Effect of Self-Rewetting Fluids on Pulsating Heat Pipe Thermal Performance
Bibliographic record
Abstract
Pulsating heat pipes (PHPs) have recently emerged as a possible cooling device for high heat flux electronics to replace conventional cooling devices. In this study, new experimental results were obtained for using self-rewetting fluids to enhance the heat transport of PHPs. Unlike other common liquids, the surface tension of self-rewetting fluids increases with temperature. The increase in surface tension at high temperatures causes the liquid to be drawn towards a heated surface if a dry spot appears, which improves boiling heat transfer. PHPs were constructed out of multiport extruded aluminum tubing with a square channel cross section. In experiments, heptanol was added to water at a concentration of less than 1 wt% to form the self-rewetting fluid. Several other parameters were adjusted for optimization, such as the aqueous alcohol solution concentration of the working fluid, the fluid fill ratio, and the heat pipe orientation. Using a self-rewetting fluid in PHPs was found to be highly effective in improving their heat transport capability. The PHPs delivered a better performance when oriented vertically rather than horizontally. As a working fluid, the heptanol water mixture outperformed both the butanol water mixture and pure water within the parameters of this experiment.
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 | 0.000 | 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) | 0.001 | 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".