# Prediction Method of Unsteady Flow Load of Compressor
## 2 Machine Learning Methods  
### Overall Research Framework  
The main idea is shown in Figure 1. Firstly, the range and parameters of the compressor condition disturbance are defined to generate the compressor condition disturbance data matrix. Based on the transient flow excitation analysis model of the compressor stator blade, the flow excitation of the stator blade under the compressor disturbance condition is solved. The transient pulsation pressure of different blade heights on the stator surface was obtained. The FFT method was used to obtain the mean value, amplitude, and other characteristics of the axial and circumferential airflow excitation and establish the sample data set of the working condition disturbance. Figure 1: Overall framework. Two networks are used to predict the physical field and the excitation characteristics, respectively. The detailed network structure is given later. The flow field reconstruction network was used to establish the compressor stator surface pulsation pressure prediction model. The compressor disturbance condition variable was used as the input parameter, and the transient pulsation pressure distribution of the stator blades was used as the output parameter. The convolutional neural networks were selected to build the analysis model of the compressor stator blade's condition disturbance excitation. The flow field information of the compressor stator blade is used as the input parameter, and the mean value and amplitude of airflow excitation of stator blade are used as the output parameters to train the convolutional neural network model. Once the well-trained model was obtained, the flow excitation characteristics of stator blades could be predicted by the disturbance variables in any working condition. In the figure, the gray rectangle represents the numerical calculation steps, while the bottom rectangle represents the neural network. In addition, \"SCNN architecture\" is used later to represent the entire network. The performance prediction network and the field reconstruction network are the two sub-networks that make up the SCNN framework that has been built in this paper, which is depicted in Figure 1. The design variables are the first network's input; the second network's input is flow field information; the latter network's output is aerodynamic performance targets. The field reconstruction progress can be described as follows:  
\[\mathbf{\hat{Y}}_{1}\;=\;\mathbf{\hat{F}}_{1}(\mathbf{X},\mathbf{\Theta}_{1}) \tag{1}\]  
where the loss function of the field reconstruction network is written as:  
\[L_{\mathbf{\hat{Y}}_{1}}\;=\;L(\mathbf{Y}_{1},\mathbf{\widehat{Y}}_{1})=L \big{(}\|\mathbf{Y}_{1}-\mathbf{\hat{F}}_{1}(\mathbf{X},\mathbf{\Theta}_{1}) \|\big{)} \tag{2}\]  
where \(\mathbf{X}\) is the design variables, \(\mathbf{\widehat{Y}}_{1}\) is the predicted flow field, \(\mathbf{Y}_{1}\) is the true flow field, and \(\mathbf{\Theta}_{1}\) is the factors in the network that are to be learned which can be obtained by minimizing \(L_{\mathbf{\hat{Y}}_{1}}\) when it is subjected to:  
\[\mathbf{\Theta}_{1}=\underset{\mathbf{\Theta}_{1}}{\text{argmin}}\Bigg{\{} \sum_{D}\Big{(}L_{\mathbf{\hat{Y}}_{1}}\Big{)}\Bigg{\}} \tag{3}\]  
where \(D\) is the dataset of flow fields obtained by CFD.