Asymptotic-Preserving Schemes for Kinetic-Fluid Modeling of Mixture Flows

We consider coupled models for particulate flows, where the disperse phase is made of particles with distinct sizes. We are thus led to a system coupling the incompressible Navier-Stokes equations to the multi-component Vlasov-Fokker-Planck equations. We design an asymptotic-preserving numerical scheme to approximate the system. The scheme is based on suitable implicit treatment of the stiff drag force term as well as the Fokker-Planck operator, and can be formally shown to capture the hydrodynamic limit with time step and mesh size independent of the Stokes number. Numerical examples illustrate the accuracy and asymptotic behavior of the scheme, with several interesting applications.

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Publication Details

Journal
Communications in Computational Physics
Published
2024-09-05
DOI
https://doi.org/10.4208/cicp.oa-2023-0298
Primary Topic
Advanced Mathematical Modeling in Engineering
Type
article
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article

Asymptotic-Preserving Schemes for Kinetic-Fluid Modeling of Mixture Flows

Shi Jin, Yi-Wen Lin
Communications in Computational Physics
Advanced Mathematical Modeling in Engineering
article

Asymptotic-Preserving Schemes for Kinetic-Fluid Modeling of Mixture Flows

Shi Jin, Yi-Wen Lin
article en

Abstract

We consider coupled models for particulate flows, where the disperse phase is made of particles with distinct sizes. We are thus led to a system coupling the incompressible Navier-Stokes equations to the multi-component Vlasov-Fokker-Planck equations. We design an asymptotic-preserving numerical scheme to approximate the system. The scheme is based on suitable implicit treatment of the stiff drag force term as well as the Fokker-Planck operator, and can be formally shown to capture the hydrodynamic limit with time step and mesh size independent of the Stokes number. Numerical examples illustrate the accuracy and asymptotic behavior of the scheme, with several interesting applications.

Communications in Computational PhysicsVol. 36(2)
Openalex Percentile: Top 82%
Advanced Mathematical Modeling in Engineering
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