Formal Probabilistic Risk Assessment of a Nuclear Power Plant
Bibliographic record
Abstract
Functional Block Diagrams (FBD) are commonly used as a graphical representation for probabilistic risk assessment in a wide range of complex engineering applications. An FBD models the stochastic behavior and cascading dependencies of system components or subsystems. Within FBD-based safety analysis, Event Trees (ET) dependability modeling techniques are typically used to associate all possible risk events to each subsystem. In this paper, we conduct the formal modeling and probabilistic risk assessment of a nuclear power plant in the HOL4 theorem prover. Using an FBD modeling in HOL4 of the nuclear Boiling Water Reactor (BWR), we formally determine all possible classes of accident events that can occur in the BWR. We compare our formal analysis in HOL4 with those obtained analytically and by simulation using Matlab and the specialized Isograph tool. Experimental results showed the superiority of our approach in terms of scalability, expressiveness, accuracy and CPU time.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".