## 4 Neural Network Framework Design  
Convolutional neural networks have been proposed since 2012 and have achieved many application results in the fields of image classification and image detection [23; 24]. Figure 4: \(I_{Y}\) field of an ellipse with AR = 4. Figure 5: Pressure field \(O_{pressure}\) of a rectangular section with AR = 4. The multi-layer convolutional structure of CNNs can automatically learn features. Shall-lower convolutional layers can learn some local region features, while deeper convolutional layers have larger receptive fields [25; 26; 27]. These properties allow CNNs to learn more abstract features, and, therefore, it is widely used in image processing problems [28]. The fully convolutional network developed on the basis of CNN is very suitable for solving the problem of mapping from image to image [29]. FCN replaces the last fully connected layer of CNN with a convolutional layer and uses deconvolution to the last convolutional layer. The output result is unsampled, and the size of the output result is gradually restored to the same size as the input image so that a prediction value can be generated for each pixel point. The spatial information of the original input image is also preserved. FCN is good at pixel-level classification problems, and this paper predicts the pressure field or continuous pressure value at each pixel position, which is a regression problem. Although pixel-level regression problems have higher requirements from neural network models than classification problems, they generally require training using generative neural networks. Considering that the pressure field involved in this paper has a gentle gradient and continuous smoothness, the fully convolutional network can be extended from solving pixel-level classification problems to solving pixel-level regression problems. The UNet network proposed by [PERSON] [30] in 2015 can complete end-to-end training with very simple architecture, and the network inference speed is very fast, so a modified UNet network (Figure 6) is adopted to solve the pixel-level regression problem in this paper. Figure 6 shows the image width, height, and number of channels after convolution, pooling, and feature fusion. D represents the number of channels, S represents the number of convolutions, and N represents the number of poolings. These parameters will be studied in Chapter 6. Meanwhile, the deep learning network structure will be determined. In order to ensure that the pressure data in the aerodynamic shape is 0, a mask layer consisting of 0 and 1, with a bluff body section shape, is passed when the prediction result is output. Next, the innovative design of the UNet structure and loss value is explained. First, the deep learning model needs to match the input and output data sizes. The input involved in this paper is the wall distance field (\(I_{Dist}\)) and the spatial coordinate field (\(I_{X}\), \(I_{Y}\)), and the number of channels is 3. Considering the information content and training time contained in the image, the image pixels are set to 64 \(\times\) 64. The output is a pressure field with the same size as the input image, which is a single channel picture. Since the  
Figure 6: Modified UNet network structure. regression problem of continuous variables needs to be predicted, it is necessary to modify the output layer of the UNet and remove the Softmax layer. After modifying the input and output layers, the hidden layer details of the network structure are also required. The network architecture used in this paper is based on the transformation of the UNet and also consists of three parts: downsampling, upsampling, and skipping structure. The downsampling part includes the two basic components of the convolution layer and pooling layer, and the feature extraction of the input is realized by repeating the convolution-pooling process many times. The convolutional layer uses a 3 \(\times\) 3 small convolution kernel, and the padding size is 1. Using max pooling makes the image twice as small after each pooling.