FSN‐based cosimulation for fault propagation analysis in nuclear power plants
Bibliographic record
Abstract
The safety of Nuclear Power Plants (NPPs) is an important issue that is of concern to all including; regulators, operators, and the general public. Assuring the safety of a NPP is a primary objective by all stakeholders. In the wake of accidents in NPPs recorded in the past such as Chernobyl, TMI, and recently in Fukushima, the need to review existing safety system design and operation as well as performing safety verification of these systems as a means of preventing such accidents in the future is necessary. In this study, we present a framework for achieving safety verification of a NPP with emphasis on using cosimulation with reduced error for real time fault propagation analysis based on Fault Semantic Network in which a multiphysics model is mapped unto fault/risk models, and safety/protection systems of NPPs in order to achieve safety verification based on highest risks and previous accidents. A statistical method is used to reduce errors between simulation results and real time data which is illustrated with a case study from literature. © 2015 American Institute of Chemical Engineers Process Saf Prog 35: 53–60, 2016
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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".