Bubble recognizing and tracking in a plate heat exchanger by using image processing and convolutional neural network

Abstract Water and air are usually employed as a heat exchange medium in cold channels of a plate heat exchanger (PHE), while air, in the state of bubbles in water, has an apparent impact on PHE performance, such as heat exchanging efficiency, flow resistance, etc. However, individual bubble behavior, such as bubble rupturing, merging, colliding, etc., are difficult to detect due to the flow complexity in PHE. Aiming at the problem of exploring individual bubble behavior visually, this study proposes a new method to recognize and track the bubbles in PHE based on a visualization bench for the cold channel of a dimple-type embossing PHE. Firstly, convolutional neural network (CNN) and improved three-frame difference (ITFD) method are used to detect and attain the position and state of the bubble flow in the transparent passage of the PHE from captured videos. Then, the intersection-over-union (IOU) screening algorithm is adopted to optimize the results. Finally, the bubble positions and velocities are calculated. Furthermore, dimensionless parameters such as the local Reynolds number, Weber number, and Froude number are also obtained. The results show that the proposed method could precisely recognize and track individual bubble's spatiotemporal behavior, such as rupturing, merging, and colliding. In the presence of a large number of dense bubbles in the channel of a PHE, this method can achieve an average precision rate of over 94 %, a recall rate of over 87 %, and F1 score of 0.91.