Bubble segmentation and heat flux estimation in nucleate boiling using a resource-efficient deep learning framework

• Deep learning overcomes diagnostic bottlenecks in bubble dynamics visualization. • Synthetically augmented boiling dataset is used for neural network training. • Spatiotemporal bubble characteristics across varying heat fluxes are extracted. • Bubble-level contributions to macroscopic cooling performance are estimated. • Framework can be benchmarked and retuned for new boiling experiments. Boiling is a critical heat transfer mechanism in high-power-density systems driven by bubble ebullition cycles that efficiently manage thermal loads. However, the simultaneous challenges of the millisecond-scale lifecycle of strongly deformable bubbles and the heavy computational burden of processing gigabytes of high-speed photographic data demand immense resources with meticulous parameter tuning for conventional image processing techniques. Herein, we present an end-to-end resource-efficient deep learning framework that overcomes the diagnostic bottlenecks of bubble dynamics visualization and delivers quantitative insights into these processes using a Mask R-CNN model trained on a synthetically augmented boiling dataset. The model extracts spatiotemporal bubble characteristics by segmenting bubble contours across varying heat fluxes ranging from 100 to 1000 kW/m², tracking unique bubbles, and detecting bubble departure from the surface, enabling the estimation of bubble-level contributions to macroscopic cooling performance. Our resource-efficient approach can be rapidly retuned for new boiling experiments with low manual effort, independent of the setup.