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Heat fluxes in the data center have been increasing significantly due to the rise in advanced technologies such as Artificial Intelligence (AI), 5G, high-performance computing (HPC), and machine learning. The traditional air-cooling technology cannot handle high heat fluxes and requires a bigger heat sink; therefore, hindering high heat flux and high density in the data center. Two-phase cooling schemes are particularly appropriate for high heat flux situations because of their enhanced heat transfer coefficients and the non-linear relationship between heat flux and surface-to-fluid temperature difference. In this study, an experimental setup was developed to characterize and optimize the thermo-hydraulic performance of two-phase cooling cold plates intended for high heat flux applications. An improvement of 12% in thermal performance was obtained by cutting the original fins and creating mini-channels perpendicular to the original microchannels without a significant pressure drop penalty.
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Recent progress of artificial intelligence for liquid-vapor phase change heat transfer
Abstract Artificial intelligence (AI) is shifting the paradigm of two-phase heat transfer research. Recent innovations in AI and machine learning uniquely offer the potential for collecting new types of physically meaningful features that have not been addressed in the past, for making their insights available to other domains, and for solving for physical quantities based on first principles for phase-change thermofluidic systems. This review outlines core ideas of current AI technologies connected to thermal energy science to illustrate how they can be used to push the limit of our knowledge boundaries about boiling and condensation phenomena. AI technologies for meta-analysis, data extraction, and data stream analysis are described with their potential challenges, opportunities, and alternative approaches. Finally, we offer outlooks and perspectives regarding physics-centered machine learning, sustainable cyberinfrastructures, and multidisciplinary efforts that will help foster the growing trend of AI for phase-change heat and mass transfer.
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- Award ID(s):
- 2045322
- PAR ID:
- 10497759
- Publisher / Repository:
- Nature Publishing Group
- Date Published:
- Journal Name:
- npj Computational Materials
- Volume:
- 10
- Issue:
- 1
- ISSN:
- 2057-3960
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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