Generation of solution-adaptive computational grids using optimization

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Abstract

A new method for generating solution-adaptive computational grids is presented that builds on the requirement that the adapted grid retains maximum possible smoothness and local orthogonality. The approach taken is one of nonlinear optimization, where an objective function combining measures of grid smoothness, local orthogonality, and cell volume control is minimized using a fast iterative scheme. The method is multidimensional by construction, and accepts any arbitrary grid as input, even a grid that is initially overlapped. Several applications to published test problems allow comparisons with some of the existing adaptive grid generation methods. An example of dynamic adaptation to the solution of finite difference equations is also given that demonstrates the error reduction capabilities of the new adaptive grid generation and optimization method, and suggests further applications to practical engineering problems.

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