A Locally Conservative Mixed Finite Element Framework for Coupled Hydro-Mechanical-Chemical Processes in Heterogeneous Porous Media
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Abstract (arXiv)
This paper presents a mixed finite element framework for coupled hydro-mechanical-chemical processes in heterogeneous porous media. The framework combines two types of locally conservative discretization schemes: (1) an enriched Galerkin method for reactive flow, and (2) a three-field mixed finite element method for coupled fluid flow and solid deformation. This combination ensures local mass conservation, which is critical to flow and transport in heterogeneous porous media, with a relatively affordable computational cost. A particular class of the framework is constructed for calcite precipitation/dissolution reactions, incorporating their nonlinear effects on the fluid viscosity and solid deformation. Linearization schemes and algorithms for solving the nonlinear algebraic system are also presented. Through numerical examples of various complexity, we demonstrate that the proposed framework is a robust and efficient computational method for simulation of reactive flow and transport in deformable porous media, even when the material properties are strongly heterogeneous and anisotropic.
Key facts. Cited by 5 papers in the map (data through 7 Oct 2026). First posted on arXiv 10 Oct 2020.
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Citing papers per year: 2021: 3; 2022: 1; 2023: 1.
5 papers.
- 2021A framework for data-driven solution and parameter estimation of PDEs using conditional generative adversarial networkscited by 30
- 2021Non-intrusive reduced order modeling of natural convection in porous media using convolutional autoencoders: comparison with linear subspace techniquescited by 23
- 2022Reduced order modeling for flow and transport problems with Barlow Twins self-supervised learningcited by 12
- 2021Non-intrusive reduced order modeling of poroelasticity of heterogeneous media based on a discontinuous Galerkin approximationcited by 6
- 2023Progressive reduced order modeling: empowering data-driven modeling with selective knowledge transfercited by 0
- 2023Progressive reduced order modeling: empowering data-driven modeling with selective knowledge transfer4 Oct 2023
- 2022Reduced order modeling for flow and transport problems with Barlow Twins self-supervised learning11 Feb 2022
- 2021Non-intrusive reduced order modeling of natural convection in porous media using convolutional autoencoders: comparison with linear subspace techniques23 Jul 2021
- 2021A framework for data-driven solution and parameter estimation of PDEs using conditional generative adversarial networks27 May 2021
- 2021Non-intrusive reduced order modeling of poroelasticity of heterogeneous media based on a discontinuous Galerkin approximation28 Jan 2021