A Parametric Investigation of Operating Limits in Heat Pipes Using Novel Metal Foams as Wicks
Bibliographic record
Abstract
A parametric investigation has been performed to study the different operating limits of heat pipes employing a novel type of metal foam as wick for chip cooling applications. These foams have a unique spherical pore cluster microstructure with very high surface to volume ratio compared to traditional metal foams and exhibit higher operating limits in preliminary tests of heat pipes, suggesting high cooling rates for microelectronics. In the first part of this parametric study, widely used correlations are applied to calculate the five types of heat transfer limits (capillary, boiling, viscous, entrainment and sonic) as a function of temperature, type of foam, and porosity. Results show that the dominant limit is mostly the capillary limit, but for 50 pore-per-inch (PPI) foam, the boiling limit will be dominant. Also, 50 and 60 PPI foams have higher heat transfer limits than sintered copper powder. In the second part of this study, thermodynamic steady state modeling of a flat heat pipe has been done to study the effect of the different parameters on the dominant limit (capillary). A dimensionless number has been proposed to evaluate the balance between the pressure loss in the vapor and liquid phases as an additional design guideline to improve the capillary limit in flat heat pipes.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".