Assessment of Choking Flow Models in RELAP5 for Subcooled Choking Flow Through a Small Axial Crack of a Steam Generator Tube
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Abstract
Choking flow plays an integral part not only in the engineered safeguards of a nuclear power plant (NPP), but also to everyday operation. Current NPP steam generators operate on the leak-before-break approach. The ability to predict and estimate a leak rate through a steam generator tube crack is an important safety parameter. Knowledge of the maximum flow rate through a crack in the steam generator tube allows the coolant inventory to be designed accordingly while limiting losses during loss of coolant accidents. Here an assessment of the choking flow models in thermal-hydraulics code RELAP5/MOD3.3 is performed and its suitability to predict choking flow rates through small axial cracks of the steam generator tubes is evaluated based on previously collected experimental data. Three sets of the data were studied in this work which corresponds to steam generator tube crack sample 1, 2, and 3. Each sample has a wall thickness, channel length (L), of 1.285 mm to 1.3 mm. Exit areas of these samples are 5.22 mm2, 9.05 mm2, and 1.72 mm2 respectively. Samples 1 and 2 have the same flow channel length to hydraulics diameter ratio (L/D) of 2.9 whereas sample 3 has a L/D of 6.5. A pressure differential of 6.8 MPa was applied across the samples with a range of subcooling from 5 °C to 60 °C. Flow rates through these samples were modeled using the thermal-hydraulic system code RELAP5/MOD3.3. Simulation’s results are compared to experimental values and modeling techniques are discussed. It is found that both the Henry-Fauske (H-F) and Ransom-Trapp (R-T) models better predict choking mass flux for longer channels. As the channel length decreases both models’ predictions diverge from each other. While RELAP5/MOD3.3 has been shown to predict choking flow in large scale geometries, further investigation of data sets need to be done to determine if it is suited well for small channel lengths.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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