An Adaptive Subdomain Coupling Approach in Domain Decomposition for Multiphase Porous Media Flow

[2409.10875] An Adaptive Subdomain Coupling Approach in Domain Decomposition for Multiphase Porous Media Flow Skip to main content Search Submit Donate Log in Search arXiv Press Enter to search · Advanced search Mathematics > Numerical Analysis arXiv:2409.10875 (math) [Submitted on 17 Sep 2024 ( v1 ), last revised 10 Apr 2026 (this version, v3)] Title: An Adaptive Subdomain Coupling Approach in Domain Decomposition for Multiphase Porous Media Flow Authors: Shizhe Li , Li Zhao , Chen-Song Zhang View a PDF of the paper titled An Adaptive Subdomain Coupling Approach in Domain Decomposition for Multiphase Porous Media Flow, by Shizhe Li and 1 other authors View PDF HTML (experimental) Abstract: The numerical simulation of large-scale multiphase flow in porous media is of considerable importance across various application fields, particularly in the petroleum industry. The fully implicit method is preferred in reservoir simulations owing to its superior numerical stability and more relaxed time step constraints. However, this method requires solving a large nonlinear system, which becomes highly nonlinear in complex heterogeneous media with small grid scales, emphasizing the need for efficient and convergent numerical methods to accelerate nonlinear solvers on parallel computing systems. In this paper, we present an adaptively coupled subdomain framework based on domain decomposition methods. This framework effectively handles strong local nonlinearities in global problems by solving subproblems within the coupled regions. Furthermore, we propose several adaptive coupling strategies and present a novel method for calculating initial guesses, aimed at improving the convergence and scalability of nonlinear solvers. A series of numerical experiments validate the effectiveness and robustness of the proposed framework. Additionally, large-scale reservoir simulations demonstrate that the proposed method achieves competitive parallel performance. Subjects: Numerical Analysis (math.NA) Cite as: arXiv:2409.10875 [math.NA] (or arXiv:2409.10875v3 [math.NA] for this version) https://doi.org/10.48550/arXiv.2409.10875 Focus to learn more arXiv-issued DOI via DataCite Submission history From: Li Zhao [ view email ] [v1] Tue, 17 Sep 2024 04:09:44 UTC (19,196 KB) [v2] Mon, 2 Jun 2025 11:02:01 UTC (8,708 KB) [v3] Fri, 10 Apr 2026 01:50:43 UTC (9,105 KB) Full-text links: Access Paper: View a PDF of the paper titled An Adaptive Subdomain Coupling Approach in Domain Decomposition for Multiphase Porous Media Flow, by Shizhe Li and 1 other authors View PDF HTML (experimental) TeX Source view license Current browse context: math.NA < prev | next > new | recent | 2024-09 Change to browse by: cs cs.NA math References & Citations NASA ADS Google Scholar Semantic Scholar export BibTeX citation Loading... BibTeX formatted citation × loading... Data provided by: Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer ( What is the Explorer? ) Connected Papers Toggle Connected Papers ( What is Connected Papers? ) Litmaps Toggle Litmaps ( What is Litmaps? ) scite.ai Toggle scite Smart Citations ( What are Smart Citations? ) Code, Data, Media Code, Data and Media Associated with this Article alphaXiv Toggle alphaXiv ( What is alphaXiv? ) Links to Code Toggle CatalyzeX Code Finder for Papers ( What is CatalyzeX? ) DagsHub Toggle DagsHub ( What is DagsHub? ) GotitPub Toggle Gotit.pub ( What is GotitPub? ) Huggingface Toggle Hugging Face ( What is Huggingface? ) ScienceCast Toggle ScienceCast ( What is ScienceCast? ) Demos Demos Replicate Toggle Replicate ( What is Replicate? ) Spaces Toggle Hugging Face Spaces ( What is Spaces? ) Spaces Toggle TXYZ.AI ( What is TXYZ.AI? ) Related Papers Recommenders and Search Tools Link to Influence Flower Influence Flower ( What are Influence Flowers? ) Core recommender toggle CORE Recommender ( What is CORE? ) Author Venue Institution Topic About arXivLabs arXivLabs: experimental projects with community collaborators arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs . Which authors of this paper are endorsers? | Disable MathJax ( What is MathJax? ) We gratefully acknowledge support from our major funders , member institutions , , and all contributors. About · Help · Contact · Subscribe · Copyright · Privacy · Accessibility · Operational Status (opens in new tab) Major funding support from

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