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.

水和空气通常在板式换热器（PHE）的冷通道中用作热交换介质，而空气在水中呈气泡状态时会对PHE性能产生明显影响，例如热交换效率，流阻然而，由于PHE中的流动复杂性，难以检测诸如气泡破裂，合并，碰撞等的单个气泡行为。针对视觉上观察单个气泡行为的问题，本研究提出了一种基于酒窝型压印PHE冷通道可视化平台的识别和跟踪PHE中气泡的新方法。首先，卷积神经网络（CNN）和改进的三帧差（ITFD）方法用于从捕获的视频中检测并获得PHE透明通道中气泡流动的位置和状态。然后，采用联合交叉（IOU）筛选算法来优化结果。最后，计算气泡的位置和速度。此外，还获得了无量纲参数，例如本地雷诺数，韦伯数和弗洛德数。结果表明，该方法能够准确识别和跟踪单个气泡的时空行为，例如破裂，合并和碰撞。在PHE通道中存在大量密集气泡的情况下，此方法可以实现94％以上的平均准确率，87％以上的召回率，以及 采用联合交叉（IOU）筛选算法来优化结果。最后，计算气泡的位置和速度。此外，还获得了无量纲参数，例如本地雷诺数，韦伯数和弗洛德数。结果表明，该方法能够准确识别和跟踪单个气泡的时空行为，例如破裂，合并和碰撞。在PHE通道中存在大量致密气泡的情况下，此方法可以实现94％以上的平均准确率，87％以上的召回率，以及 采用联合交叉（IOU）筛选算法来优化结果。最后，计算气泡的位置和速度。此外，还获得了无量纲参数，例如本地雷诺数，韦伯数和弗洛德数。结果表明，该方法能够准确识别和跟踪单个气泡的时空行为，例如破裂，合并和碰撞。在PHE通道中存在大量致密气泡的情况下，此方法可以实现94％以上的平均准确率，87％以上的召回率，以及 和弗洛德数也被获得。结果表明，该方法能够准确识别和跟踪单个气泡的时空行为，例如破裂，合并和碰撞。在PHE通道中存在大量致密气泡的情况下，此方法可以实现94％以上的平均准确率，87％以上的召回率，以及 和弗洛德数也被获得。结果表明，该方法能够准确识别和跟踪单个气泡的时空行为，例如破裂，合并和碰撞。在PHE通道中存在大量致密气泡的情况下，此方法可以实现94％以上的平均准确率，87％以上的召回率，以及F 1得分0.91。