Experimental Investigation of the Effect of Heat Spreading on Boiling of a Dielectric Liquid for Immersion Cooling of Electronics
Bibliographic record
Abstract
Abstract This paper investigated the effect of heat spreading on the boiling of the Novec 649™ for two-phase immersion cooling of electronics. Reference pool boiling tests were performed by attaching a 25.4 mm by 25.4 mm square copper plate to a same-sized heater, thus minimizing lateral heat spreading. Experimental measurements showed that the critical heat flux (CHF) happened at a heat flux of 17.4±0.8 W/cm2. Then, lateral heat spreading through the heat spreader was studied by attaching larger (47 mm by 47 mm) spreaders with four different thicknesses to the copper plate. With an increase in the integrated heat spreader (IHS) thickness from 1 mm to 6 mm, the CHF increased by more than 60% at the saturation condition. One plate was a 1 mm-thick IHS removed from a commercial microprocessor. In this case, the CHF happens at 8.6 W/cm2 (50% lower compared to the reference case) in the saturation condition. At CHF, the boiling can be observed on the whole surface, with columns and slugs regime at the center and the fully developed nucleate boiling regime at the edges. This nonuniform boiling was more pronounced in subcooled conditions, in which the CHF occurred at the center while there were regions at the edges that had no boiling. Finally, the performance of a microporous-coated IHS (with 3.15 mm thickness) was compared to the 6 mm thick IHS. The thermal resistance was almost equal for powers above 200 W. This indicates that lateral heat spreading is a critical parameter for the thermal design of immersion cooling along with microporous coating.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".