Comparative assessment of small modular reactor Passive Safety System design via integration of dynamic methods of analyses
Bibliographic record
Abstract
Integral Pressurized Water Reactor (iPWR) type SMR designs were studied featuring Passive Safety Systems (PSS) in all cases. As many as 11 current SMR designs use PSS to remove decay heat. Variations in PSS designs were studied and compared using evaluation metrics and a proposed weighting method. This resulted in classification of iPWRs designs based on the methodology presented. A prototypic Passive Residual Heat Removal System (PRHRS) was then studied using a scaling analysis to compare the scaling ratio of system parameters, and failure probability relative to existing reference LWR plant data. The impact of single versus two-phase PRHRS designs was also considered. We found that a classical Probabilistic Risk Assessment (PRA) model describing active systems does not consider time evolution nor event ordering that a dynamic PRA approach can accommodate. We thus developed and realized basic coupling between LabVIEW as simulation code and CAFTA as PRA code. Coupling these codes using Python provides real-time simulation that leads to a dynamic simulation result. A representative difference in failure probability using dynamic versus classic PRA revealed that for one, there can be more component demands with different event ordering; thus providing insights into PSS failure probability in the iPWR-type SMR designs. The limitation of the work is essentially in the proprietary details of each SMR design. The value however is in the integrated method of system analysis.
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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 | 0.003 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".