Machine Learning Enabled Prediction of Phase Change Heat Transport during Boiling
| Interdisciplinary Areas: | Power, Energy, and the Environment, Others |
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Proect Description
Proper thermal management of semiconductor devices is critical to maximize performance and ensure energy efficient operation in applications ranging from large-scale AI computing to electric vehicle traction drives. Various proposed cooling technologies rely on two-phase flow boiling due to widely recognized advantages of substantial size reduction, efficiency gains, and power density increases in components compared to single-phase liquid cooling. However, the design and optimization of systems that can achieve these benefits in practice is hindered by the inability to model and predict two-phase flow and heat transfer with high certainty and generality. To address this gap, emerging physics-informed (PhI) machine learning (ML) techniques have been shown to enable interpretation and forecasting of noisy and chaotic dynamic systems, with recent application to interpretation of phase change processes including boiling and condensation. This Lillian Gilbreth Postdoctoral Fellowship project will explore techniques that leverage PhI ML and computer vision techniques to enhance understanding two-phase flow boiling (spanning potential topics of metrology development, data interpretation, model discovery, etc.) to facilitate generalized prediction of phase change during two-phase flows.
Start Date
2025
Postdoc Qualifications
| Background in mechanical or electrical engineering with expertise in physics-informed machine learning and computer vision or modeling/metrology expertise related to phase change processes, interfacial transport phenomena, or flow boiling heat transfer. |
Co-advisors
Justin A Weibel, jaweibel@purdue.edu Professor, School of Mechanical Engineering; Director, Cooling Technologies Research Center: https://engineering.purdue.edu/CTRC/research/index.php
Fengqing Maggie Zhu, zhu0@purdue.edu Associate Professor, Elmore Family School of Electrical and Computer Engineering; https://engineering.purdue.edu/~zhu0/
Bibliography
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