Operational Impacts and Consequences of Piping Component Failure: A Review of Operating Experience Data As Recorded in CODAP
Bibliographic record
Abstract
Water chemistry plays an important part in maintaining corrosion resistance in water transport systems throughout nuclear power plants (NPP’s). Small changes in liquid chemistry such as pH, borate concentration, or build-up of crud in reactor cooling water can result in rapid degradation or damage to components and lead to unexpected failures. The Chemical and Volume Control System (CVCS) and Reactor Water Cleanup System (RWCU) are responsible for maintaining these parameters at appropriate levels, and so failure of either of these systems can result in unnecessary stresses on many other reactor systems due to resulting transients. While the major components of these systems all have sufficient redundancy to prevent major accidents, failure of components in these systems can result in failure of other redundant components and affect plant safety [1]. The CVCS and RWCU systems have experienced aging related degradations and failures in the past, and although they have not affected the system’s emergency functions, they have resulted in unnecessary actuation of related systems, and reactor shutdowns [1]. Reactor shutdowns can result in large changes in reactor coolant chemistry such as oxygen and borate concentration transients, and the build-up of corrosion products which can’t be as easily removed during periods of reactor shutdown [2]. In the following analysis of Component Operational Experience Degradation and Ageing Program (CODAP) experience data; causes, impacts, and preventative actions as recorded in CODAP are examined for degradation events which took place in the CVCS and RWCU, of PWRs and BWRs, respectively. The analysis will demonstrate the usefulness of CODAP in examining reactor component failure trends, as well as discuss insights on improvement for the program.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.010 | 0.011 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".