This paper explores machine learning approach as a heat transfer correlation. Machine learning significantly reduces the effort to develop multi-variable heat transfer correlations, and is capable of readily expanding the parameter domain. Random forests algorithm is used to predict the convection heat transfer coefficients for a high-order nonlinear heat transfer problem, i.e., convection in a cooling channel integrated with variable rib roughness. For 243 different rib array geometries, numerical simulations are performed to train and test ML model based on six input features. Machine learning model predicts closely to numerical simulation data with high determination of coefficient (R), e.g., R > 0.966 for the testing dataset. The capability and limitation of random forests algorithm are discussed with validation dataset.

中文翻译：

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传热相关性的机器学习


本文探讨了作为传热相关性的机器学习方法。机器学习显着减少了开发多变量传热相关性的工作量，并且能够轻松扩展参数域。随机森林算法用于预测高阶非线性传热问题的对流传热系数，即与可变肋粗糙度集成的冷却通道中的对流。对于 243 种不同的肋阵列几何形状，基于六个输入特征进行数值模拟来训练和测试 ML 模型。机器学习模型的预测与具有高确定系数 (R) 的数值模拟数据非常接近，例如测试数据集的 R > 0.966。通过验证数据集讨论了随机森林算法的能力和局限性。