# Prediction Method of Unsteady Flow Load of Compressor
## 4 Results and Discussion
### Comparison of Performance Prediction  
In view of the characteristics of the airflow excitation of the compressor stationary blades, we mainly compared and analyzed the mean and amplitude of the airflow force. The average tangential airflow force data results for all of the validation sets are presented in Figure 12. The excitation prediction model can be used to obtain the average tangential airflow force of the blade under each working condition sample. Additionally, this part of data can be compared and analyzed with the numerical simulation results. The distribution of the predicted values and the actual values as well as the relative error of distribution density can be clearly obtained from the figure. Figure 11: Unsteady pressure on stator surface at 90% span. (a) t = 0. (b) t = 1/4T. (c) t = 1/2T. (d) t = 3/4T. (e) t = T.  
It is easy to observe that the prediction error of all of the test sample points is very small. The predicted and actual values of the average tangential load of the blade are in good agreement with y = x ray. All of the points are within the error range of 2%, without there being any exceptions. Additionally, the trend of the distribution density is consistent with the characteristics of the normal distribution. This result reveals the sufficiency of the network training and the accuracy of the model in predicting the average tangential force. In addition to the mean value of the excitation force, the amplitude of each order of the airflow force load is also an important research parameter for blade vibration. The FFT treatment in this paper ignores the higher-order terms and retains the first-order airflow force amplitude characteristics of the blades under each working condition. For the validation set data, the real value and the predicted value of the first-order amplitude of the tangential airflow force are shown in detail in Figure 13. It is not difficult to find that the tangential airflow force amplitude of the blade has a large variation range due to the disturbance of the operating conditions. This further illustrates the complexity and necessity of studying the blade load under the condition of disturbance. It can be found that the data points of each test set are also very concentrated around y = x. Very few outliers fall outside the 2% error band. Therefore, it is reliable to apply this model to the prediction of airflow excitation with the first-order amplitude of the tangential flow force. Through the above method, the load characteristics of compressor blades can be predicted quickly and accurately. Figure 12: Mean tangential flow force (**(a)** Predicted value and true value; **(b)** relative error density distribution). A similar analysis can be performed for the axial flow force of the blade. Here, the model results and the calculated values of the axial force load mean of the compressor stator blade are also shown in Figure 14. It is not difficult to find that the model still achieves a quite satisfactory prediction accuracy. The data points in each validation set fall well within the 2% error band and fit tightly around y = x. In addition, the distribution density of the graph also corresponds to the characteristics of normal distribution. Additionally, it can be found that the mean value of the axial force is smaller than the tangential force is. The same conclusion can be obtained from the results of the first-order axial flow force, as shown in Figure 15. With the exception of a few points that fell outside the 2% margin of error, almost all of the predictions were very accurate. This is because the prediction parameters themselves are small so the error fluctuations that are caused by the disturbance are large. In conclusion, this network model constructed that was In this research could be used to effectively predict the aerodynamic load of the compressor blades. Figure 13: First order amplitude of tangential flow force ((**a**) Predicted value and true value; (**b**) relative error density distribution).