ConvLSTM Based Spatiotemporal Prediction of Aircraft Engine Jet Flow
To investigate the extent to which the engine jet of a takeoff aircraft affects the rear passing air-craft,a prediction model of aircraft engine jets based on Convolutional Long Short-Term Memory Network(ConvLSTM)was proposed to predict the flow field data at a certain time period in the future.Aircraft en-gine jet data were acquired and pre-processedby lidar.The temporal and spatial structure characteristics of jet flow of aircraft engines are captured by temporal and spatial sub networks.By fusing spatial-temporal features and using full junction layer to output future flow field data,a convolved short-and long-term memory network is constructed for future frame prediction of aircraft engine complex jet data.The results show that the ConvLSTM model can accurately predict the spatial-temporal distribution of jet flow in air-craft engines,and the experimental results of RMSE 12.28 and MAE 9.26 are obtained.Compared with the traditional neural network model,the prediction results have more stable RMSE values and prediction accu-racy,which effectively improve the quality and accuracy of the spatial-temporal prediction of jet flow,provi-ding support for the study of jet flow influence ranges in aircraft engines.
万方数据
维普
