键词
Direct discontinuous Galerkin method; Adjoint-based h-adaptation; Adjoint consistency; Compressible Navier-Stokes equations; FINITE-ELEMENT METHODS; PENALTY DG METHODS; CONSERVATION-LAWS; ERROR ESTIMATION; MESH ADAPTATION;
D O I
10.1016/j.jcp.2018.02.031
中图分类号
TP39 [计算机的应用];
学科分类号
081203 ; 0835 ;
摘要
In this paper, an adjoint-based high-order h-adaptive direct discontinuous Galerkin method is developed and analyzed for the two dimensional steady state compressible Navier-Stokes equations. Particular emphasis is devoted to the analysis of the adjoint consistency for three different direct discontinuous Galerkin discretizations: including the original direct discontinuous Galerkin method (DDG), the direct discontinuous Galerkin method with interface correction (DDG(IC)) and the symmetric direct discontinuous Galerkin method (SDDG). Theoretical analysis shows the extra interface correction term adopted in the DDG(IC) method and the SDDG method plays a key role in preserving the adjoint consistency. To be specific, for the model problem considered in this work, we prove that the original DDG method is not adjoint consistent, while the DDG(IC) method and the SDDG method can be adjoint consistent with appropriate treatment of boundary conditions and correct modifications towards the underlying output functionals. The performance of those three DDG methods is carefully investigated and evaluated through typical test cases. Based on the theoretical analysis, an adjoint-based h-adaptive DDG(IC) method is further developed and evaluated, numerical experiment shows its potential in the applications of adjoint-based adaptation for simulating compressible flows. (C) 2018 Elsevier Inc. All rights reserved.
引用
收藏
页码:305 / 326
页数:22
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