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Multi-group cross section library generation for HTGR lattice physics code XPZ

2021· article· en· W3208818547 on OpenAlexaff
Lerui Zhang, Ding She, Lei Shi, Richard Chambon, Alain Hébert

Bibliographic record

VenueJournal of Physics Conference Series · 2021
Typearticle
Languageen
FieldEngineering
TopicNuclear reactor physics and engineering
Canadian institutionsPolytechnique Montréal
Fundersnot available
KeywordsNuclideNuclear dataCode (set theory)ComputationComputer scienceComputational scienceLattice (music)Source codeNuclear engineeringPhysicsNuclear physicsSet (abstract data type)AlgorithmProgramming languageEngineeringNeutron

Abstract

fetched live from OpenAlex

Abstract The XPZ code was previously developed for the lattice physics computation in High Temperature Gas-cooled Reactors (HTGRs), which adopted the multi-group cross section library converted from the existing open-source DRAGON library. In this paper, a new format of multi-group cross section library named XPZLIB has been implemented in XPZ code. XPZLIB is designed in binary and HDF5 formats, including detailed data contents for resonance, transport and depletion calculations. A new data-processing module named XPZR is developed based on NJOY-2016 to generate nuclide dependent XPZLIB from the most recent evaluated nuclear data, and besides, the PyNjoy-2016 system is developed for automatic generation of integrated XPZLIB including a complete set of nuclides. The new generated XPZLIB is presented with the XPZ code. Numerical results demonstrate the accuracy of the new library XPZLIB and the reliability of the data processing scheme. Moreover, the influence of different versions of ENDF/B data is investigated.

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: none
Teacher disagreement score0.711
Threshold uncertainty score0.713

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 communication0.0000.002
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 head0.038
GPT teacher head0.245
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 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

Citations1
Published2021
Admission routes1
Has abstractyes

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