Condensation on surfaces in the presence of non-condensable gas (NCG) is a ubiquitous and critical phenomenon in many industrial fields. However, current empirical correlations for predicting condensation heat transfer coefficients in the presence of NCG may exhibit significant deviations from reality. Machine learning algorithms are now being dedicated to this field, but the models developed can only predict the condensation heat transfer coefficients of steam or vapor of a non-aqueous working fluid with similar physical properties to water in the presence of NCG. In the present study, a comprehensive theoretical analysis was conducted to investigate the total condensation heat transfer coefficients with NCG, and 16 dimensionless numbers were identified as input variables for machine learning models. Based on a filtered database consisting of 4377 data points extracted from 37 papers, the Spearman correlation coefficients were calculated to evaluate the relationship between the total heat transfer coefficients and the input variables, indicating the magnitude of the impact of the 16 dimensionless variables. Four machine learning models, namely Gradient Boosting Regression (GBR), Extreme Gradient Boosting (XGBoost), Random Forest Regression (RFR), and Multilayer Perceptron (MLP), were developed to predict the total heat transfer coefficients for water and non-aqueous working fluids. The mean absolute percentage errors for the four models were 1.38%, 1.63%, 3.00%, and 4.42%, respectively, with the GBR model exhibiting the highest degree of accuracy. The determination of the application scope of these models was conducted by analyzing the value ranges for each dimensionless parameter and its corresponding frequency distribution.

中文翻译：

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用于预测存在不凝性气体时的凝结传热系数的机器学习算法

在存在不凝性气体 (NCG) 的情况下，表面凝结是许多工业领域中普遍存在的关键现象。然而，当前在 NCG 存在的情况下预测冷凝传热系数的经验相关性可能与现实存在显着偏差。机器学习算法现在致力于该领域，但开发的模型只能预测在 NCG 存在下物理性质与水相似的非水工作流体的蒸汽或蒸气的冷凝传热系数。在本研究中，进行了全面的理论分析，以研究 NCG 的总冷凝传热系数，并确定了 16 个无量纲数作为机器学习模型的输入变量。基于从 37 篇论文中提取的 4377 个数据点组成的过滤数据库，计算 Spearman 相关系数来评估总传热系数与输入变量之间的关系，表明 16 个无量纲变量的影响大小。开发了四种机器学习模型，即梯度提升回归（GBR）、极限梯度提升（XGBoost）、随机森林回归（RFR）和多层感知器（MLP）来预测水和非水工作的总传热系数液体。四个模型的平均绝对百分比误差分别为 1.38%、1.63%、3.00% 和 4.42%，其中 GBR 模型的准确度最高。通过分析各无量纲参数的取值范围及其对应的频率分布来确定这些模型的适用范围。