Modelling of heat transfer in an enclosed two-phase dielectric immersioncooling system for electronic components
Bibliographic record
Abstract
Electronic components, such as high-performance processors for data centres, are becoming increasingly powerful and generate large quantities of heat. The 2022 International Technology Roadmap for Devices and Systems projects that the maximum thermal design power for individual chip socket will increase from 300 W in 2022 to 700 W in 2032. Thermal management of these components has been a major performance bottleneck for years. Two-phase dielectric immersion is a cooling solution that could provide the cooling capacity required for future high performances electronics. The current study is part of an effort to develop a closed two-phase dielectric immersion cooling system for high-performance processors. The system studied was a chamber filled with a dielectric fluid, that had a heating element at the bottom on which boiling occurred. At the top of the chamber was a heat sink cooled by a water-fed cold plate on which condensation occurred. An experimental prototype was built to evaluate the impact of important operating parameters on the thermal performances of the cooling system. The operating parameters chosen were the filling ratio of the chamber, the flow rate and the inlet temperature of the water circulating in the cold plate. The temperature and the pressure inside the chamber were measured. In each set of tests, the power supplied to the heating element was increased until the maximum pressure limit of the system was reached. The objective of the study was to develop a model based on basic principles and equations to calculate the temperature and the pressure inside the chamber as a function of the power supplied the heating element. To model was used to provide insights on how the operating parameters had an impact on the thermal performances of the system. The output of the model was close to the experimental data. The transient temperature and pressure profiles inside the chamber calculated with the model matched the experimental data with less than 10% of error. The impact of the filling ratio on the overall thermal resistance was negligible. The cold plate flow rate and temperature both had a significant impact on the thermal performances. A higher flow rate and a lower temperature led to a lower overall thermal resistance. The model was developed for the condensation assembly used in the experimental setup due to the complexity of the condensation in a closed chamber with boiling a dielectric fluid.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".