Heat Convection Enhancement of Unilateral-Heated Square Channels by Inclined Ribs Optimization with Machine Learning

IF 1.5 4区 工程技术 Q3 ENGINEERING, MECHANICAL
Xiangyu Wang, Xiang-Hua XU, Xingang Liang
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Abstract

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.
利用机器学习优化斜肋加强单侧加热方形通道的热对流
优化结构参数是提高对流热量的关键。本研究利用机器学习方法建立了输入参数与目标之间的关系,为带有倾斜肋片的单侧加热方形通道对流传热中的结构参数优化提供了一种新方法。首先,采用尺寸分析来确定影响摩擦系数、努塞尔特数和综合传热特性(PEC)的结构参数。通过批量建模和 CFD 模拟获得了大量数据集。由于数据具有连续性和平滑性,因此采用高斯过程回归来训练数据。通过 CFD 模拟和训练结果分析了肋骨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了与最佳 Nu 和 PEC 相对应的结构参数。通过 CFD 模拟验证了优化结果,得出的最佳结构参数表明 Nu 和 PEC 分别增加了 7% 和 3%,优于用于训练机器学习模型的数值数据的最佳结果。研究分析了带有倾斜肋片的单侧加热方形通道的传热机制和传热效果。这项研究强调了机器学习在优化对流传热通道方面的潜力,有利于该领域未来的研究和应用。
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来源期刊
Journal of Enhanced Heat Transfer
Journal of Enhanced Heat Transfer 工程技术-工程:机械
CiteScore
3.60
自引率
8.70%
发文量
51
审稿时长
12 months
期刊介绍: The Journal of Enhanced Heat Transfer will consider a wide range of scholarly papers related to the subject of "enhanced heat and mass transfer" in natural and forced convection of liquids and gases, boiling, condensation, radiative heat transfer. Areas of interest include: ■Specially configured surface geometries, electric or magnetic fields, and fluid additives - all aimed at enhancing heat transfer rates. Papers may include theoretical modeling, experimental techniques, experimental data, and/or application of enhanced heat transfer technology. ■The general topic of "high performance" heat transfer concepts or systems is also encouraged.
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