# Prediction Method of Unsteady Flow Load of Compressor
## 5 Conclusions  
As far as this study is concerned, the deep learning method is used to carry out the excitation prediction of the compressor stator unsteady flow field. Two network structures are established to connect the compressor operating parameters with the blade flow field parameters and the excitation characteristics. In addition, the aerodynamic characteristics of the compressor under complex working conditions are obtained by an unsteady calculation. A large amount of CFD data were used to train the network model, and good training results have been obtained. Finally, an effective prediction model was formed to analyze the unsteady flow field and the flow force parameters of the compressor stator blades under complex working conditions. The research in this paper contributes a simple and reliable method to the analysis of the compressor blade load under complex working conditions. The main conclusions are as follows:  
Figure 15: First order amplitude of axial flow force ((**a**) Predicted value and true value; (**b**) relative error density distribution). 1. This paper compares the influence of different training set sizes on the network training effect. In both the flow field prediction model and the blade airflow excitation parameter prediction model, the relative error between the model results and the actual values decreases gradually when the training set size increases gradually. The best training set size is 0.3. In this case, both the flow field and the excitation prediction loss function are reduced to below \(10^{-4}\). At this time, the relative errors of the model forecasts have been reduced to a considerable level. If the training accuracy is increased, overfitting will occur. It can be seen that the SCNN network model that is presented in this paper can use a small number of CFD data to obtain good training effects and achieve accurate parameter predictions. 2. A test sample was selected for the study, and the prediction effects for the transient pressure on the stator's different sections at 0.1, 0.4, 0.6, and 0.9 blade heights were compared and analyzed in detail. It can be found from the results that the flow prediction network has a very good prediction effect on the unsteady pressure. The actual value that was calculated by the CFD is very close to the predicted result of the network model at each time point or blade section. This method can also reliably obtain the flow parameter details of the LE and TE of the stator blade. At the same time, very obvious periodic information of the pressure fluctuations can be obtained by solving an unsteady calculation. It shows that the network that is presented in this paper is accurate and reliable for the prediction of the blade's transient parameters. 3. In terms of the compressor flow excitation prediction under complex working conditions, it can be seen that the network model that was trained with 1000 working condition disturbance samples has achieved good results. By comparison, it can be found that the relative errors of both the mean value and the first-order amplitude of the axial and tangential blade airflow loads are very small. Most of the data points were within 2% of the error. Moreover, the predictions of various parameters are in good agreement with the normal distribution. This method of the blade load analysis is efficient and accurate, and it has a good reference value for the blade vibration analysis. The research of this paper has more scope for further expansion. On the one hand, the airflow load data are combined with the finite element method, and the network model of the blade vibration response and the operating condition disturbance can be analyzed. On the other hand, the excitation characteristics of the compressor rotor blades are more complex, and the method that is proposed in this paper can also be used to predict the load under complex working conditions. Each author has contributed different work in this study. [PERSON] is mainly responsible for data curation, methodology and writing--original draft.