Technique for Determining Local Nucleate and Film Boiling Correlations for Large Diameter Tubes
Bibliographic record
Abstract
Abstract The boiling behaviour on the surface of large diameter tubes is known to be strongly dependent on the local orientation of the surface around the circumference. Understanding such local variations in boiling behaviour is of particular interest in the CANDU® nuclear industry, where part of the heat removal path for cooling the fuel under postulated accident conditions is via pool-boiling on the surface of the 132 mm diameter calandria tube that is immersed in a pool of the heavy water moderator. While the average pool boiling behaviour of the calandria tubes has been well studied with integrated experiments, local boiling correlations have not been developed for prototypical diameter calandria tubes. Local boiling correlations will allow for more detailed modelling of postulated accident scenarios and better quantification of safety margins. In this study, the nucleate and film pool-boiling characteristics of a large diameter Zircaloy-2 CANDU® calandria tube were tested in a pool of subcooled water. A novel technique is developed to derive the local boiling curve along the circumference of the tube, involving the local heating, measurement of the wall temperature, and heat flux. The adequacy of the technique is determined by comparing the local boiling curves with previous experiments in which averaged pool boiling characteristics were obtained.
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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) | 0.000 | 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 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".