Assessment of Convective Heat Transfer Correlations Against an Expanded Database for Different Fluids at Supercritical Pressures
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.007 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".