PWR assembly transport calculation: A validation benchmark using DRAGON, PENTRAN, and MCNP
Bibliographic record
Abstract
This paper presents a 2D PWR fuel assembly benchmark performed with 3 transport codes: DRA- GON which uses the collision probability method, PENTRAN, an Sn transport code, and MCNP, a Monte Carlo code. First, DRAGON was used to produce a 2-group pin-by-pin cross-section library associated with 45 materials that describe the fuel assembly. Using the same library, it was then possible to perform comparisons between DRAGON and MCNP, and between PENTRAN and MCNP. Here, MCNP was considered as the reference multigroup Monte Carlo tool used to validate the deterministic codes. This type of 2-group benchmark can be utilized to evaluate the performance of different solvers using the very same cross-sections. The transport solutions provided here may be used as references for further comparisons with industrial reactor core codes using a diffusion or a SPn solver, and generally relying on 2-group cross-sections. Results show an excellent overall agreement between the 3 codes, with discrepancies that are less than 0.5% on the pin-by-pin flux, and less than 20 pcm on the k e . Therefore, it may be concluded that these deterministic codes are reliable tools to perform criticality transport calculations for PWR lattices. Moreover, the use of multigroup Monte Carlo appears as an efficient independent technique to per- form detailed code to code comparisons relying on the same cross-section library. The present work may be considered as the first step of a 3D PWR core benchmark using DRAGON generated cross-sections and comparing PENTRAN and MCNP multigroup calculations.
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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 | 0.003 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".