Research on heat transfer characteristics of supercritical hydrogen in rough U‐shaped channels
Bibliographic record
Abstract
Abstract To explore hydrogen's cooling performance and mechanism as a coolant in heat exchangers, the RNG k‐ε turbulence model is employed to simulate the heat transfer characteristics of supercritical hydrogen within U‐shaped tubes. The influence of factors such as the heat‐to‐mass ratio, hydraulic radius, and roughness on the flow and heat transfer characteristics is analyzed. Emphasis is placed on exploring the mechanism by which the introduction of roughness enhances heat transfer. A new heat transfer correlation equation is established. The results show that for vertical U‐shaped tubes under cooling conditions, increasing surface roughness or reducing the heat‐to‐mass ratio and hydraulic radius significantly enhances heat transfer. Compared to increasing the mass flow rate, reducing the heat flux and consequently lowering the heat‐to‐mass ratio can increase the heat transfer coefficient by approximately three times, with less heat transfer decay at the outlet section. Dimensionless number analysis reveals that buoyancy and flow acceleration effects have negligible impacts on heat transfer during hydrogen flow. However, centrifugal forces alter the circumferential thermal property gradients, effectively enhancing heat transfer in the curved sections. Finally, the proposed heat transfer correlation based on surface roughness predicts an accuracy deviation of ±20%, making it suitable for predicting the heat transfer of supercritical hydrogen.
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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 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".