WAKE RECOGNITION OF A BLUNT BODY BASED ON CONVOLUTIONAL NEURAL NETWORK

Wake structures of different blunt bodies with identical characteristic length are similar, this is quite challenging to be distinguished using solely human eyes. Here, we propose a blunt body wake recognition method based on the convolutional neural network (CNN), which is then verified to be highly accurate with various types of blunt bodies models in vertical soap-film water tunnel experiments. The experimental platform is composed of a self-built vertical soap-film device, three typical blunt body models (square cylinder, circular cylinder, and triangle cylinder), and an image acquisition system. Based on the optical interference method, this image processing modulus can realize continuous high-fidelity photography of blunt body wakes with different incoming velocities. The CNN recognition model is built up with input layer, convolutional layer, pooling layer, fully-connected layer, and classification layer. Among them, the convolutional layer and the pooling layer are used to extract the deep feature information of wakes, while the fully-connected layer and the classification layer together can finally determine the category or Reynolds numbers of the input wake image. By importing a data set with 9000 wake images into the CNN model, a wake feature recognition model capable of classifying various body shapes is established in a data-driven manner. Results show that the shape recognition accuracy is 97.6% at the same Reynolds number (300 wake images), and 96% at different Reynolds numbers (1200 wake images). Even when wake images with different shapes and Reynolds numbers are mixed together, the recognition accuracy in terms of both shape and Reynolds number can still reach 91% (1500 mixed wake images). The proposed method provides a solid reference for future applications of artificial intelligence in extracting physical information from blunt body wakes.