Deep Learning-based Intelligent Spatio-Temporal Prediction of Full 3D Gas-solid Two-phase Flow
Gas-solid fluidized beds have been widely studied and applied in chemical,metallurgical and pharmaceutical fields.An in-depth study of the kinetic behavior of gas-solid two-phase flow in fluidized beds is beneficial to the design and performance optimization of fluidized bed equipments.In this study,a data-driven full 3D deep time-series model is constructed using the deep learning technology to learn the complex kinetic behavior of 3D gas-solid two-phase temporal flow fields in the fluidized bed.With this model,it is able to achieve a reasonable prediction of the velocity fields of gas and particle phases in the fluidized bed under unknown incoming flow velocity conditions.The test results show that the prediction results of this full 3D intelligent model are highly consistent with the CFD calculation results,and have good generalization ability.In addition,the model is hundreds of times faster than the traditional numerical simulation and can be used for fast prediction of the flow field to alleviate the time-consuming issue of numerical simulation.
deep learninggas-solid two-phase flow3D spatio-temporal predictionfluidized bed
NETL
NSTL
万方数据
