Comparative assessment of small modular reactor Passive Safety System design via integration of dynamic methods of analyses
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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 it