Identifying two-phase flow patterns is fundamental to successfully design and subsequently optimize high-precision heat transfer equipment, given that the heat transfer efficiency and pressure gradients occurring in such thermo-hydraulic systems are dependent on the flow structure of the working fluid. This paper shows that with visualization data and artificial neural networks, the flow pattern images of condensation of R-134a refrigerant in inclined smooth tubes can be classified with more than 98% accuracy. The study considers 10 classes of flow pattern images acquired from previous experimental works for a wide range of flow conditions and the full range of tube inclination angles. Although not the focus of this paper, the use of a Principal Component Analysis allowed feature dimensionality reduction, dataset visualization, and decreased associated computational cost when used together with multilayer perceptron neural networks. In addition, the superior two-dimensional spatial learning capability of convolutional neural networks allowed improved image classification and generalization performance. In both cases, the classification was performed sufficiently fast to enable real-time implementation in two-phase flow systems.

确定两相流动模式是成功设计并随后优化高精度传热设备的基础，因为在此类热液压系统中发生的传热效率和压力梯度取决于工作流体的流动结构。本文表明，利用可视化数据和人工神经网络，R-134a 制冷剂在倾斜光滑管中冷凝的流型图像分类准确率可达 98% 以上。该研究考虑了从以前的实验工作中获得的 10 类流型图像，这些图像适用于各种流动条件和全范围的管倾角。虽然不是本文的重点，但使用主成分分析允许特征降维、数据集可视化、与多层感知器神经网络一起使用时，降低了相关的计算成本。此外，卷积神经网络卓越的二维空间学习能力提高了图像分类和泛化性能。在这两种情况下，分类执行得足够快，可以在两相流系统中实时实施。