Simulation and prediction of three-dimensional rotating flows based on convolutional neural networks

Abstract

Two deep learning models to reconstruct three-dimensional (3D) steady-state rotating flows are proposed to capture the spatial information: the 3D convolutional encoder–decoder and the 3D convolutional long short-term memory model. They are based on deep learning methods such as the encoder–decoder convolutional neural network and recurrent neural network. Their common components are an encoder, a middle layer, and a decoder. The rotating flows in a stirred tank with four inclined blades are calculated for the dataset to train and test the two models. A workflow for the flow field reconstruction is established and all variants made up of various components are executed according to the flow. The optimal networks of the two models are selected by comparing performance measures. The results show that both models have the excellent ability to fit the 3D rotating flow field. Performance measures of the second model are better than those of the first one, but its running time is slower than that of the first one. In practice, this method can be used in the design and optimization of stirred tanks, centrifugal pumps, and other machines with rotating parts.