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

翻译：微结构表面不仅增加了传热表面积，还影响了成核特性和气泡动力学，从而实现高效的核态沸腾传热。在不同应用场景中，对这些表面在各种条件下的池沸腾传热特性进行建模至关重要。由于现有关联式的精度受限且适用范围较窄，本研究基于前人研究中收集的多组实验数据，提出了适用于微通道结构表面核态沸腾的新经验关联式。本研究还考察了多种机器学习算法和深度神经网络在微通道结构表面数据集上对核态池沸腾传热系数的预测性能。为融合机器学习与领域知识，提出了物理信息机器学习辅助框架。该框架将本研究提出的经验关联式作为先验物理模型，并采用深度神经网络对先验模型的残差进行建模。与其他机器学习模型和深度神经网络相比，该混合框架取得了最佳性能。由于所提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至不同数据集。此外，SHAP解释分析识别出影响模型预测的关键参数及其对传热系数预测的作用，进一步增强了模型的鲁棒性与可靠性。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