Assessment of Filtered Containment Venting Strategies for Mitigating Severe Accident Consequences at a Generic Single-Unit CANDU
Bibliographic record
Abstract
Abstract In many jurisdictions, filtered containment venting systems are an integral part of severe accident management guidelines but are normally only used as a last resort, to be manually activated when the containment pressure approaches the containment failure limit. This approach fails to utilize the capability of the filtered venting system to better control containment pressure throughout an accident and to create capacity for possible future pressurization based on the accident progression. In this paper, smart venting strategies are explored, using a generic single-unit Canada Deuterium Uranium (CANDU) (660 MWel) plant model in MAAP-CANDU, as a means to mitigate the consequences of a severe accident. These strategies are able to account for both current conditions as well as possible future pressurization. The use of strategic venting offers an improvement in maintaining containment integrity, as well as reducing I-131 releases to the environment by about 10% to 31% compared to last-resort venting while being feasible for manual operator actions. Overall, this comprehensive analysis demonstrates the capability of a smart venting strategy paired with a filtered containment venting system as an active mitigation tool.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".