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The drag force model is vital for capturing gas–solid flow dynamics in many simulation approaches. Most of the homogeneous drag models in the literature are expressed as a function of phase fraction (ε) and particle Reynolds number (Res). In this work, we use a "big data" approach to analyze ~108 data points for drag coefficient (Fd) for Geldart Group A particles at atmospheric pressure and find that the contribution of Res on Fd is much less than ε based on the Maximal information coefficient analysis. Thus, these drag models are separately reduced to machine learning and conventional expressions only related to ε. The reduced models achieve almost the same predictive performance as the originals in bubbling, turbulent, and jet fluidizations. Moreover, the reduced models provide better numerical stability for coarse grid simulations. These findings provide new insights into the drag coefficient for Geldart Group A particles under full fluidization conditions. [ABSTRACT FROM AUTHOR]

• Applicability of different mesoscale drags for predicting hydrodynamics over all flow regimes. • Applying the optimal drag model to design bed internals and quantify bed size effects. • The proposed nature-inspired snowflake-type internal significantly improves fluidization quality. • Proposing a preliminary correlation to quantify the bed size-dependent effect. This work systematically investigates the applicability of our recently developed three mesoscale drag models for predictions of hydrodynamics over extensive flow regimes ranging from bubbling, turbulent, rapid to full-loop fluidization. Subsequently, we try to apply the suitable mesoscale model for designing bed internals and quantifying bed size effects in a dense turbulent fluidized bed reactor. It is found that the optimized nature-inspired snowflake-type internal scheme contributes to breaking up the bubbles effectively and thereby weakening the degree of the particle clustering near the wall significantly. This phenomenon can enhance the uniform solid hold-up distribution (A ∼25% reduction of its nonuniformity) and improve the fluidization quality. Moreover, the bed size evidently affects the gas–solid fluidization characteristics and we thereby propose a preliminary correlation to quantify this size-dependent effect. This work may contribute to facilitating the CFD design practice of multiphase reactors from an art to science. [ABSTRACT FROM AUTHOR]

The gas-solid fluidized bed is a complex multiscale system with diverse mesoscale structures such as bubbles and particle clusters. The Euler-Euler two-fluid model is one of the most popular methods in simulating this kind of system. However, when using coarse grids, the mesoscale structures involved can hardly be characterized. In this study, a new quasi-three-phase method was proposed to properly account for the mesoscale structures by using three phases (bubble phase/solid phase/emulsion phase) to represent the gas-solid fluidized bed, and comparative simulations were performed under different gas velocities. It was found that the simulation could reasonably predict the flow changes from bubbling fluidization to turbulent fluidization. The predicted gas velocity at the flow pattern transition is close to that calculated by the empirical equation concerning the bed pressure fluctuation, and the predicted solids fraction distribution fits well with the experimental data in the literature. [Display omitted] • Both bubbles and particle clusters are considered simultaneously. • Application of three-fluid model in gas-solid two-phase flow is feasible. • Three-fluid model reveals well the flow pattern transition of FBR. • The simulation using the classical drag model can match the experimental data. [ABSTRACT FROM AUTHOR]