Power intensification and miniaturization of electronics and energy systems are causing a critical challenge for thermal management. Single-phase heat transfer mechanisms including natural and forced convection of air and liquids cannot meet the ever-increasing demands. Two-phase heat transfer modes, such as evaporation, pool boiling, flow boiling, have much higher cooling capacities but are limited by a variety of practical instabilities, e.g., the critical heat flux (CHF), aka departure from nucleate boiling (DNB) in the nuclear industry, flow maldistribution, flow reversal, among others. These instabilities are often triggered suddenly during normal operation, and if not identified and mitigated in time, will lead to overheating issues and detrimental device failures. For example, when CHF is triggered during pool boiling, the device temperature can ramp up in the order of 150 °C/min. It is thus critical to implement real-time detection and mitigation algorithms for two-phase cooling. In the present work, we have developed an accurate and reliable technology for fault detection of high-performance two-phase cooling systems by coupling acoustic emission (AE) with multimodal fusion using deep learning. We have leveraged the contact AE sensor attached to the heater and hydrophones immersed in the working fluid to enable non-invasive fault detection.
The rapid growth and scaling of electronics are causing more severe thermal management challenges. For example, the high-performance computing processors are driving the data center power density to unprecedented levels, approaching the limit of conventional air cooling. In electric vehicles (EVs) and hybrid EVs, the power conversion electronics are integrated into a compact space, leading to ultra-high heat fluxes to dissipate. Among the available thermal management mechanisms, two-phase cooling that involves the phase-change process of the working fluid can maintain electronic devices at safe operating temperatures by taking advantage of the high latent heat of the fluid. Particularly, pool boiling plays a critical role in the two-phase immersion cooling of servers and other IT hardware, integrated cooling for three-dimensional electronic packaging, cooling of the core, and used fuel in nuclear reactors. Two-phase coolers are limited by instabilities such as the critical heat flux (CHF). At the critical heat flux, the temperature increases. It is important to be able to identify the CHF in order to prevent overheating. We aim to develop and compare boiling image classification models to distinguish between 2 boiling regimes. We will leverage principal component analysis (PCA) and K-means clustering to investigate the key differences between bubbles during nucleate boiling (pre-CHF) and transition boiling (post-CHF). We will also compare the results of the unsupervised learning model against popular supervised learning models that have been used for boiling regime classification in existing studies, such as convolutional neural networks, multiplayer perceptrons, and transformers. We successfully created 4 supervised and 1 unsupervised learning models to distinguish between the two types of boiling images.
more » « less- Award ID(s):
- 1946391
- NSF-PAR ID:
- 10497396
- Publisher / Repository:
- American Society of Mechanical Engineers
- Date Published:
- Journal Name:
- Journal of Electronic Imaging
- Format(s):
- Medium: X
- Location:
- Philadelphia, Pennsylvania, USA
- Sponsoring Org:
- National Science Foundation
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