Rapid identification of boiling crisis with event-based visual streaming analysis

The formation of a stable vapor layer during boiling processes triggers the boiling crisis that can damage the entire system. While there have been efforts to build a predictive model for critical heat flux on live images, unfortunately, images from conventional frame-cameras show limitations for performing real-time digital inference as they contain significant unnecessary information. A new type of imaging technique, neuromorphic event imaging, captures individual pixel-wise brightness changes, thereby recording motion-based phenomena in a sparse and efficient manner. In this study, we develop neural network models capable of predicting the onset of boiling crisis by utilizing neuromorphic-simulated event data. We reveal that event-based models demonstrate their proficiency and time efficiency in learning patterns of the boiling process from relatively small datasets. The event-based models also prove the generalizability to unseen surfaces by showing the potential for transfer learning. The results suggest that an extension of this work may prove effective in developing forecasting models that detect the incipient boiling crisis before it damages boiling surfaces. These findings highlight that event data can be effectively applied to real-time, motion-dependent contexts.