A fugacity-based Lattice Boltzmann method for multicomponent multiphase systems

TL;DR

This paper introduces a novel fugacity-based Lattice Boltzmann model for simulating multicomponent multiphase fluids with partial miscibility, capable of incorporating any multicomponent equation of state and accurately predicting thermodynamic and dynamic behaviors.

Contribution

The paper develops a fugacity-driven Lattice Boltzmann method that generalizes to any multicomponent equation of state and accurately models partially miscible multicomponent multiphase systems.

Findings

Accurately reproduces phase densities and compositions in vapor-liquid equilibrium.

Successfully models pressure-composition and temperature-composition envelopes.

Provides reliable predictions under dynamic conditions.

Abstract

The free energy model can extend the Lattice Boltzmann method to multiphase systems. However, there is a lack of models capable of simulating multicomponent multiphase fluids with partial miscibility. In addition, existing models cannot be generalized to honor thermodynamic information provided by any multicomponent equation of state of choice. In this paper, we introduce a free energy Lattice Boltzmann model where the forcing term is determined by the fugacity of the species, the thermodynamic property that connects species partial pressure to chemical potential calculations. By doing so, we are able to carry out multicomponent multiphase simulations of partially miscible fluids and generalize the methodology for use with any multicomponent equation of state of interest. We test this fugacity-based Lattice Boltzmann method for the cases of vapor-liquid equilibrium for two and three-component mixtures in various temperature and pressure conditions. We demonstrate that the model is able to reliably reproduce phase densities and compositions as predicted by multicomponent thermodynamics and can reproduce different characteristic pressure-composition and temperature-composition envelopes with a high degree of accuracy. We also demonstrate that the model can offer accurate predictions under dynamic conditions.