Learning pool boiling physics with scientific machine learning

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Phase changes are fundamental processes that underpin many natural phenomena and industrial applications such as dew condensation on insects, water droplet harvesting, immersion cooling for electronic devices, and nuclear reactor cooling. Boiling is a particular phase change process which involves the dynamic formation, movement, and evolution of bubbles. Bubbles mark liquid-vapor interfaces, and characterization of their statistics is crucial for unveiling fundamental boiling principles. So far, it has been very challenging to characterize and extract bubble statistics as they are extremely complex, rapidly move, and deform drastically. Such limitations about object tracking and data processing can be addressed by the recent advances in computer vision and deep learning. In this proposed work, computer models are trained to detect and track bubbles in live-imaging data and, subsequently, extract meaningful statistics on bubbles, for example, their number, size, and trajectory, to learn bubbles and boiling physics. The success of using computer vision concepts in thermofluidic engineering will enable modelling the interconnected relationships among surface designs, bubble data, and boiling performances by integrating new technologies in engineering, computer science, and data science. The goal of this project is to develop a novel data-based approach to obtain new understanding about boiling processes. The proposal envisions three major thrusts: (1) Experimentally and computationally extract interpretable and rich physical descriptors from live bubble images, which has been challenging in past; (2) develop learning models to connect surfaces, bubbles, and thermal performances by applying transfer learning, probabilistic surrogate modelling, and sequence learning; and (3) provide a holistic and fundamental understanding of dynamic boiling physics, enabling the inverse design of surfaces with desired heat transfer performance. Another contribution will be enriching the thermal science community with concepts and methods from machine learning, data science, and statistics. This enrichment can positively impact numerous thermofluidic experiments, in general, including cell or particle tracking in biofluidics, droplet study during condensation, and bubbly flow statistics during flow boiling.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.