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arXiv · 2001.11536

Simulation-Driven Optimization of High-Order Meshes in ALE Hydrodynamics

Veselin Dobrev·Patrick Knupp·Tzanio Kolev·Ketan Mittal·Robert Rieben·Vladimir Tomov
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Abstract

In this paper we propose tools for high-order mesh optimization and demonstrate their benefits in the context of multi-material Arbitrary Lagrangian-Eulerian (ALE) compressible shock hydrodynamic applications. The mesh optimization process is driven by information provided by the simulation which uses the optimized mesh, such as shock positions, material regions, known error estimates, etc. These simulation features are usually represented discretely, for instance, as finite element functions on the Lagrangian mesh. The discrete nature of the input is critical for the practical applicability of the algorithms we propose and distinguishes this work from approaches that strictly require analytical information. Our methods are based on node movement through a high-order extension of the Target-Matrix Optimization Paradigm (TMOP) of Knupp. The proposed formulation is fully algebraic and relies only on local Jacobian matrices, so it is applicable to all types of mesh elements, in 2D and 3D, and any order of the mesh. We discuss the notions of constructing adaptive target matrices and obtaining their derivatives, reconstructing discrete data in intermediate meshes, node limiting that enables improvement of global mesh quality while preserving space-dependent local mesh features, and appropriate normalization of the objective function. The adaptivity methods are combined with automatic ALE triggers that can provide robustness of the mesh evolution and avoid excessive remap procedures. The benefits of the new high-order TMOP technology are illustrated on several simulations performed in the high-order ALE application BLAST.

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Veselin Dobrev, Patrick Knupp, Tzanio Kolev, Ketan Mittal, Robert Rieben, Vladimir Tomov. 2020-01-30. Simulation-Driven Optimization of High-Order Meshes in ALE Hydrodynamics. https://doi.org/10.1016/j.compfluid.2020.104602

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