# Prediction Method of Unsteady Flow Load of Compressor
## 4 Results and Discussion
### Results of Flow Field Reconstruction  
The pressure is the target variable of the flow field reconstruction; the prediction of the pressure is completed at the same time when the model is training. Figure 7 gives the information about the changes with the iteration steps of the loss function in the training process. After several training and testing phases in the network model, the batch size of 32 with a considerable level of accuracy is finally selected. According to the results in the previous section, the training set sample proportion here is defined as 0.3.  
Figure 7: Change trend of model loss function ((**a**) Flow field reconstruction model; (**b**) load prediction model).  
It is not difficult to find from the figure that at the beginning, the loss function of both of the network structures decreases very rapidly. While the step of the iteration exceeds 200, the loss function of flow field reconstruction model reaches a small value as the learning rate further decreases. It is \(10^{-5}\) in the training set, and it is \(10^{-4}\) in the testing set. Similarly, by observing the training process of the network structure of the incentive prediction, it can be clearly observed that the loss function has the same downward trend. In a word, the training process of network model is complete according to the characteristics of loss function.  
Figures 8-11 show the real pressure field and the predicted pressure field of 0.1, 0.4, 0.6, and 0.9 stator heights for a specific instance in the test set at different times, respectively. The stationary blade bears the impact of the wake flow of the upper swimming blade, and periodic airflow excitation appears on the blade surface under the unsteady calculation. It can be clearly seen that the surface pressure of different blade height sections undergoes an obvious change at different calculation times. Moreover, the surface trend is basically the same at 0T and T, while the difference is the largest at 0.5T. This corresponds to a complete cycle of airflow excitation.  
Figure 10: Unsteady pressure on stator surface at 60% span. (a) t = 0. (b) t = 1/4T. (c) t = 1/2T. (d) t = 3/4T. (e) t = T.  
Figure 9: Unsteady pressure on stator surface at 40% span. (a) t = 0. (b) t = 1/4T. (c) t = 1/2T. (d) t = 3/4T. (e) t = T.  
At the same time, it can be seen that the airflow excitation level of the stator at several blade heights is also different, which is reflected in the curve area surrounded by the pressure surface and suction surface. Due to the complex characteristics of the internal flow field of the compressor, the pressure pulsation values at different heights are inconsistent. By comparing all of the pictures, it can be observed that the unsteady prediction data of each section at different times are very close to the numerical curve that was calculated by the CFD. It can be proven that the network performance of flow prediction reaches a high accuracy level. On the other hand, the pressure values at LE and TE of the stator blade changed sharply, which is clearly reflected and accurately predicted in the figure. The results showed that the influence of the wake on the leaves was also clearly reflected. Therefore, the flow field reconstruction network is also very effective in capturing the details of the compressor flow field. In conclusion, this method is very reasonable and reliable for a transient pressure evaluation. Because the two subnetworks are combined, the high prediction accuracy of this network also lays a foundation for the prediction of the airflow excitation.