Classification of Heat Transfer Coefficient Using Deep Learning Incorporated Boiling Images
Year: 2025
Type: conference-paper
Abstract: 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.
Authors: Fuga Mitsuyama, Ren Umeno, Kaito Takakuma, Tomohide Yabuki, Tohru Kamiya
Institution: Kyushu Institute of Technology
Language: English
Subfield: Mechanical Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: bronze
OpenAlex ID: W4409913965