Heat Transfer to Supercritical Water in a Horizontal Pipe: Modeling, New Empirical Correlation, and Comparison Against Experimental Data
Bibliographic record
Abstract
Abstract Enhancement of heat transfer to supercritical fluids has drawn the attentions of many researchers within the past few decades. Modeling and predicting heat transfer to turbulent flow of supercritical fluids, however, are very complicated due to severe variations of fluid properties near the critical point. Large discrepancies between available heat transfer data are greatly due to confusion of forced convection and mixed convection data. The data unaffected by buoyancy have been selected cautiously from a large database generated in this study. Such data have been used to develop a 1D numerical model as well as a semi-empirical correlation to predict forced convection heat transfer to turbulent flow of supercritical water. In the numerical model, radial variations of heat flux and shear stress are taken into account. Modifications to turbulent Prandtl number and wall shear stress formulations have been applied to a law of the wall type of model to fit supercritical conditions. The model shows good agreement with experiments. In the experimental part, the extensive database obtained on a full-scale test facility in the present study, plus a new conceptual approach, has been employed together to develop a semi-empirical heat transfer correlation. It accurately predicts the experiments.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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".