Mixed FEM for Coupled Unsteady Fluid Flow Problems with [math]-Type Brinkman–Forchheimer Framework and its Application for Reverse-Osmosis Desalination
{"title":"Mixed FEM for Coupled Unsteady Fluid Flow Problems with [math]-Type Brinkman–Forchheimer Framework and its Application for Reverse-Osmosis Desalination","authors":"Zeinab Gharibi, Mostafa Abbaszadeh, Mehdi Dehghan","doi":"10.1137/25m1767650","DOIUrl":null,"url":null,"abstract":"SIAM Journal on Numerical Analysis, Volume 64, Issue 4, Page 1645-1681, August 2026. <br/> Abstract. This work analyzes a fully discrete mixed finite element method in a Banach space framework for solving nonstationary coupled fluid flow problems modeled by the Brinkman–Forchheimer equations, with applications to reverse osmosis. The model couples unsteady [math]-type convective Brinkman–Forchheimer and transport equations with nonlinear boundary conditions across a semipermeable membrane. A mixed formulation is used for both the fluid equation (pseudostress-velocity) and the transport equation (concentration, its gradient, and a Lagrange multiplier from the membrane condition). The continuous problem is reformulated in Banach spaces as a fixed-point problem, enabling a well-posedness analysis via differential-algebraic system theory. Spatial discretization employs lowest-order Raviart–Thomas elements for fluxes and piecewise constants for primal variables, while linear elements are used for the Lagrange multiplier. A fully discrete Galerkin scheme with backward Euler time-stepping is proposed. Its well-posedness and stability are proven using a fixed-point argument, and optimal convergence rates are established. Numerical results confirm the theoretical error estimates and demonstrate the method’s effectiveness.","PeriodicalId":49527,"journal":{"name":"SIAM Journal on Numerical Analysis","volume":"29 1","pages":""},"PeriodicalIF":3.2000,"publicationDate":"2026-08-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"SIAM Journal on Numerical Analysis","FirstCategoryId":"100","ListUrlMain":"https://doi.org/10.1137/25m1767650","RegionNum":2,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"MATHEMATICS, APPLIED","Score":null,"Total":0}
引用次数: 0
Abstract
SIAM Journal on Numerical Analysis, Volume 64, Issue 4, Page 1645-1681, August 2026. Abstract. This work analyzes a fully discrete mixed finite element method in a Banach space framework for solving nonstationary coupled fluid flow problems modeled by the Brinkman–Forchheimer equations, with applications to reverse osmosis. The model couples unsteady [math]-type convective Brinkman–Forchheimer and transport equations with nonlinear boundary conditions across a semipermeable membrane. A mixed formulation is used for both the fluid equation (pseudostress-velocity) and the transport equation (concentration, its gradient, and a Lagrange multiplier from the membrane condition). The continuous problem is reformulated in Banach spaces as a fixed-point problem, enabling a well-posedness analysis via differential-algebraic system theory. Spatial discretization employs lowest-order Raviart–Thomas elements for fluxes and piecewise constants for primal variables, while linear elements are used for the Lagrange multiplier. A fully discrete Galerkin scheme with backward Euler time-stepping is proposed. Its well-posedness and stability are proven using a fixed-point argument, and optimal convergence rates are established. Numerical results confirm the theoretical error estimates and demonstrate the method’s effectiveness.
期刊介绍:
SIAM Journal on Numerical Analysis (SINUM) contains research articles on the development and analysis of numerical methods. Topics include the rigorous study of convergence of algorithms, their accuracy, their stability, and their computational complexity. Also included are results in mathematical analysis that contribute to algorithm analysis, and computational results that demonstrate algorithm behavior and applicability.