Enriched immersed finite element and isogeometric analysis: algorithms and data structures

Abstract 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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Publication Details

Journal
Engineering With Computers
Published
2025-08-18
DOI
https://doi.org/10.1007/s00366-025-02163-7
Primary Topic
Advanced Numerical Analysis Techniques
Type
article
Field-Weighted Citation Impact
0.00

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article

Enriched immersed finite element and isogeometric analysis: algorithms and data structures

John A. Evans, Keenan Doble, Nils Wunsch, Mathias Schmidt et al.
Engineering With Computers
Advanced Numerical Analysis Techniques
article

Enriched immersed finite element and isogeometric analysis: algorithms and data structures

John A. Evans, Keenan Doble, Nils Wunsch, Mathias Schmidt, Lise Noël, Kurt Maute
article en

Abstract

Abstract 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.

Engineering With ComputersVol. 41(6)
Lawrence Livermore National Laboratory (US), University of Colorado Boulder (US), Delft University of Technology (NL)
National Science Foundation, U.S. Department of Energy, Defense Advanced Research Projects Agency, Advanced Research Projects Agency, Air Force Office of Scientific Research, Lawrence Livermore National Laboratory, Sandia National Laboratories
Openalex Percentile: Top 78%
Advanced Numerical Analysis Techniques
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