Probabilistic Safety Assessment for Nugleab Power Plants Application at PHWR-CANDU And Possible Appltcations for Expert Systems
Bibliographic record
Abstract
The paper deals with the problem of probabilistic safety asaeeaement method (a brief presentation of the method, application at Shutdovn Cooling System at PHWR -CANDU 600 MW NPP) and PSA type knowledge utilisation for advisory systems for operational support. The first part of the paper presents the PSA method, which is an important improvement of the earlier methods (the Canadian method of safety matrix -SUM – and the American method of probabilistic risk evaluation -PRA) and it is the first part of a vast PSA project, which will be realised in Romania under the direct supervision of the IAEA specialists. The example in the paper refers to the TOP-EVENT, the Shutdovn Cooling System does not perform his function of the at removing from the core, after the reactor shutdown, in the normal operation mode, with full and pressurised primary circuit, (the heat removal presumes that in none of the core canals does not occur the boiling). For this Shutdown Cooling System fault tree generation after defining the TOP-EVENT, we described the system, then its interface systems (which are 14), the fault tree (and the hypotheses used in its generation) After the fault tree generation to the last allowen (by the available data) resolution level, we implemented the tree on the computer (using the PSA – PACK programmes), 804 we reduced it to the minimal cut-sets (formed by the base events), permitting to obtain the first qualitative results). The tree will be developed in the future to the last possible resolution level»in order to make opportune a quantitative analyse. Concerning the specific plant PSA type knowledge using in the Advisory Systems for Operational Support, we present some aspects of current status of these systems.
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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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.006 | 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".