Detailed Finite Element Analysis of Darlington NGS Feeder Pipes With Locally Thinned Regions Below Pressure Minimum Thickness
Bibliographic record
Abstract
Feeder Pipes in CANDU nuclear stations are experiencing wall thinning due to flow accelerated corrosion (FAC) resulting in locally thinned regions in addition to general thinning. In Darlington NGS these locally thinned regions can be below pressure based minimum thickness (tmin) in the straight pipe region (STR1) between Grayloc and Bend 1. The (tmin) is required as per ASME Code Section III NB-3600 Equation (1). A methodology is presented to qualify the locally thinned regions under NB-3200 (NB-3213 & NB-3221) using internal pressure loading only. Detailed finite element models are used for internal pressure analysis using ANSYS v11.0. All other loadings such as deadweight, thermal and seismic loadings are separately qualified under NB-3600 using a general purpose piping stress analysis software. The piping stress analysis is based on average thickness equal to tmin along with maximum values of ASME Code stress indices (Table NB-3681(a)-1). The requirement for the use of this methodology is that the average thickness of each cross-section with the locally thinned region shall be at least tmin. In contrast, the finite element analysis models are thinned to 0.75 tmin (in increments of 0.05 tmin) all-around the circumference in the straight section region allowing for flexible inspection requirements. In Darlington NGS there are 22 different types of feeder pipes which have been grouped into 6 types based on similarities in pipe size, straight section length, bend angle and orientation. Thickness versus the allowable axial extent curves were developed for 6 representative feeder types. The stress analysis results show that all Darlington NGS outlet feeder pipes are fit for service with locally thinned regions in STR1 with down to 75% of the pressure based minimum thickness. This paper demonstrates the methodology and effectiveness of finite element analysis in extending the useful life of degraded piping components.
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How this classification was reachedexpand
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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".