A novel data-driven reduced-order model for the fast prediction of gas-solid heat transfer in fluidized beds

Abstract

Computational fluid dynamics − discrete element method (CFD-DEM) emerges as a promising tool to model dense gas–solid two-phase flow in fluidized beds, yet the numerical efficiency is still far from being satisfactory. Accordingly, this work develops a proper orthogonal decomposition (POD) reduced-order model (ROM) based on the CFD-DEM method for the fast prediction of gas–solid flow and heat transfer in a bubbling fluidized bed (BFB). The POD method is employed to decompose particle coordinates and temperature into spatial modes and time evolution coefficients. The radial basis function neural network (RBFNN) and temporal convolutional neural network (TCN) are utilized to predict the time evolution coefficients. Regarding the POD modes, the particle temperature exhibits an obvious monotonic or periodic characteristic compared to the particle coordinates, indicating the potential for long-term prediction. The RBFNN-ROM and TCN-ROM reconstruct the flow field effectively, achieving acceleration ratios of 2000 and 3000, respectively. The ROM developed in this study holds the potential to enable real-time prediction of industrial processes, thus paving the way for the realization of digital twins.