The Impact of Fueling Operations on Full Core Uncertainty Analysis in CANDU Reactors
Bibliographic record
Abstract
Abstract Within emerging best-estimate-plus-uncertainty (BEPU) approaches, code output uncertainties can be inferred from the propagation of fundamental or microscopic uncertainties. This paper examines the propagation of fundamental nuclear data uncertainties though the entire analysis framework to predict macroscopic reactor physics phenomena, which can be measured in Canada Deuterium Uranium (CANDU) reactors. In this work, 151 perturbed multigroup cross sections libraries, each based on a set of perturbed microscopic nuclear data, were generated. Subsequently, these data were processed into few-group cross sections and used to generate full-core diffusion models in PARCS. The impact of these nuclear data perturbations leads to changes in core reactivity for a fixed set of fuel compositions of 4.5 mk. The impact of online fueling operations was simulated using a series of fueling rules, which attempted to mimic operator actions during CANDU operations such as studying the assembly powers and selecting fueling sites, which would minimize the deviation in power from some desirable reference condition or increasing or decreasing fueling frequency to manage reactivity. An important feature of this analysis was to perform long-transients (1–3 years) starting with each one of the 151 perturbed full core models. It was found that the operational feedback reduced the standard deviation in core reactivity by 99% from 0.0045 to 2.8 × 10−5. Overall, the conclusions demonstrate that while microscopic nuclear data uncertainties may give rise to large macroscopic variability during simple propagation, when important macrolevel feedback are considered the variability is significantly reduced.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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 teacher head, 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".