GENERAL ASSESSMENT OF CONVECTION HEAT TRANSFER CORRELATIONS FOR MULTIPLE GEOMETRIES AND FLUIDS AT SUPERCRITICAL PRESSURE
Bibliographic record
Abstract
The objective of this paper is to assess different correlations independently against a diversified databank—the Canadian Nuclear Laboratories multi-fluid and multi-geometry supercritical heat transfer databank. This databank was recently expanded by adding compiled and original experimental data obtained through collaboration with the Nuclear Power Institute of China. The databank was subjected to screening for outliers, duplicates, and unreliable data. In addition, inappropriate data, not satisfying certain conditions, were removed. Nevertheless, the used databank comprised more than 41 000 measurements of heat transfer to different fluids flowing vertically upward in different geometries. Following a literature review and a compilation of correlations, an assessment of the tabulated correlations was performed against the databank. In total, 24 correlations were considered and applied to the entire database for different fluids including water and different flow geometries including tube, annulus, and rod bundle. Graphical comparison of best-estimate correlations and representative experimental data is presented in this paper. In addition, statistical error analysis was performed and leading correlations were identified. Although the leading correlation showed a standard deviation of less than 6%, variation of predicted wall temperature and heat transfer coefficient with fluid temperature followed the scatter of the experimental data.
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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 | 0.014 | 0.048 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.017 | 0.015 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".