Machine Learning-Based Correlation Framework for Flow Condensation in Horizontal Channels

doi:10.1080/01457632.2025.2590952

2025-11-01

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DOI：10.1080/01457632.2025.2590952

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摘要

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本研究提出利用机器学习算法的优势，开发适用于水平光滑通道的流动冷凝模型。采用包含27种纯制冷剂的6,532个数据样本的多样化数据库，用于建立所提出的冷凝传热系数模型。基于智能设计的数学方法，将9种机器学习算法应用于该大型数据库，以提取建模对流冷凝传热系数最有利的无量纲参数。在训练好的机器学习模型中，极端梯度提升（Extreme Gradient Boosting）和深度神经网络算法提供了最佳估算结果，其决定系数（coefficient of determination）值分别为0.9874和0.9822。极端梯度提升算法（该算法产生最准确的估算结果）所提取的最具影响力的无量纲数，被用于开发流动冷凝的新型传热系数模型。该模型对应的平均绝对误差（mean absolute error）为15.92%，平均相对误差（mean relative error）为-2.38%，其预测精度显著优于文献中关于对流流动冷凝的研究结果。此外，基于排除的数据库，分析了所提出关联式预测结果的合理性，并成功验证了传热模型在数据外推（extrapolating）实验数据方面的优越性。

Keyword:

HEAT-TRANSFER CHARACTERISTICS

PRESSURE-DROP CHARACTERISTICS

IN-TUBE CONDENSATION

TRANSFER COEFFICIENT

MICROFIN-TUBE

SMOOTH TUBE

PART II

HYDROCARBON REFRIGERANTS

GENERAL CORRELATION

FILM CONDENSATION

期刊

Heat Transfer Engineering

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1.6

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4.4K

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adnan menderes university

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146

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yildiz technical university

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540

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Izmir Democracy University

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247

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sirnak university

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168

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