# Prediction of Boiling Heat Transfer Coefficient for Micro-Fin Using Mini-Channel
## 1 Introduction  
### Motivation to Predict Heat Transfer Coefficients for Micro-Fins  
Heat control systems utilizing boiling two-phase flow are employed in cooling power generation facilities and electronic devices. Additionally, two-phase flow boiling technology is being considered in space applications such as communication satellites and lunar exploration. Heat control techniques using boiling two-phase flow leverage the latent heat of vaporization during the phase change of the liquid and provide a heat transfer performance that is generally two orders of magnitude higher than systems that transport heat using only liquid. This results in smaller temperature increases, contributing to the miniaturization and energy efficiency of the system. Moreover, in renewable energy and energy saving, high performance and miniaturization of heat exchangers are recognized as critical efforts to improve heat pumps' efficiency and enhance the use of renewable energy for achieving carbon neutrality. Heat pump technology uses a compressor and exchanges heat with the external environment through a heat exchanger. This technology can yield four or five times as much heating or cooling rate as the electrical power input to the compressor. A heat pump consists of several components: the compressor, expansion valve, and heat exchanger. Among these, the performance of the heat exchanger, which exchanges heat with the external environment, is particularly critical. The requirements for improving the performance of heat exchangers include (1) enhancement of heat transfer performance and (2) downsizing of the heat exchanger. To meet these needs, research is being conducted on methods such as mini-channel technology, which involves reducing the diameter of the tubes, and micro-fin technology, which involves fine grooving. Predicting the heat transfer coefficient of two-phase flow in the boiling state is particularly important in downsizing heat exchangers. Consequently, many studies have reported the heat transfer coefficients of boiling two-phase flow. These studies categorized the two-phase state into representative flow patterns to predict appropriate heat transfer coefficients. However, predicting boiling heat transfer is challenging due to several factors. The mixture of gas and liquid with different properties undergoes a phase change while flowing, requiring predictions to be made according to the flow state. Additionally, phenomena such as post-dryout, where the heat transfer coefficient changes abruptly under high-quality conditions, complicate accurate predictions of heat transfer coefficients. In the post-dryout, the heat transfer coefficient decreases significantly, causing a rapid increase in the temperature of the heat exchanger. Therefore, predicting dryout quality is crucial in design. Numerous experimental verifications are necessary to predict these characteristics accurately. Furthermore, it has been found that these phenomena depend on the properties of the refrigerant and the operating conditions. Therefore, appropriate design and efficient operation require designers to accurately estimate boiling heat transfer coefficient. This presents a significant challenge in development. Thus, our research group has investigated prediction techniques for boiling heat transfer coefficients using deep learning to address these issues. Prediction methods utilizing artificial intelligence consider the heat transfer coefficients obtained under multiple conditions as big data, offering a promising alternative to traditional methods. Focusing on mini-channels, we examined the prediction of heat transfer coefficients using deep learning for different refrigerants and various operating conditions. Our results demonstrated that this approach achieves higher accuracy than existing studies. Moreover, while conventional deep learning predictions typically cannot evaluate the uncertainty of the obtained results, [PERSON] et al. propose a novel method that combines deep neural networks (DNN) and Gaussian Process Regression (GPR).