抄録
The prediction of the heat transfer coefficient commonly relies on the development of new empirical prediction equations when operating conditions and refrigerants change from the existing equations. Creating new prediction equations is expensive and time-consuming; therefore, recent attention has been given to machine learning approaches. However, machine learning requires a large amount of data, and insufficient data can result in inadequate accuracy and applicability. This study showed that using mini-channel data as highly relevant data for the micro-fin heat transfer coefficient yields high prediction accuracy, even when the experimental dataset of interest is limited. In the proposed method, we added dimensionless numbers assumed to significantly influence heat transfer coefficients calculated from experimental data to the training dataset. This allowed efficient learning of the characteristics of thin liquid films present in mini-channels and micro-fins. By combining distinctive physical mechanisms related to heat transfer coefficients with DNN/GPR/Fine-tuning, the proposed method can predict 96.7% of the data points within ±30% deviation. In addition, it has been confirmed that the dryout quality and post-dryout heat transfer coefficients were predicted with high accuracy. Additionally, we utilized visualization techniques to investigate the contents of the black-box machine learning models.