Localized Pool Boiling and Condensation Experiments over Functional CPU: Optimizing the Overall Thermal Resistance via Different Heat Transfer Scenarios
Bibliographic record
Abstract
Pool boiling cooling systems are one of the most promising candidates to address the increase of electronics power consumption. This cooling technique still exhibits many challenges to be fully adopted, such as the high-reliability risk associated with the full immersion of electronic components in dielectric liquids and the film boiling phenomena. This paper reports an investigation of the effects of multiple boiling scenarios on the overall thermal resistance of a close two-phase cooling system, mounted directly over a functional microprocessor. Two dielectric fluids (Novec 649 and 7000 from the 3M Corporation) were tested over nickel and copper processor surfaces. A better overall thermal resistance was achieved when boiling the Novec 7000 on top of the copper exposed processor surface. Degassing the setup to remove non-condensable gases lowered the absolute pressure inside the system and reduced the overall thermal resistance. Moreover, partially immersing inward heat sink pins into the dielectric liquid was observed to also lower thermal resistance. The best boiling scenario was achieved while using Novec 7000 and combining all other improvements. A (0.38±0.01) °C/W minimum overall thermal resistance was calculated from junction to air at a (130±4) W power consumption and a (73±0.4) °C maximum junction temperature. This minimum overall thermal value was 30% lower than the one associated with the best boiling scenario using Novec 649.
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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 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".