Reliability and Safety Assessment of a Passive Containment Cooling System in Advanced Heavy Water Reactors
Bibliographic record
Abstract
Passive Safety Systems (PSSs), which rely on natural forces and processes, such as natural circulation, gravity, internal stored energy, etc., are increasingly utilized in generation 3+ and generation 4 advanced nuclear power plants to increase inherent safety features of the nuclear reactor design.Although PSSs should considerably increase the safety of nuclear power plants, it is still challenging to systematically assess the reliability of passive systems because of the lack of data and uncertainties associated with phenomenon involving natural forces that underlies their safety functions.In this study, the Fault Tree Analysis (FTA) was used to assess the reliability and safety of the Passive Containment Cooling System (PCCS) in Advanced Heavy Water Reactor (AHWR).The failure probability of PCCS was calculated from the failure probabilities of Basic Events (BEs).Using the data for the failure probabilities of Top Event (TE) and BE from the FTA model, two Artificial Neural Network (ANN) models were proposed for the reliability analysis of PCCS to supplement the FTA model.Rectified Linear Unit (ReLU) and Sigmoid activation functions were utilized to build ANN models, and an Adaptive moment estimation (Adam) optimizer was used to train the ANN models to make these models computationally efficient.The results of the FTA model were compared with the predictions of the ANN models to find out the ANN model performance.
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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 | 0.000 |
| 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) | 0.000 | 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".