Abstract
Boiling is arguably Nature’s most effective thermal management mechanism that cools submersed matter through bubble-induced advective transport. Central to the boiling process is the development of bubbles. Connecting boiling physics with bubble dynamics is an important, yet daunting challenge because of the intrinsically complex and high dimensional of bubble dynamics. Here, we introduce a data-driven learning framework that correlates high-quality imaging on dynamic bubbles with associated boiling curves. The framework leverages cutting-edge deep learning models including convolutional neural networks and object detection algorithms to automatically extract both hierarchical and physics-based features. By training on these features, our model learns physical boiling laws that statistically describe the manner in which bubbles nucleate, coalesce, and depart under boiling conditions, enabling in situ boiling curve prediction with a mean error of 6%. Our framework offers an automated, learning-based, alternative to conventional boiling heat transfer metrology.
Highlights
Boiling is arguably Nature’s most effective thermal management mechanism that cools submersed matter through bubble-induced advective transport
These experimental methods are inefficiently connected with visual information, which is a huge downfall for providing a clear description of dynamic boiling physics
Image-based deep learning models can play a vital role in fully understanding boiling physics because boiling images are richly embedded with bubble statistics, which are quantitative measurements of the dynamic boiling phenomena[46,47,48]
Summary
Boiling is arguably Nature’s most effective thermal management mechanism that cools submersed matter through bubble-induced advective transport. The framework leverages cutting-edge deep learning models including convolutional neural networks and object detection algorithms to automatically extract both hierarchical and physics-based features By training on these features, our model learns physical boiling laws that statistically describe the manner in which bubbles nucleate, coalesce, and depart under boiling conditions, enabling in situ boiling curve prediction with a mean error of 6%. Researchers still heavily rely on experiments to measure the boiling heat flux via, e.g., thermocouples[24], electrical power input[25,26], or infrared (IR) techniques[27] These experimental methods are inefficiently connected with visual information, which is a huge downfall for providing a clear description of dynamic boiling physics. Our framework conceptualizes state-of-the-art CNNs and object detection algorithms to automatically extract hierarchical image features as well as physics-based bubble statistics to learn inherent boiling physics. The framework thereby provides quantitative descriptions of underlying boiling activities that can potentially help discover unknown boiling laws
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
