DEVELOPMENT OF A HYDROGEN MANAGEMENT CONCEPT FOR THE CANADIAN SUPERCRITICAL WATER-COOLED REACTOR
Bibliographic record
Abstract
Accidental hydrogen production in nuclear reactors has been a significant focus of nuclear reactor safety for decades. However, since the accident at Fukushima Daiichi nuclear generating station, hydrogen safety in nuclear reactors is a more relevant topic. As new reactor concepts, such as the supercritical water-cooled reactor (SCWR), are designed and developed the risk of unintentional hydrogen generation is not eliminated; however, it can be mitigated in the design. A systematic assessment of the hydrogen risk from both normal and accident conditions in the Canadian SCWR design was performed, in which various techniques to mitigate the hydrogen combustion potential were considered. While the rate of hydrogen generation under normal operating conditions was found to be low when held at supercritical water conditions, conservative estimates suggest that a significant quantity of hydrogen may be produced and released to the containment building in a severe accident. As a result, a hydrogen–oxygen management concept has been proposed to mitigate the hydrogen produced in a severe accident that includes a nitrogen-inerted containment building to reduce the combustion potential of hydrogen and the installation of passive autocatalytic recombiners for oxygen management. This hydrogen–oxygen management concept results in significant design changes and likely significant economic and operational impacts on the Canadian SCWR design.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".