Pulsating Heat Pipe Performance Modeling with Liquid Metal Coolants Under Hypersonic Aerothermal Heating
Bibliographic record
Abstract
Hypersonic heating loads concentrate at leading-edge compression areas to create excessively high local temperatures and thermally driven stresses. The fast and reliable thermal dispersion of heat pipes with significantly high thermal conductance can alleviate these localized thermal stiffness problems. The pulsating heat pipe (PHP) holds numerous advantages when compared to capillary, constant-conductance heat pipes for hypersonic thermal management applications, primarily because they lack a wicking structure. This paper numerically investigates thermal performances of a four-turn C103 niobium alloy PHP operating with lithium, potassium, sodium, and a eutectic sodium–potassium alloy (NaK-78) when exposed to heating conditions relevant to the hypersonic environment, investigating flight Mach numbers ranging from 6 to 8 and dynamic pressures ranging from 40 to 44 kPa. The robust thermofluidic properties of liquid metals, along with the powerful fluid pulsation induced in the PHP, can provide significant thermal transport from hot stagnant regions of the leading edge to the cooler trailing surfaces. Potassium showed superior thermal performance when compared to other liquid metal coolants under the presently tested conditions, with overall PHP thermal conductance as high as [Formula: see text] predicted for Mach 8 flight conditions. In contrast, sodium was associated with startup difficulties in the PHP; this paper attributes this to its significantly larger thermal conductivity, which can limit the vapor pressure difference over the liquid slug lengths. These predictions indicate an overall latent heat transfer dominance of around 70–95% in liquid metal PHPs.
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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 | 0.000 |
| 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 | 0.000 |
| 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".