June 15, 2026 at 3:00 PM UTC
Duration: 50 mins
Boiling heat transfer offers exceptionally high heat removal capability, making it attractive for energy-efficient thermal management in electronics cooling, power systems, and other high heat-flux applications. However, the practical use of boiling is constrained by the critical heat flux (CHF), beyond which vapor blanketing can trigger a sharp surface-temperature excursion and potential device failure. This talk presents an acoustics-based sensing and control framework for predicting and mitigating CHF in pool boiling systems. Acoustic emissions generated during boiling are used as a non-intrusive diagnostic signal to identify boiling regimes and detect precursors to CHF. Deep learning models trained on these acoustic signatures enable real-time classification of boiling states and advance prediction of impending CHF. Beyond prediction, the framework is extended to adaptive control, where an on-demand cooling intervention is activated based on acoustic feedback to delay thermal runaway and push the operating limit beyond the nominal CHF condition. The results demonstrate how low-cost acoustic sensing, machine learning, and active control can transform boiling from a passively monitored heat-transfer process into an actively regulated thermal management strategy. The broader implication is a pathway toward safer, more compact, and more energy-efficient two-phase cooling systems that operate closer to their physical limits.
Postdoctoral Fellow
Purdue University
The purpose of this webinar is to introduce an acoustics-based sensing, machine learning, and control framework for improving the safety and performance of boiling-based thermal management systems. The talk will show how acoustic emissions from pool boiling can be used to identify boiling regimes, predict the onset of critical heat flux, and activate adaptive cooling interventions before thermal runaway occurs. By connecting low-cost sensing, deep learning, and active control, the webinar will highlight a practical pathway toward compact, energy-efficient, and reliable two-phase cooling systems for electronics, power systems, and other high heat-flux applications. This topic aligns with the broader Ind-US Energy Efficiency Initiative by advancing applied thermal technologies and AI-enabled optimization for next-generation energy and thermal systems.