Emergent Mind

Abstract

The Cahn-Hilliard-Navier-Stokes (CHNS) equations represent the fundamental building blocks of hydrodynamic phase-field models for multiphase fluid flow dynamics. Due to the coupling between the Navier-Stokes equation and the Cahn-Hilliard equation, the CHNS system is non-trivial to solve numerically. Traditionally, a numerical extrapolation for the coupling terms is used. However, such brute-force extrapolation usually destroys the intrinsic thermodynamic structures of this CHNS system. This paper proposes a new strategy to reformulate the CHNS system into a constraint gradient flow formation. Under the new formulation, the reversible and irreversible structures are clearly revealed. This guides us to propose operator splitting schemes. The operator splitting schemes have several advantageous properties. First of all, the proposed schemes lead to several decoupled systems in smaller sizes to be solved at each time marching step. This significantly reduces computational costs. Secondly, the proposed schemes still guarantee the thermodynamic laws of the CHNS system at the discrete level. It ensures the thermodynamic laws, accuracy, and stability for the numerical solutions. In addition, unlike the recently populated IEQ or SAV approach using auxiliary variables, our resulting energy laws are formulated in the original variables. Our proposed framework lays out a foundation to design decoupled and energy stable numerical algorithms for hydrodynamic phase-field models. Furthermore, given different splitting steps, various numerical algorithms can be obtained, making this framework rather general. The proposed numerical algorithms are implemented. Their second-order accuracy in time is verified numerically. Some numerical examples and benchmark problems are calculated to verify the effectiveness of the proposed schemes.

We're not able to analyze this paper right now due to high demand.

Please check back later (sorry!).

Generate a summary of this paper on our Pro plan:

We ran into a problem analyzing this paper.

Newsletter

Get summaries of trending comp sci papers delivered straight to your inbox:

Unsubscribe anytime.