Simulation of flow field evolution in fluidized bed based on artificial neural network
Computational fluid dynamics(CFD)is a commonly used method to simulate complex gas-solid flow in fluidized beds.Due to the solution of partial/ordinary differential equations,the computational efficiency of this method is still low even if the coarse-grained method is used.The flow field simulation method based on data-driven artificial neural network(ANN)model can avoid the equation solving process and achieve efficient calculation.At present,researchers have applied the ANN model to the prediction of single-phase flow field,and there are only a few studies on the complete fluidized gas-solid two-phase flow field.This work combines CFD and ANN to develop an ANN based field evolution model that quickly obtains the evolution of the flow field in the fluidized bed.Compared with those complex large models,a compact network model has been developed and can be used to complete the prediction of complex two-phase flow field.The model includes different network structures for predictions of particle concentration,gas pressure,and gas-solid two-phase velocity.The results obtained by simulating the fluidized bed with the multiphase particle-in-cell(MP-PIC)method are used as data sets for training.The verification results show that the ANN model successfully realizes the prediction of particle concentration,gas pressure,and gas-solid two-phase velocity in the fluidized bed.In terms of accuracy,the ANN model can accurately predict the flow field in a time step,and there are still obvious errors in the long-term flow field prediction.In terms of computational efficiency,the calculation speed of the ANN model is about 13 000 times that of the MP-PIC method.The multi-time-step continuous prediction performance of current model gradually deteriorates with time,and further research still needs to be done to improve this issue.
flow field evolutionartificial neural networkmultiphase flowfluidized bedcomputational fluid dynamics
NETL
NSTL
万方数据
