Choking Flow of Subcooled Liquid in Steam Generator Tube Wall Cracks
Bibliographic record
Abstract
Steam generator tubes have a history of small cracks and even ruptures, which lead to a loss of coolant from the primary side to the secondary side. Currently, steam generators operate under a leak-before-break approach. A rupture then signifies the loss of the integrity of the tube itself. Therefore, choking flow plays an integral part not only in the engineered safeguards of a nuclear power plant, but also to everyday operation. Choked flow of subcooled water through small cracks such as in steam generator tube wall cracks is studied both with experiments and analytical models. The knowledge of this maximum flow rate through a crack in the steam generator tubes of a pressurized water nuclear reactor will allow designers to calculate leak rates and design inventory levels accordingly while limiting losses during loss of coolant accidents. Slits of very small channel length to hydraulics diameter ratio (L/D) were manufactured and tested upto 6.89 MPa pressure and range of subcoolings 10–40 °C. Small flow channel length was used (1.3mm) equivalent to steam generator tube thickness with differences in surface roughness. The effect of L/D on the choking flow rates was examined and was contrasted with other data in literature. Analytical models were applied highlighting the importance of non-equilibrium effects and the effects of L/D ranging from 1.3 to 400 on the chocked flow were investigated.
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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 | 0.000 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".