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Mathias R. Schmidt

Publications and source records attributed to Mathias R. Schmidt.

2 recordsLinked to original sources

Remap for ALE Hydrodynamics via Direct Interpolation and Constraint Optimization

This work presents an optimization-based remap approach for arbitrary Lagrangian-Eulerian (ALE) hydrodynamics that preserves sharp features and physical bounds while conserving global quantities such as volume, mass, momentum, and total energy. Achieving these properties is the main challenge in remap methods. The presented approach is well-suited for high-order finite element (FE) discretizations and ALE methods that allow for large, purely Lagrangian displacements between successive remeshing and remapping steps. Recent advances in parallel, GPU-capable interpolation routines enable the initial transfer of all fields via direct physical-space interpolation, avoiding computationally costly and diffusive pseudo-transient PDE-based advection. The interpolation produces bounded fields with minimal numerical diffusion. However, it may violate the desired conservation of global properties. Thus, a subsequent optimization step restores conservation while preserving physical bounds. The presented method provides minimally diffusive remapping of finite element fields of arbitrary order. Moreover, it directly remaps quantities defined at integration points, eliminating the need for projection onto finite element spaces. The approach also mitigates typical challenges associated with synchronizing primal variables and conserved quantities in advection-based remap methods. The proposed optimization-based remap method is evaluated using standard two- and three-dimensional remap benchmarks and full ALE hydrodynamics simulations.

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Enriched Immersed Finite Element and Isogeometric Analysis -- Algorithms and Data Structures

Immersed finite element methods provide a convenient analysis framework for problems involving geometrically complex domains, such as those found in topology optimization and microstructures for engineered materials. However, their implementation remains a major challenge due to, among other things, the need to apply nontrivial stabilization schemes and generate custom quadrature rules. This article introduces the robust and computationally efficient algorithms and data structures comprising an immersed finite element preprocessing framework. The input to the preprocessor consists of a background mesh and one or more geometries defined on its domain. The output is structured into groups of elements with custom quadrature rules formatted such that common finite element assembly routines may be used without or with only minimal modifications. The key to the preprocessing framework is the construction of material topology information, concurrently with the generation of a quadrature rule, which is then used to perform enrichment and generate stabilization rules. While the algorithmic framework applies to a wide range of immersed finite element methods using different types of meshes, integration, and stabilization schemes, the preprocessor is presented within the context of the extended isogeometric analysis. This method utilizes a structured B-spline mesh, a generalized Heaviside enrichment strategy considering the material layout within individual basis functions$'$ supports, and face-oriented ghost stabilization. Using a set of examples, the effectiveness of the enrichment and stabilization strategies is demonstrated alongside the preprocessor$'$s robustness in geometric edge cases. Additionally, the performance and parallel scalability of the implementation are evaluated.

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