Comparison of probabilistic and deterministic error propagation calculations in DRAGON
Bibliographic record
Abstract
One of the major goal of lattice calculations is to evaluate cell homogenized and few group condensed cross sections for finite reactor calculations. However, there is no general provision in most lattice codes to take into account the impact of lattice property uncertainties (enrichment, temperature, density) on the final cross sections. Here we propose two different approaches to resolve this problem. The first approach is probabilistic in nature and relies on probability distribution functions to generate perturbations in the cell properties that can then be analyzed using the lattice code. The uncertainties in the cross section are then inferred from these calculations using a statistical analysis. The perturbative calculations can be performed using two different techniques: the direct technique where a new transport solution is obtained for each perturbation and the generalized perturbation theory (GPT) technique where the perturbed cross sections are evaluated approximately using perturbation theory methods. The second approach we will consider is deterministic and uses the GPT method to evaluate the sensitivity coefficients required for error propagation calculations. These two approaches are compared to assess their relative performance for error propagation calculations. Here fuel and coolant temperature perturbations for a simple PWR fuel pin are considered. Our analysis shows that the results, obtained using the deterministic approach, are very similar to the reference probabilistic results but with a considerable CPU gain. Using an approximate rather than an exact flux solution for the probabilistic approach has only a very small impact on the error predictions and has much smaller impact on the CPU requirements.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".