Prediction of Fluid Accumulation in Engine Disk Cavity Based on Encoder-Decoder

A neural network model based on the encoder and decoder is proposed for aero-engine fluid accumulation. The model uses the attention mechanism to adjust the output weight of the encoder and then uses the decoder to predict the fluid volume. The vibration eigenvalues were obtained by fast Fourier transform of the original vibration signals, and the eigenvalues of vibration data were used as the input model to predict the volume of fluid in the disk cavity. To evaluate the effectiveness of the model, an aero-engine disk cavity fluid simulation experiment was built, and the data obtained from the experiment were used to test the method and the model. The prediction accuracy of the proposed model for the volume of engine disk cavity fluid was over 95%, which has an important engineering value for the reliable operation of an aero-engine.