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Record W4399361257 · doi:10.1080/00295639.2024.2328451

Improvements to the Baff-Refl Equivalence Technique Applied to Reflector Models in PWRs

2024· article· en· W4399361257 on OpenAlexafffundabout
Sami Machach, Alain Hébert, Aldo Dall’Osso

Bibliographic record

VenueNuclear Science and Engineering · 2024
Typearticle
Languageen
FieldEngineering
TopicNuclear reactor physics and engineering
Canadian institutionsPolytechnique Montréal
FundersFramatomePolytechnique Montréal
KeywordsEquivalence (formal languages)Nuclear engineeringReflector (photography)Materials sciencePhysicsMathematicsOpticsEngineering

Abstract

fetched live from OpenAlex

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.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.012
Threshold uncertainty score0.040

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.005
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.002
Open science0.0030.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0120.004

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.

Opus teacher head0.011
GPT teacher head0.218
Teacher spread0.207 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreMethods

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".

Quick stats

Citations2
Published2024
Admission routes3
Has abstractyes

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