Any motion, forced or free, of boundary affects the flow field around this boundary. A new kind of reduced order model (ROM) based on hybrid deep neural network is proposed to model flow field evolution process of uns...Any motion, forced or free, of boundary affects the flow field around this boundary. A new kind of reduced order model (ROM) based on hybrid deep neural network is proposed to model flow field evolution process of unsteady flow around moving boundary. This hybrid deep neural network can map the relationship between the flow field at the next time step and the flow field and boundary positions at the previous time steps. Based on the learned information, the hybrid deep neural network can quickly and accurately predict the flow field. Unsteady flows around forced oscillation cylinder with various amplitudes, frequencies, and Reynolds numbers are simulated to establish the training and testing datasets. The prediction results of the hybrid deep neural network and the computational fluid dynamics (CFD) simulation results are consistent with high accuracy. The forces on the moving boundary can be integrated through the predicted flow field data. Good performance makes this new ROM method can be used in many fluid dynamics research fields, which needs fast and accurate simulation.展开更多
基金This work was supported by the National Natural Science Foundation of China(Grants 11872293,11672225)Science and Technology on Reliability and Environment Engineering Laboratory(Grant 6142004190307)the Program of Introducing Talents and Innovation of Discipline(Grant B18040).
文摘Any motion, forced or free, of boundary affects the flow field around this boundary. A new kind of reduced order model (ROM) based on hybrid deep neural network is proposed to model flow field evolution process of unsteady flow around moving boundary. This hybrid deep neural network can map the relationship between the flow field at the next time step and the flow field and boundary positions at the previous time steps. Based on the learned information, the hybrid deep neural network can quickly and accurately predict the flow field. Unsteady flows around forced oscillation cylinder with various amplitudes, frequencies, and Reynolds numbers are simulated to establish the training and testing datasets. The prediction results of the hybrid deep neural network and the computational fluid dynamics (CFD) simulation results are consistent with high accuracy. The forces on the moving boundary can be integrated through the predicted flow field data. Good performance makes this new ROM method can be used in many fluid dynamics research fields, which needs fast and accurate simulation.
