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Record W2969740371 · doi:10.2172/1557508

Depletion, Chemical Reaction and Transport in High Burnup Nuclear Fuel

2019· report· en· W2969740371 on OpenAlexaff
Srdjan Simunovic, Theodore M. Besmann, Emily E. Moore, Kevin Clarno, William Wieselquist, Jake Mcmurray, M.H.A. Piro

Bibliographic record

Venuenot available
Typereport
Languageen
FieldMaterials Science
TopicNuclear Materials and Properties
Canadian institutionsOntario Tech University
FundersUT-BattelleBattelleU.S. Department of Energy
KeywordsBurnupThermochemistryNuclear engineeringNuclear fuelUranium dioxideSpent nuclear fuelChemistryEnvironmental scienceUraniumNuclear physicsPhysicsEngineeringPhysical chemistry

Abstract

fetched live from OpenAlex

We have developed a formulation and computational model for oxygen transport in Light Water Reactor (LWR) uranium dioxide fuel. The overall model couples the burnup simulation isotopic composition with a thermochemistry model of the fuel phase, and oxygen transport using the driving forces from the thermodynamic calculations. The diffused oxygen is accounted for in the thermochemistry model in order to establish consistent material and thermodynamic conditions in the fuel undergoing burnup. The model has been implemented in the nuclear fuel performance code Bison. The formulation and the model have been successfully demonstrated on fuel burnup data from the open literature. The developed capability enables consideration of complex chemical composition of irradiated fuels beyond available burnup models in Bison.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.945
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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)0.0040.001

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.

Opus teacher head0.021
GPT teacher head0.232
Teacher spread0.211 · 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.

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

Citations2
Published2019
Admission routes1
Has abstractyes

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