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Abstract 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.
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CFD Simulation of Two-Phase Immersion Cooling Using FC-72 Dielectric Fluid
Abstract With more development in electronics system capable of having larger functional densities, power density is increasing. Immersion cooling demonstrates the highest power usage efficiency (PUE) among all cooling techniques for data centers and there is still interest in optimizing immersion cooling to use it to its full potential. The aim of this paper is to present the effect of inclination and thermal shadowing on two-phase immersion cooling using FC-72. For simulation of boiling, the RPI (Rensselaer Polytechnic Institute) wall boiling model has been used. Also, two empirical models were used for calculation of bubble departure diameter and nucleate site density. The boundary condition was assumed to be constant heat flux and the bath temperature was kept at boiling temperature of FC-72 and the container pressure is assumed to be atmospheric. this study showed that due to the thermal shadowing, boiling boundary layer can lay over the top chipset and increases vapor volume fraction over top chipsets. This ultimately causes increase in maximum temperature of second chip. The other main observation is with higher inclination angle of chip, maximum temperature on the chip decreases up to 3°C.
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- Award ID(s):
- 1738811
- PAR ID:
- 10276622
- Date Published:
- Journal Name:
- ASME 2020 International Technical Conference and Exhibition on Packaging and Integration of Electronic and Photonic Microsystems
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1115/IPACK2020-2595
