Condensation heat transfer has been widely studied for various applications such as power generation, water desalination, data centers, chemical and pharmaceutical syntheses, heating and air conditioning systems, and especially, the passive containment cooling system (PCCS) in nuclear power plants. The PCCS is designed to reduce the risk resulting from a loss of AC power or compounding human error. In a hypothetical accident situation, one of the main safety systems, the PCCS, condenses water vapor with gravity-driven force to remove the heat from the containment vessel. Although the condensation heat transfer in the presence of non-condensable gases for predicting the heat transfer performance of the PCCS has been successfully investigated, there is still no generalized model or correlation available. In this work, we focused on the development of universal models for predicting the heat transfer coefficients of free-fall condensation heat transfer on the external surfaces of vertical tubes in the presence of non-condensable gases. For this, we created a consolidated database covering a broad range of geometric values and operating conditions, including tube hydraulic diameter D_h = 10–41.2 mm, tube length L = 0.3–3.5 m, total pressure P_tot = 0.15–2.0 MPa, wall subcooling temperature ΔT_sub = 5–71 K, average condensation heat transfer coefficients of 90 ≤ ͞h ≤ 19,400 W/m²K, and Reynolds numbers of the film of 8 ≤ Re_film ≤ 7160. We analyzed the influence of varying the geometric and operational parameter values by using this database. Conventional machine learning techniques such as nonlinear regression and multilayer perceptron (MLP) neural network methods were adopted to predict the condensation heat transfer rate based on the consolidated database. Moreover, the prediction accuracies of the condensation heat transfer rates of the proposed prediction models and 12 relevant correlations were compared by using the consolidated database. The proposed nonlinear regression model exhibited good prediction accuracy with a mean absolute error (MAE) of 12.7 % for average ͞h, which is much lower than those achieved by previously proposed relevant correlations. In addition, the MLP neural network model showed excellent prediction accuracy with an MAE of 4.2 % for the consolidated database.

冷凝传热已被广泛研究用于各种应用，例如发电、海水淡化、数据中心、化学和制药合成、加热和空调系统，尤其是核电站中的被动安全壳冷却系统 (PCCS)。PCCS 旨在降低因交流电源中断或复合人为错误而导致的风险。在假设的事故情况下，主要安全系统之一的 PCCS 在重力驱动下冷凝水蒸气，以从安全壳中带走热量。尽管已经成功研究了在不凝气体存在下用于预测 PCCS 传热性能的冷凝传热，但仍然没有可用的通用模型或相关性。在这项工作中，我们专注于开发通用模型，用于预测在存在不可冷凝气体的情况下垂直管外表面上自由落体冷凝传热的传热系数。为此，我们创建了一个统一数据库，涵盖范围广泛的几何值和操作条件，包括管道水力直径D _h  = 10–41.2 mm，管长L  = 0.3–3.5 m，总压力P _tot  = 0.15–2.0 MPa，壁面过冷温度ΔT _sub  = 5–71 K，平均冷凝传热系数 90 ≤ ͞ h  ≤ 19,400 W /米² 8≤的膜的K，和雷诺数重新_膜 ≤ 7160。我们使用该数据库分析了改变几何和操作参数值的影响。采用传统的机器学习技术，如非线性回归和多层感知器（MLP）神经网络方法，基于统一数据库预测冷凝传热率。此外，通过使用统一数据库比较了所提出的预测模型和12个相关相关性的冷凝传热率的预测精度。所提出的非线性回归模型表现出良好的预测精度，平均绝对误差 (MAE) 为 12.7% 平均͞h，这远低于先前提出的相关相关性所实现的结果。此外，MLP 神经网络模型显示出出色的预测精度，统一数据库的 MAE 为 4.2%。