Numerical Calculation of Shear Stress Distribution on the Inner Wall Surface of CANDU Reactor Feeder Pipe Conveying Two-Phase Coolant
Bibliographic record
Abstract
Two-phase flow fields inside feeder pipes of a CANada Deuterium Uranium (CANDU) reactor have been simulated numerically using a computational fluid dynamics (CFD) code to calculate the shear stress distribution, which is the most important factor to be considered in predicting the local areas of feeder pipes highly susceptible to flow-accelerated corrosion (FAC)-induced wall thinning. The CFD approach with schemes used in this study to simulate the turbulent flow situations inside the CANDU feeder pipes has been verified by showing a good agreement between the investigation results for the failed feedwater pipe at Surry Unit 2 plant in the U.S. and the CFD calculation. Sensitivity studies of the three geometrical parameters such as angle of the first and second bends, length of the first span between the grayloc hub and the first bend, and length of the second span between the first and second bends had already been performed. In this study, the effects of void fraction of the primary coolant coming out from the exit of pressure tubes containing nuclear fuel on the fluid shear stress distribution at the inner surface of the feeder pipe wall have been investigated to find out the local areas of feeder pipes conveying a two-phase coolant, which are highly susceptible to FAC-induced wall thinning. From the results of the CFD analysis, it is seen that the local regions of feeder pipes of the operating CANDU reactors in Korea, on which the wall thickness measurements have been performed so far, do not coincide with the worst regions predicted by the present CFD analysis, which is the connection region of straight and bend pipes near the inlet part of the bend intrados. Finally, based on the results of the present CFD analysis, a guide to the selection of the weakest local positions where the measurement of wall thickness should be performed with higher priority has been provided.
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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 | 0.000 |
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".