Optimizing structure parameters is pivotal in enhancing the convective heat. This study leverages machine learning methods to establish a relationship between input parameters and targets, providing a novel approach to structure parameter optimization in convective heat transfer of a unilateral-heated square channel with inclined ribs. Initially, dimensional analysis is employed to identify structure parameters that influence friction coefficient, Nusselt number, and comprehensive heat transfer characteristic (PEC). A substantial dataset is procured through batch modeling and CFD simulations. The Gaussian process regression is applied to train the data due to its continuity and smoothness. The influence of the rib structure parameters on the flow and heat transfer characteristics is analyzed by CFD simulations and the training results. Finally, the structure parameters corresponding to the optimal Nu and PEC are obtained via the well-trained machine learning model. The optimization results are validated through CFD simulations, yielding the best structure parameters that demonstrate a 7% and 3% increase in Nu and PEC, respectively, which is better than the best results from the numerical data used for training the machine learning model. The heat transfer mechanism and heat transfer effects of the unilateral-heated square channels with inclined ribs are analyzed. This study underscores the potential of machine learning in optimizing convective heat transfer channels, benefiting future research and applications in this field.

中文翻译：

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通过机器学习优化斜肋增强单边加热方形通道的热对流

优化结构参数是增强对流热的关键。本研究利用机器学习方法建立输入参数与目标之间的关系，为斜肋单边加热方形通道对流传热的结构参数优化提供了一种新方法。最初，采用尺寸分析来确定影响摩擦系数、努塞尔数和综合传热特性 (PEC) 的结构参数。通过批量建模和 CFD 模拟获得大量数据集。高斯过程回归因其连续性和平滑性而被应用于训练数据。通过CFD模拟和训练结果分析了肋片结构参数对流动和传热特性的影响。最后，通过训练有素的机器学习模型获得与最优Nu和PEC对应的结构参数。优化结果通过 CFD 模拟进行验证，得出最佳结构参数，Nu 和 PEC 分别增加 7% 和 3%，这优于用于训练机器学习模型的数值数据的最佳结果。分析了单边加热斜肋方形通道的传热机理和传热效果。这项研究强调了机器学习在优化对流换热通道方面的潜力，有利于该领域的未来研究和应用。