Development of a TRACE Critical Break LOCA Model for D-PSA Applications with RAVEN
Bibliographic record
Abstract
In the nuclear power industry, several design-basis accidents are critical for nuclear power plant design and licensing, with loss-of-coolant accidents (LOCAs) being particularly significant for ensuring safe shutdown, emergency cooling, and adequate containment systems. In CANada Deuterium Uranium (CANDU) reactors, a large-break LOCA causes an immediate power surge due to rapid voiding and the positive void reactivity coefficient, with break location greatly influencing severity. Inlet piping breaks, for example, can cause flow reversal, higher voiding rates, or flow stagnation. Conservative assumptions like double-ended guillotine breaks ensure bounding analyses, but for certain metrics (e.g. CANDU fuel channel integrity), partial inlet breaks may be more restrictive, necessitating critical break searches. Break size is crucial in determining mass loss, reactivity, heat deposition, and post-LOCA cooling, impacting severity and mitigation strategies. The Dynamic Probabilistic Safety Assessment (D-PSA) CANDU LOCA pilot aims to identify the most sensitive parameters in critical scenarios and demonstrate the value of D-PSA for risk-informed methods. Using stochastic generation of input parameters under uncertainty, D-PSA quantifies effective risk mitigation factors. While best-estimate analyses attempt to quantify uncertainty in figures of merit, they often impose restrictive conditions. This study integrates uncertainty analysis with component and human reliability in the D-PSA framework, applying Monte Carlo sampling of TRACE input parameters through the RAVEN framework to evaluate dynamic parameters’ impact and compare results with existing CANDU LOCA studies.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".