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Anisotropic Solution Adaptive Unstructured Grid Generation Using Aflr

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An existing volume grid generation procedure, AFLR3, was successfully modified to generate anisotropic tetrahedral elements using a directional metric transformation defined at source nodes. The procedure can be coupled with a solver and an error estimator as part of an overall anisotropic solution adaptation methodology. It is suitable for use with an error estimator based on an adjoint, optimization, sensitivity derivative, or related approach. This offers many advantages, including more efficient point placement along with robust and efficient error estimation. It also serves as a framework for true grid optimization wherein error estimation and computational resources can be used as cost functions to determine the optimal point distribution. Within AFLR3 the metric transformation is implemented using a set of transformation vectors and associated aspect ratios. The modified overall procedure is presented along with details of the anisotropic transformation implementation. Multiple two-and three-dimensional examples are also presented that demonstrate the capability of the modified AFLR procedure to generate anisotropic elements using a set of source nodes with anisotropic transformation metrics. The example cases presented use moderate levels of anisotropy and result in usable element quality. Future testing with various flow solvers and methods for obtaining transformation metric information is needed to determine practical limits and evaluate the efficacy of the overall approach.

58 pages, Paperback

Published July 23, 2013

About the author

David Marcum

63 books49 followers
Librarian Note: There is more than one author by this name in the Goodreads database.

David Marcum and Steven Smith travel the world teaching people to utilize the corporate asset of ego and limit its liabilities. With decades of experience and degrees in management and psychology, they¹ve worked with organizations including Microsoft, Accenture, the U.S. Air Force, General Electric, Disney, and State Farm. Their work has been published in eighteen languages in more than forty countries.

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