(204a) A Critical Assessment of the Energy Minimization Multi-Scale (EMMS) Model for Turbulent Fluidized Beds

Coarse grid simulations of circulating fluidized bed (CFB) reactors require an accurate estimation of the particle drag force. Conventional homogeneous drag laws are known to be insufficient for such configurations owing to the heterogeneous particle structure that emerges (e.g., clustering). The Energy Minimization Multi-Scale (EMMS) model has received much attention in recent years for capturing the effect of heterogeneity in simulations of CFB reactors. Originally derived for calculating the net particle force in CFB risers, EMMS has been extended and integrated within coarse grid simulations based on both Euler-Euler (EE) and Euler-Lagrange (EL) approaches to modify the drag force in each computational cell. EMMS decomposes the flow field into a dense and dilute phase that interact with each other. Many assumptions are made in the derivation of this model including (i) heterogeneity is represented by a single spherical cluster; (ii) invariant gas and particle superficial velocities for the dilute and dense phases; (iii) constant void fraction for each phase; and (iv) invariant particle drag force within each phase among others. Leveraging highly resolved EL simulations, we present a critical assessment of EMMS. The fundamental assumptions are probed in detail and model accuracy is quantified. We consider fully-developed homogeneous flow of solid particles settling under gravity. Strong interphase coupling results in the spontaneous generation of dense clusters that generate and sustain underlying turbulence. This setup is analogous to an individual computational cell in coarse-grained EE or EL simulations for which the EMMS model is typically applied. A structure tracking algorithm is applied to the highly-resolved EL data to measure cluster characteristics. Results show significant variation in the two-phase flow parameters, which differs from the key assumptions in the EMMS model. This work provides a framework for incorporating the observed variation and improving the standard EMMS model.