# Deep learning predicts boiling heat transfer

## TL;DR

This paper presents a deep learning framework that predicts boiling heat transfer by analyzing bubble dynamics using high-quality imaging data.

## Contribution

The novel contribution is a data-driven learning framework that automatically extracts features from bubble dynamics to predict boiling curves.

## Key findings

- The model learns physical boiling laws describing bubble nucleation, coalescence, and departure.
- The framework achieves in situ boiling curve prediction with a mean error of 6%.
- It offers an automated alternative to traditional boiling heat transfer measurement methods.

## 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.