# Prediction of Boiling Heat Transfer Coefficient for Micro-Fin Using Mini-Channel
## 6 Conclusions  
This study developed a deep learning model that achieves high prediction accuracy for the heat transfer coefficient of micro-fins, including post-dryout, using a relatively small amount of data. The proposed approach involves conducting pre-training based on data from mini-channels, which are highly relevant to the heat transfer coefficient of micro-fins and fine-tuning the pre-trained model using the target data. Furthermore, we added dimensionless numbers assumed to significantly influence heat transfer coefficients \begin{table}
\begin{tabular}{c c c c} \hline \hline  & **DNN, Case 1** & **DNN + GPR, Case 2** & **Fine-Tuning and Consideration of Physical Mechanisms, Case 3 (Present)** \\ \hline _SD_ & 23.6 & 25.0 & 24.1 \\ \hline _R20_ & 89.4 & 92.9 & 93.8 \\ \hline _R30_ & 94.2 & 95.9 & 96.3 \\ \hline _MSE_ & 4.6 & 2.5 & 2.4 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: MSE and percentages of SD, R20, and R30 per prediction condition (mini-channel data > micro-fin data). Figure 15: Comparison of prediction (open) and experimental (closed) heat transfer coefficients. calculated from experimental data to the training dataset. We propose a novel approach that utilizes microchannel data to predict the heat transfer coefficients of micro-fins, combining physical mechanisms with DNN/GPR/fine-tuning. In the experiments, 4584 data points for micro-fin heat transfer coefficients and \(1111\) data points for mini-channel heat transfer coefficients were used to develop the prediction model, considering 16 types of refrigerants. Three predicting conditions were evaluated: a model using only a deep neural network (DNN), a model combining DNN with GPR, and the proposed fine-tuning approach. In our proposed method, we verified the attainment of high accuracy by introducing dimensionless numbers presumed to exert significant influence on calculated heat transfer coefficients from data. It has been reported that heat transfer coefficients in mini-channels improve as the liquid film thins, and similarly, microfins enhance heat transfer coefficients by facilitating the formation of thin liquid films between fins. Thus, we represented the predominant influence of phase interactions crucial to heat transfer coefficients using dimensionless numbers added to the training dataset. Consequently, by reutilizing the physical insights derived from data in mini-channels as weights, we effectively fine-tuned predictions of microfin characteristics based on thin liquid films. The results predict that the proposed method using fine-tuning with pre-training on mini-channel data outperformed other methods solely trained on micro-fin data in prediction accuracy. Additionally, under conditions where the training data was reduced compared with pre-training data (mini-channel > micro-fin), the proposed method showed equivalent results to existing methods only in terms of the standard deviation (SD) but outperformed in all other evaluation metrics. Furthermore, the proposed method accurately predicted the decrease in heat transfer coefficients according to the quality of post-dryout data with limited experimental data, confirming the effectiveness of this approach. Hence, it has been confirmed that high heat transfer coefficients not only before dryout obtained from experimental data, but also dryout quality and post-dryout heat transfer coefficients, can be predicted with high accuracy. We employed SHAP for visualizing the opaque black box of machine learning models. Our findings revealed that fine-tuning mini-channel data to predict heat transfer coefficients for microfins showed significant influence from mini-channels where surface tension played a crucial role. This influence was observed to affect the initial weights of the fine-tuning process, emphasizing the substantial contribution of features related to surface tension and inner diameter in elucidating the relationship.