标题： 核态池沸腾的实验建模与微通道结构表面的混合机器学习框架 显示英文标题

标题： Empirical modeling and hybrid machine learning framework for nucleate pool boiling on microchannel structured surfaces

摘要： 微结构表面除了增加传热表面积外，还影响成核特性及气泡动力学，从而实现高效的核态沸腾传热。在各种应用中，对不同条件下这些表面的池沸腾传热特性进行建模是至关重要的。由于现有相关公式受其准确性和狭窄操作范围的限制，自以前的研究中收集的数据提出了一种新的微通道结构表面核态沸腾的经验关联式。本研究还检查了各种机器学习（ML）算法和深度神经网络（DNN）在微通道结构表面数据集上预测核态池沸腾传热系数（HTC）的能力。为了整合ML和领域知识，提出了一种物理信息机器学习辅助框架（PIMLAF）。本研究提出的关联式被用作PIMLAF的先验物理模型，而DNN被用来模拟先验模型的残差。与其它ML模型和DNN相比，这种混合框架表现出最佳性能。由于所提出的关联式提供了沸腾行为的基础知识，该框架能够很好地泛化到不同的数据集。此外，SHAP解释分析确定了影响模型预测的关键参数及其对HTC预测的影响。此分析进一步使模型更加稳健和可靠。关键词：池沸腾，微通道，传热系数，关联分析，机器学习，深度神经网络，物理信息机器学习辅助框架，SHAP分析 显示英文摘要

摘要： Micro-structured surfaces influence nucleation characteristics and bubble dynamics besides increasing the heat transfer surface area, thus enabling efficient nucleate boiling heat transfer. Modeling the pool boiling heat transfer characteristics of these surfaces under varied conditions is essential in diverse applications. A new empirical correlation for nucleate boiling on microchannel structured surfaces has been proposed with the data collected from various experiments in previous studies since the existing correlations are limited by their accuracy and narrow operating ranges. This study also examines various Machine Learning (ML) algorithms and Deep Neural Networks (DNN) on the microchannel structured surfaces dataset to predict the nucleate pool boiling Heat Transfer Coefficient (HTC). With the aim to integrate both the ML and domain knowledge, a Physics-Informed Machine Learning Aided Framework (PIMLAF) is proposed. The proposed correlation in this study is employed as the prior physics-based model for PIMLAF, and a DNN is employed to model the residuals of the prior model. This hybrid framework achieved the best performance in comparison to the other ML models and DNNs. This framework is able to generalize well for different datasets because the proposed correlation provides the baseline knowledge of the boiling behavior. Also, SHAP interpretation analysis identifies the critical parameters impacting the model predictions and their effect on HTC prediction. This analysis further makes the model more robust and reliable. Keywords: Pool boiling, Microchannels, Heat transfer coefficient, Correlation analysis, Machine learning, Deep neural network, Physics-informed machine learning aided framework, SHAP analysis