A Case Study on the Normality of Monte-Carlo Simulation Results
Bibliographic record
Abstract
In order to address the risks associated with the operation of ageing pressure boundary components, many assessments incorporate probabilistic analysis methodologies for alleviating excessive conservatism of deterministic methodologies. In general, deterministic techniques utilize conservative upper bound values for all critical parameters. Equally, defense-in-depth assessments for the nuclear industry employ probabilistic methods in order to estimate potential risks associated with unanticipated events to demonstrate adequate margins associated with the licensed activity. Probabilistic approaches typically invoke the Monte-Carlo (MC) approach where a set of critical input variables, assumed independent, are randomly distributed and inserted in deterministic computer models. Estimates of results from probabilistic structural integrity assessments are then compared against assessment criteria, at times, based on the assumption that these results follow normal distributions. However, this assumption is not always valid, as normality depends both on the initially assumed distributions of the input variables and linearity, or lack thereof, of the deterministic model. In particular, the characteristic of a system function (either a linear or a non-linear system function) and the sampling region of input parameters affect the level of normality of the MC simulation results. As a proof of principle, a specific case study is presented. A system function is chosen based on the steady-state thermal creep of Zr-2.5Nb Pressure Tube (PT), instead of a full deterministic computational model, to show whether it can give rise to MC results that deviate from normality. The consequence of the deviation from normality when compared against assessment criteria is briefly discussed. It is noted that this study does not deal with analysis of Probabilistic Safety Assessments, also known as PSAs.
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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 | 0.012 | 0.047 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".