STUDY ON FLOW FIELD PARAMETERS OF WAKE TIME HISTORY TARGET RECOGNITION

Wall immersed in fluid will form highly complex wake flow with specific features. Therefore, the extraction and analysis of flow feature has important research value. However, in the case of high Reynolds number, the wake flow field are complex, so it is difficult to identify and extract the flow features by traditional mathematical and statistical method. In this paper, a new flow field feature extraction and analysis method based on deep learning of wake time history data is proposed, and the shape recognition based on local time history is realized; At the same time, accuracy of different time history parameter is analyzed, and the optimal physical parameters suitable for target recognition are obtained. Research results on the flow field data of cylinder and square cylinder show that the model based on convolution neural network proposed in this paper has good training convergence and high prediction accuracy, and model using transverse velocity time history has highest accuracy. At the same time, it is proved that method proposed in this paper is a new high-precision method for target recognition immersed in fluid.