PRELIMINARY METHODOLOGY FOR THE ANALYSIS OF THE NATIONAL RESEARCH UNIVERSAL REACTOR USING INTEGRATED SEVERE ACCIDENT MODELLING CODES
Bibliographic record
Abstract
The National Research Universal (NRU) Reactor is a multi-purpose research reactor located at Atomic Energy of Canada Limited (AECL) Chalk River Laboratories. The severe accident case for the NRU has been explored through deterministic and probabilistic safety analysis (PSA) including multi-level PSAs that detail the progression and consequences of a severe accident in the NRU. These previous calculations lack the interconnected and comprehensive features of a full severe accident modelling code that is now the standard for severe accident analysis of power reactors. It was of interest within AECL to evaluate modern severe accident modelling codes to the NRU reactor case to enhance the understanding of accident progression and predict the system damage and radiation release consequences of a severe accident, which is a very low probability event. The NRU is smaller and operates at a lower power than the large scale power reactors (e.g., pressurized heavy water reactors, pressurized water reactors, and boiling water reactors) that these codes were designed to analyze. Additionally, the NRU has a unique design different from the power reactors and several features relevant to severe accidents including filtered venting, large passive heat sinks, and a dispersion fuel design of uranium-silicide in an aluminum matrix. The major severe accident analysis codes available to AECL and their applicability to the NRU are explored in this paper. In addition, a preliminary strategy for employing the most applicable codes to the NRU for the purposes of severe accident modelling is proposed.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.003 |
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".