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首页> 外文期刊>International Journal of Heat and Mass Transfer >Machine learning predictions of critical heat fluxes for pillar-modified surfaces
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Machine learning predictions of critical heat fluxes for pillar-modified surfaces

机译：机器学习预测柱改性表面的临界热量

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摘要

For convection research, one important topic is the maximum heat flux achieved on a boiling surface, known as the critical heat flux (CHF). This phenomenon is characterized by the formation of a blanket of heat-blocking vapor on the surface. Over several decades, numerous surface structures have been fabricated to enhance the CHF for various high-power cooling applications. However, the complexity of the surface structures and many other factors (e.g., capillary wicking flux) restrict the prediction of the CHF using theoretical models. In this work, three popular machine learning (ML) methods are employed to analyze and further predict the CHFs for a given surface modified with micro-structures. Among these, the random forest regression method consistently produced the best fitting models of previously published data. The importance analysis algorithm developed for random forest models facilitated efficient discovery of the most important descriptors predicting the CHF. One key descriptor used in these models was the mean beam length (MBL), a terminology borrowed from radiative heat transfer, which effectively described the characteristic spacing between adjacent surface features. The models showed greatest sensitivity to the MBL and the height of the features, compared to the other surface descriptors.
机译：对于对流研究，一个重要的主题是在沸腾表面上实现的最大热量通量，称为临界热通量（CHF）。这种现象的特征在于形成表面上的覆盖蒸气覆盖。多十年来，已经制造了许多表面结构，以增强各种高功率冷却应用的CHF。然而，表面结构的复杂性和许多其他因素（例如，毛细管芯筒磁通量）限制了使用理论模型的CHF的预测。在这项工作中，使用三种流行的机器学习（ML）方法来分析并进一步预测与微结构改性给定表面的CHF。其中，随机森林回归方法一致地生产出先前公布数据的最佳拟合模型。为随机林模型开发的重要性分析算法促进了预测CHF最重要描述符的有效发现。这些模型中使用的一个关键描述符是平均光束长度（MBL），从辐射传热借用的术语，这有效地描述了相邻表面特征之间的特性间隔。与其他表面描述符相比，该模型对MBL和特征的高度显示出最大的敏感性。

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