Improvements to the Baff-Refl Equivalence Technique Applied to Reflector Models in PWRs
Bibliographic record
Abstract
A calculation module is developed for testing and validating the improved nodal equivalence techniques of reflectors for full-core nodal calculations. This module, BRISINGR, is a new implementation of the nodal expansion method developed by Delft University of Technology and Framatome, and has been inserted into the version 5 environment of Polytechnique Montréal, providing a fast prototyping setup used to assess the impact of different nodal equivalence approaches.We focus our investigations on an open-source implementation of the legacy equivalence technique Baff-Refl originating from the SCIENCE platform at Framatome. The proposed improvements to Baff-Refl are twofold: modification of the nodal equivalence procedure and modification of the reflector diffusion coefficients. We review the Nodal Expansion Method (NEM) and Analytical Nodal Method (ANM) for reflector calculations, the discontinuity factor (DF) renormalization, the DF decorrelation, the albedo calculation, and the procedure for obtaining few-group reflector diffusion coefficients from fine-group leakage coefficients.Our validation tests focus on the accuracy of the average nodal power of the fuel region in the downstream full-core calculation. A benchmark set of four two-dimensional 9 × 9 core configurations with Evolutionary Power Reactor-type assemblies with either steel or water reflectors was used for validation. The results on the core impact of the reflector model show that the Inscatter model for the calculation of diffusion coefficients improves the accuracy of the full-core power in all benchmark configurations. DF renormalization is another studied aspect of this paper, and has been shown to provide notable improvements. Actually, renormalization to assembly DFs provides better results than renormalization to 1, which is itself more preferable than none for accuracy. Finally, calculating reflector constants with ANM is shown to have no conclusive improvement over NEM.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 0.004 |
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".