Scientific Machine Learning Workflows for Phase-Change Heat Transfer Applications

Scientific Machine Learning (SciML) models show promise in various fields, including phase-change heat transfer. However, obtaining diverse and accurately labeled datasets for training remains a significant challenge, particularly in this domain where evaporation and bubble dynamics play a critical role in determining heat transfer efficiency. Specialized experimental setups, including instrumentation, sensors, and high-speed visualization techniques, incur substantial costs and challenges. This greatly limits the availability of high-fidelity datasets that encompass a wide range of operating conditions necessary to train generalizable SciML models. Numerical simulations can offer high-fidelity multiscale data to complement and enhance experimental measurements. However, training SciML models with large spatio-temporal simulation datasets requires scalable workflows for distributed memory systems with efficient cache and memory management. In this talk, we will present an approach to address these challenges using Flash-X, an open-source simulation software, and BoxKit, a Python interface for managing simulation data. Our computational pipeline integrates numerical simulations, experimental data, and SciML models, enabling learning predictive models capable of handling large 4D spatio-temporal datasets for thermal science applications.