A numerical study was carried out to analyze the condensation phenomena in a vertical channel between two flat plates. To solve the three-dimensional governing equations including conservation of the mass, momentum, and energy, the Volume of Fluid model was adopted. For the phase change phenomena, the Lee model was used. The tuning for the model parameter was performed using existing experimental data. To obtain the heat transfer coefficients under desired conditions, a model based on machine learning using artificial neural network was developed, which shows good accuracy and the future possibility of using the model for the tuning and validation of the phase-change model. A super-hydrophilic (10°), hydrophilic (58°), and hydrophobic surface (130°) were considered together with the variation of the gap spacing and the inlet velocity. The different condensation behavior according to the various surfaces wettability conditions was clearly identified. The formation of a liquid bridge in the channel for the dropwise condensation was observed, and its effects on the thermal and pressure drop performance were assessed. Details on the its performance were summarized quantitatively. It is expected that the present result and numerical method could provide useful information for the fin-type heat exchanger design.

进行了数值研究来分析两个平板之间的垂直通道中的冷凝现象。为了求解包括质量守恒、动量守恒和能量守恒在内的三维控制方程，采用流体体积模型。对于相变现象，使用李模型。模型参数的调整是使用现有的实验数据进行的。为了获得所需条件下的传热系数，开发了一种基于机器学习的人工神经网络模型，该模型具有良好的准确性以及未来将该模型用于相变模型的调整和验证的可能性。考虑了超亲水（10°）、亲水（58°）和疏水表面（130°）以及间隙间距和入口速度的变化。清楚地确定了根据各种表面润湿性条件的不同冷凝行为。观察了在滴状冷凝通道中形成的液桥，并评估了其对热和压降性能的影响。对其性能的细节进行了定量总结。预计目前的结果和数值方法可以为翅片式换热器的设计提供有用的信息。