Comparison of the Reactivity Effects Calculated by DRAGON and Serpent for a PHWR 37-Element Fuel Bundle
Bibliographic record
Abstract
Deterministic and Monte Carlo methods are regularly employed to conduct lattice calculations. Monte Carlo methods can effectively model a large range of complex geometries and, compared to deterministic methods, they have the major advantage of reducing systematic errors and are computationally effective when integral quantities such as effective multiplication factor or reactivity are calculated. In contrast, deterministic methods do introduce discretization approximations but usually require shorter computation times than Monte Carlo methods when detailed flux and reaction-rate solutions are sought. This work compares the results of the deterministic code DRAGON to the Monte Carlo code Serpent in the calculation of the reactivity effects for a pressurized heavy water reactor (PHWR) lattice cell containing a 37-element, natural uranium fuel bundle with heavy water coolant and moderator. The reactivity effects are determined for changes to the coolant, moderator, and fuel temperatures and to the coolant and moderator densities for zero-burnup, mid-burnup [3750 MWd/t(U)] and discharge burnup [7500 MWd/t(U)] fuel. It is found that the overall trend in the reactivity effects calculated using DRAGON match those calculated using Serpent for the burnup cases considered. However, differences that exceed the amount attributable to statistical error have been found for some reactivity effects, particularly for perturbations to coolant and moderator density and fuel temperature.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".