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Record W3132156621 · doi:10.1051/epjconf/202124702009

VALIDATION OF THE POLARIS CANDU EXTENSION FOR LATTICE PHYSICS

2021· article· en· W3132156621 on OpenAlexaff
Simon Younan, D. R. Novog

Bibliographic record

VenueEPJ Web of Conferences · 2021
Typearticle
Languageen
FieldEngineering
TopicNuclear reactor physics and engineering
Canadian institutionsMcMaster University
Fundersnot available
KeywordsPhysicsElectromagnetic shieldingSolverLattice (music)Nuclear dataNuclear engineeringComputational physicsStatistical physicsNuclear physicsMathematicsEngineeringNeutronMathematical optimizationQuantum mechanics

Abstract

fetched live from OpenAlex

Polaris is a new lattice physics package, introduced in version 6.2 of the SCALE package. It uses a method of characteristics transport solver and the embedded self-shielding method. It is able to model light water reactor systems with a minimal amount of input. The goal of this project is to include support for CANDU models in Polaris for the next version of SCALE. So far, the model has been implemented and shown to give results with reasonable agreement to other SCALE sequences. This study extends the model to a reflector model, and shows that most quantities agree well with other codes. Some quantities, such as k eff and assembly discontinuity factors, are sensitive to meshing. This study also performs a correlation between the TRITON and Polaris sequences using Sampler to perturb the nuclear data. Overall, there is good agreement between the two codes, though coolant void reactivity is only moderately correlated, likely due to the differences in resonance self-shielding methods. Additionally, this work shows that a coarser mesh can be used to speed up uncertainty calculations compared to the mesh used for a best estimate. Finally, this work shows that the mass lumping feature in CENTRM significantly affects heavy water moderated calculations, whether using TRITON or calculating self-shielding factors, and thus should be disabled for heavy water calculations.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.115
Threshold uncertainty score0.173

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.022
GPT teacher head0.221
Teacher spread0.199 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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

Citations0
Published2021
Admission routes1
Has abstractyes

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