Classification of Heat Transfer Coefficient Using Deep Learning Incorporated Boiling Images

Boiling cooling has been used as a cooling method for electronic devices due to its high heat transfer coefficient (HTC).The regularity of the boiling phenomenon is a crucial factor in the development of more efficient cooling systems.To develop such systems, it is essential to accurately measure the HTC, which is closely related to the boiling phenomenon.In this paper, we propose a method for predicting the HTC of two different heat transfer surfaces using deep learning with boiling sound and boiling images as inputs.The proposed method achieves an accurate improvement of 2.0% and 16.7% compared to models using only boiling sound and boiling images as input, respectively.