Results and insight from interim seismic margin assessment of the Advanced CANDU Reactor (ACR) 1000® reactor
Bibliographic record
Abstract
The ACR-1000™ reactor developed by Atomic Energy of Canada Limited (AECL) is a 1200-MWe-classlight water-cooled, heavy-water-moderated pressure-tube reactor, which has evolved from the wellestablished CANDU™ line of reactors.It retains the basic, proven, CANDU design features while incorporating innovations and state-of-art technologies to ensure fully competitive safety, operation, performance and economics.The objective of this paper is to describe the seismic margin assessment (SMA) performed for the ACR-1000 reactor at full power operation.The ACR-1000 reference design basis earthquake (DBE) is 0.3g peak ground acceleration (PGA).The seismic margin was assessed, and potential seismic failure modes as well as weak component links/functionality leading to sever core damage and widespread fuel damage were identified.The Level I internal event at-power PSA models were reviewed and the systems required to bring a plant from a normal operation to a safe shutdown were identified in the seismic safe shutdown equipment list (SSEL).In the first approach, seismic capacities of the items on the SSEL have been developed using the ACR seismic design criteria and qualification criteria, past seismic experience and recent seismic probabilistic safety analyses and seismic margin assessments.The plant responses to seismic events were modelled in seismic event trees, from which the accident sequences potentially leading to severe core damage and widespread fuel damage were identified.These accident sequences determined a combination of the failures of frontline safety systems.There are dependencies between frontline systems and their support systems, and among support systems.These dependencies were included appropriately using system dependency matrices.Then, the accident-sequence seismic capacities were estimated from the seismic failures of structures or components resulting in failures of frontline systems and their support systems in terms of high confidence of low probability of failure (HCLPF).The plant HCLPF capacities for severe core damage and widespread fuel damage were then determined.This assessment demonstrates that the ACR-1000 design can reasonably achieve a seismic margin in terms of the plant HCLPF that is equal to or exceeding 0.5g PGA.Therefore the ACR-1000 design is capable of safe shutdown in response to a strong magnitude earthquake.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 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".