Bibliographic record
Abstract
Fault trees and event trees have been widely accepted as the modeling strategy to perform Probabilistic Risk Assessment (PRA). However, there are several limitations associated with fault tree/event tree modeling. These include 1. It only considers binary events; 2. It assumes independence among basic events; and 3. It does not consider timing sequence of basic events. This thesis investigates Petri net modeling as a potential alternative for PRA modeling. Petri nets have mainly been used as a simulation tool for queuing and network systems. However, it has been suggested that they could also model failure scenarios, and thus could be a potential modeling strategy for PRA. In this thesis, the transformations required to model logic gates in a fault tree by Petri nets are explored. The gap between fault tree analysis and Petri net analysis is bridged through gate equivalency analysis. Methods for qualitative and quantitative analysis for Petri nets are presented. Techniques are developed and implemented to revise and tailor traditional Petri net modeling for system failure analysis. The airlock system and the maintenance cooling system of a CANada Deuterium Uranium (CANDU) reactor are used as case studies to demonstrate Petri nets ability to model system failure and provide a structured approach for qualitative and quantitative analysis. The minimal cutsets and the probability of the airlock system failing to maintain the pressure boundary are obtained. Furthermore, the case study is extended to non-coherent system analysis due to system maintenance.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".