Assessment of Convective Heat Transfer Correlations Against an Expanded Database for Different Fluids at Supercritical Pressures
Why this work is in the frame
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Bibliographic record
Abstract
Canadian Nuclear Laboratories (CNL) has recently expanded the supercritical heat transfer (SCHT) databank with additional data provided by the Nuclear Power Institute of China (NPIC). These additional data cover flow conditions beyond the current databank, and are applicable for improving or validating existing correlations. The expanded databank comprises more than 41,000 points of heat-transfer measurements with different fluids flowing vertically upward in tubes, annuli, and bundles at supercritical (SC) pressures. It has been applied in assessing the prediction accuracy of 24 heat-transfer correlations, which were derived from experimental data obtained with water or nonaqueous fluids (such as carbon dioxide) flowing in tubes. For the correlation assessment, a sensitivity analysis has been performed by applying the measured wall temperature as an independent parameter. The assessment against the bundle data was based on cross-sectional-averaged flow conditions and the hydraulic diameter. The iterative approach (i.e., without prior knowledge of the wall temperature) overpredicted the wall temperature, which is conservative in safety analyses.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| 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 it