Abstract
The previous sub-grid, energy-minimization multi-scale (EMMS) drag models were all established at certain fixed operating conditions and material properties. In this study, we developed a generic EMMS drag for simulating dense fluidized beds by using the Artificial Neural Network (ANN) to cover a wide range of operating conditions and material properties. To this end, the algorithm of the EMMS model was optimized to provide a huge dataset efficiently and the performance of ANN was tested by training with different numbers of data and hidden layer structures. The EMMS-ANN model was determined by balancing the training precision and computational time and then applied to the simulation of five fluidized beds under different operating conditions and material properties. It was found that the simulation with the EMMS-ANN drag enables reasonable prediction and shows good applicability to a wide range of dense fluidization.
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