Pool Boiling Experiment of Dielectric Liquids and Numerical Study for Cooling a Microprocessor
Bibliographic record
Abstract
Two-phase liquid immersion cooling has not yet reached its full potential because of two technological issues. The first issue is the boiling crisis and the second is the reliability risk caused by the immersed components, which are designed to work in air cooling applications. Experimental and numerical studies were performed to find the heat transfer limits of immersion cooling of microprocessor and new heat transfer design parameters are proposed. Pool boiling experiments were performed on bare copper surfaces for two dielectric fluids, Novec 649 and Novec 7100, and the critical heat fluxes were found to be 19.5 W/cm2and 23.8 W/cm2, respectively. Three-dimensional conduction models of a microprocessor were built to predict the junction temperature and junction-to-ambient thermal resistance. Effect of the integrated heatsink (IHS) thickness at different heat transfer coefficients have been investigated and the optimal thickness for the IHS is predicted to be around 4 mm while the heat transfer coefficient is less than 20 000 W/m2K on the IHS. Boiling directly on the silicon die has been studied, in order to examine the effect of a decreased thermal resistance by removing the thermal interface material and IHS. In this case, the heat transfer coefficient is predicted to be more than 20 000 W/m2K and to have better heat dissipation.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".