A critical review of additively manufactured wick structures for heat pipes with their thermal and hydraulic performance
Bibliographic record
Abstract
Heat pipe technology, being a passive and two-phase heat transfer method, is the future of heat transfer and thermal management. It transfers heat at much higher rates as compared to traditional active single phase heat transfer methods via heat exchangers and heat sinks. Additive manufacturing (AM) as part of industry 4.0 has helped to evolve the manufacturing of these heat pipes. Using AM in heat pipe manufacturing, thermal and capillary performance of heat pipes improved due to the inclusion of lattice-based filigree wick structures. In this review paper, thermal and capillary performance of specific categories of heat pipes, i.e. flat plat heat pipes, vapor chambers and cylindrical heat pipes have been reviewed. Moreover, measurement methods for various design and performance parameters along with operational limits have been discussed. Design performance parameters including wick material, wick structure, filling ratio of working fluid play role in designing these heat pipes. Moreover, measurement methods for gauging hydraulic and thermal performance along with their parameters including capillary performance ratio, porosity, thermal resistance and effective thermal conductivity have been summarized with analysis. The present status, challenges and prospects of HP technology vis-à-vis use of AM for this technology have been discussed.
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".