Rotating machinery flow field prediction based on hybrid neural network

The flow phenomena in fluid machinery such as pumps, wind turbines and turbines are complex. For large-scale engineering problems, the computational resources required to solve the Navier-Stokes(N-S) equation are large. In order to solve this problem, a fast and accurate solution method based on hybrid neural network (Convolutional Neural Network - Long Short-Term Memory) is proposed. The nonlinear mapping relationship between the flow field characteristics of rotating machinery and the eddy viscosity coefficient is constructed. The deep learning model is used to replace the RANS standard k-& varepsilon; model, and the eddy viscosity coefficient distribution cloud diagram of rotating machinery is obtained. The results show that the predicted value of the eddy viscosity coefficient of the hybrid neural network model on different slices is in good agreement with the original value and the error is small. Compared with traditional machine learning models such as random forest model, hybrid neural network model takes up only 11.4% of memory, and the accuracy is much better than the random forest model. The hybrid neural network model proposed in this paper has great potential in the prediction of the flow field of rotating machinery.