Effects of Filtered Containment Venting on Fission Product Releases During CANDU Reactor Severe Accidents
Bibliographic record
Abstract
Severe accidents are of increasing concern in the nuclear industry worldwide since the accidents at Fukushima Daiichi (March 2011). These events have significant consequences that must be mitigated to ensure public and employee safety. Filtered containment venting (FCV) systems are beneficial in this context as they would help to maintain containment integrity while also reducing radionuclide releases to the environment. This paper explores the degree to which filtered containment venting would reduce fission product releases during two Canada Deuterium Uranium (CANDU) 6 severe accident scenarios, namely a station blackout (SBO) and a large loss of coolant accident (LLOCA) (with limited emergency cooling). The effects on the progression of the severe accident and radionuclide releases to the environment are explored using the Modular Accident Analysis Program (MAAP)–CANDU integrated severe accident analysis code. The stylized filtered containment venting system model employed in this study avoids containment failure and significantly reduces radionuclide releases by 95–97% for non-noble gas fission products. Filtered containment venting is shown to be a suitable technology for the mitigation of severe accidents in CANDU, maintaining containment integrity and reducing radionuclide releases to the environment.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".