Determination of Operator Action Times during a Loss-of-Feedwater Event Using Extreme Value Statistics
Bibliographic record
Abstract
The pursuit of more realistic models for nuclear power plant systems is becoming increasingly important and has led to an expansion in statistical uncertainty analysis coupled with the use of best-estimate predictions. Within these methodologies, derived acceptance criteria have been developed to ensure that the ultimate safety criteria are met with acceptably high levels of probability and confidence. The meeting of these derived criteria with a probability of 95% for a confidence interval of 95%, the 95/95 criteria, ensures consistency between analysis and instrumentation accuracy requirements set forth in ISA 67.04 standards. However, the application of these statistical methods to accidents requiring operator intervention, such as complete loss-of-feedwater events, has not previously been the topic of investigation. This paper applies the extreme value statistics (EVS) methodology to the steam generator–level transients predicted to result from a total loss-of-feedwater accident and compares the result to other uncertainty propagation methods and deterministic calculations. The transient was modeled using a full-circuit one-dimensional thermal-hydraulic code, and the epistemic and aleatory uncertainties inherent in the reactor are assessed. Based upon these results the available steam generator inventories at the time of trip were statistically determined, and subsequently, the available times for operator action were determined. Comparisons were made between the EVS methods and limiting deterministic analysis results for a standard CANDU 9 design as well as to other best-estimate and uncertainty-analysis techniques. Key uncertainties were identified based on phenomena identification and ranking tables and were confirmed through sensitivity studies. The requirement for operator-initiated actions for the EVS case was ∼46 min with 95% probability and 95% confidence from the time of annunciation, and this was 30 min longer than the limiting deterministic case.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".