Grid adaptation for functional outputs of compressible flow simulations

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Aeronautics and Astronautics, 2002.Includes bibliographical references (p. 143-150).An error correction and grid adaptive method is presented for improving the accuracy of functional outputs of compressible flow simulations. The procedure is based on an adjoint formulation in which the estimated error in the functional can be directly related to the local residual errors of both the primal and adjoint solutions. This relationship allows local error contributions to be used as indicators in a grid adaptive method designed to produce specially tuned grids for accurately estimating the chosen functional. The method is applied to two-dimensional inviscid and viscous (laminar) flows using standard finite volume discretizations, and to scalar convection-diffusion using a Galerkin finite element discretization. Isotropic h-refinement is used to iteratively improve the grids in a series of subsonic, transonic, and supersonic inviscid test cases. A commonly-used adaptive method that employs a curvature sensor based on measures of the local interpolation error in the solution is implemented to comparatively assess the performance of the proposed output-based procedure. In many cases, the curvature-based method fails to terminate or produces erroneous values for the functional at termination. In all test cases, the proposed output-based method succeeds in terminating once the prescribed accuracy level has been achieved for the chosen functional.(cont.) Output-based adaptive criteria are incorporated into an anisotropic grid-adaptive procedure for laminar Navier-Stokes simulations. The proposed method can be viewed as a merging of Hessian-based adaptation with output error control. A series of airfoil test cases are presented for Reynolds numbers ranging from 5,000 to 100,000. The proposed adaptive method is shown to compare very favorably in terms of output accuracy and computational efficiency relative to pure Hessian-based adaptation.by David Anthony Venditti.Ph.D