Uncertainty Propagation in UAM Time-Dependent Neutronics PWR Studies
Bibliographic record
Abstract
A subset of pressurized water reactor (PWR) studies from Exercise II-2 of the Uncertainty Analysis in Modeling for Light Water Reactors (UAM-LWR) benchmark study has been performed to quantify the importance of both nuclear data uncertainties and manufacturing uncertainties in an assembly depletion calculation and a mini-core rod movement transient. The depletion study of the 15 × 15 PWR assembly using the SAMPLER and TRITON modules of the SCALE code system revealed a maximum uncertainty in keff of 0.49% for fresh fuel, decreasing to 0.38% at the midpoint of the fuel cycle and rising back to 0.48% at the end of the fuel cycle. Uncertainties and correlations of various homogenized cross sections and other group constant data, such as keff, have been determined, and the effect of randomly applied manufacturing uncertainties was found to be largely negligible relative to nuclear data uncertainties for bulk lattice parameters. However, for local parameters, such as the pin power factors, assembly discontinuity factors, and diffusion coefficients, the effects from manufacturing uncertainties were appreciable and sometimes dominant.Nuclear data uncertainties were found to be the dominant contributors to uncertainty in the isotopic composition of the overall assembly, with the exception of very early in the fuel cycle, where manufacturing uncertainties such as perturbations to the fuel density and pin radius made nonnegligible contributions to total uncertainty. The contribution of manufacturing uncertainties to isotopic uncertainties was nonnegligible at a pin-by-pin level, but still smaller than the contributions from nuclear data uncertainty. Studies of the PWR mini-core rod movement transient using homogenized data from the SCALE models in the PARCS diffusion code showed little difference between the tested modeling approaches and demonstrated that nuclear data uncertainties dominated the manufacturing uncertainties in the global figures of merit considered, such as the equilibrium core boron concentration, the maximum core power factor, and the maximum reactivity insertion. For local effects, such as maximum pin power during the transient, the randomly applied manufacturing uncertainties were dominant. It was found in general that for global system properties, nuclear data uncertainties made significantly larger contributions to total uncertainty, whereas for local parameters the impact of manufacturing uncertainties was at least nonnegligible, and for some parameters, dominant.
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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 | 0.014 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".