Reduced order modeling of blood perfusion in parametric multipatch liver lobules

Abstract In this paper, we present a computationally efficient reduced order model for obtaining blood perfusion profiles within parametric functional units of the liver called ‘lobules’. We consider Darcy’s equation in two-dimensional hexagonal lobule domains with six flow inlets and one outlet, whose positions are parameterized to represent varying lobule geometries. To avoid the meshing effort for every new lobule domain, we map the parametric domain onto a single reference domain. By making use of the contra-variant Piola mapping, we represent solutions of the parametric domains in the reference domain. We then construct a reduced order model via proper orthogonal decomposition (POD). Additionally, we employ the discrete empirical interpolation method (DEIM) to treat the non-affine parameter dependence that appears due to the geometric mapping. For sampling random shapes and sizes of lobules, we generate Voronoi diagrams (VD) from Delaunay triangulations and use an energy minimization problem to control the packing of the lobule structures. To reduce the dimension of the parameterized problem, we exploit the mesh symmetry of the full lobule domain to split the full domain into six rotationally symmetric subdomains. We then use the same set of reduced order basis (ROB) functions within each subdomain for the construction of the reduced order model. We close our study by a thorough investigation of the accuracy and computational efficiency of the resulting reduced order model.

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Publication Details

Journal
Advanced Modeling and Simulation in Engineering Sciences
Published
2024-12-11
DOI
https://doi.org/10.1186/s40323-024-00274-2
Citations
4
Primary Topic
Model Reduction and Neural Networks
Type
article
Field-Weighted Citation Impact
1.12

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article

Reduced order modeling of blood perfusion in parametric multipatch liver lobules

Stein K.F. Stoter, David Néron, Dominik Schillinger, Ahsan Ali Siddiqui et al.
4 citations
Advanced Modeling and Simulation in Engineering Sciences
Model Reduction and Neural Networks
1.12
article

Reduced order modeling of blood perfusion in parametric multipatch liver lobules

Stein K.F. Stoter, David Néron, Dominik Schillinger, Ahsan Ali Siddiqui, Etienne Jessen
article en
4 citations

Abstract

Abstract In this paper, we present a computationally efficient reduced order model for obtaining blood perfusion profiles within parametric functional units of the liver called ‘lobules’. We consider Darcy’s equation in two-dimensional hexagonal lobule domains with six flow inlets and one outlet, whose positions are parameterized to represent varying lobule geometries. To avoid the meshing effort for every new lobule domain, we map the parametric domain onto a single reference domain. By making use of the contra-variant Piola mapping, we represent solutions of the parametric domains in the reference domain. We then construct a reduced order model via proper orthogonal decomposition (POD). Additionally, we employ the discrete empirical interpolation method (DEIM) to treat the non-affine parameter dependence that appears due to the geometric mapping. For sampling random shapes and sizes of lobules, we generate Voronoi diagrams (VD) from Delaunay triangulations and use an energy minimization problem to control the packing of the lobule structures. To reduce the dimension of the parameterized problem, we exploit the mesh symmetry of the full lobule domain to split the full domain into six rotationally symmetric subdomains. We then use the same set of reduced order basis (ROB) functions within each subdomain for the construction of the reduced order model. We close our study by a thorough investigation of the accuracy and computational efficiency of the resulting reduced order model.

Advanced Modeling and Simulation in Engineering SciencesVol. 11(1)
Leibniz University Hannover (DE), Centre National de la Recherche Scientifique (FR), Université Paris-Saclay (FR), Technische Universität Darmstadt (DE), CentraleSupélec (FR), Eindhoven University of Technology (NL)
European Commission, Deutsche Forschungsgemeinschaft
Good health and well-being
Openalex Percentile: Top 22%
Model Reduction and Neural Networks
1.12
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