Managing Flow Accelerated Corrosion in Carbon Steel Piping in Nuclear Plants
Bibliographic record
Abstract
In 1996, Flow Accelerated Corrosion (FAC) was identified as a degradation mechanism affecting carbon steel outlet feeder pipes in CANDU® (CANadian Deuterium Uranium) reactors. The maximum rate of FAC was estimated to be <0.120 mm/year. In response, wall thickness inspection programs have been implemented to identify and measure the minimum wall thickness in outlet feeder pipes. These data are necessary to ensure fitness-for-service of the feeder pipe. These data, together with the thermalhydraulic and geometric parameters for the measured feeders, are also very useful for developing empirical wall thickness models. Such models can be used to enhance the understanding of feeder wall thinning leading to an improved capability to predict future wall thickness minima and their locations. The determined dependency of the wall-thinning rate on thermalhydraulic conditions can be used to quantify the potential benefits of maintenance activities, such as steam generator cleaning. Activities such as steam generator cleaning are generally viewed as beneficial in recovering lost thermal efficiency, thereby reducing the severity of the thermalhydraulic conditions by reducing the amount of quality (steam phase) exiting the reactor core. Finally, when wall thickness models are applied to data from different plants, there is the potential of identifying operating conditions that can lead to lower rates of wall loss. This paper addresses the aforementioned important issues associated with FAC of ASME PVP Class 1 carbon steel piping.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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 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".