Numerical and Parametric Investigation of the Effect of Heat Spreading on Boiling of a Dielectric Liquid for Immersion Cooling of Electronics
Bibliographic record
Abstract
Abstract In a two-phase immersion cooling system, boiling on the spreader surface has been experimentally found to be nonuniform, and it is highly related to the surface temperature and the heat transfer coefficient. An experimentally obtained temperature-dependent boiling heat transfer coefficient has been applied to a numerical model to investigate the spreader's cooling performance. It is found that the surface temperature distribution becomes less uniform with higher input power. But it is more uniform when the thickness is increased. By defining the characteristic temperatures that represent different boiling regimes on the surface, the fraction of the surface area that has reached the critical heat flux has been numerically calculated, showing that increasing the thickness from 1 mm to 6 mm decreases the critical heat flux reached area by 23% at saturation liquid temperatures. Therefore, on the thicker spreader, more of the surface is utilized for nucleate boiling while localized hot regions that lead to surface dry-out are avoided. At a base temperature of 90 °C, the optimal thickness is found to be 4 mm, beyond which no significant improvement in heat removal can be obtained. Lower coolant temperatures can further increase the heat removal; it is reduced from an 18% improvement in the input power for the 1 mm case to only 3% in the 6 mm case for a coolant temperature drop of 24 °C. Therefore, a tradeoff exists between the cost of maintaining the low liquid temperature and the increased heat removal capacity.
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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 | 0.002 |
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".