Meso-scale modeling: beyond local equilibrium assumption for multiphase flow

TL;DR

This paper discusses the limitations of traditional local equilibrium models in predicting multiphase flow dynamics and introduces mesoscale modeling, exemplified by the EMMS model, which better captures nonequilibrium phenomena in gas-solid fluidization systems.

Contribution

The paper presents a mesoscale modeling approach, based on the EMMS model, that improves prediction accuracy by accounting for nonequilibrium features in multiphase flows.

Findings

Mesoscale modeling captures nonequilibrium features like bimodal velocity distributions.

Traditional models relying on local equilibrium assumptions fail to predict flow dynamics accurately.

EMMS-based models enhance understanding of flow regime transitions.

Abstract

This is a summary of the article with the same title, accepted for publication in Advances in Chemical Engineering, 47: 193-277 (2015). Gas-solid fluidization is a typical nonlinear nonequilibrium system with multiscale structure. In particular, the mesoscale structure in terms of bubbles or clusters, which can be characterized by nonequilibrium features in terms of bimodal velocity distribution, energy non equipartition, and correlated density fluctuations, is the critical factor. Traditional two-fluid model (TFM) and relevant closures depend on local equilibrium and homogeneous distribution assumptions, and fail to predict the dynamic, nonequilibrium phenomena in circulating fluidized beds even with fine-grid resolution. In contrast, the mesoscale modeling, as exemplified by the energy-minimization multiscale (EMMS) model, is consistent with the nonequilibrium features in multiphase flows. Thus, the structure-dependent multi-fluid model conservation equations with the EMMS-based mesoscale modeling greatly improve the prediction accuracy in terms of flow, mass transfer, and reactions as well as the understanding of flow regime transitions. Such discrepancies raise the question of the applicability of the local equilibrium assumption underlying the TFM and further shed light to the necessity of mesoscale modeling.