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Record W2888175836 · doi:10.5006/2866

Predicting Radionuclide Release Rates from Spent Nuclear Fuel Inside a Failed Waste Disposal Container Using a Finite Element Model

2018· article· en· W2888175836 on OpenAlexaff
Nazhen Liu, Ziyan Zhu, Linda Wu, Zack Qin, James J. Noël, David W. Shoesmith

Bibliographic record

VenueCORROSION · 2018
Typearticle
Languageen
FieldMaterials Science
TopicNuclear Materials and Properties
Canadian institutionsCanadian Nuclear LaboratoriesWestern University
Fundersnot available
KeywordsRadionuclideRadioactive wasteContainer (type theory)Waste managementSpent nuclear fuelHigh-level wasteEnvironmental scienceNuclear engineeringFinite element methodRadiochemistryMaterials scienceEngineeringChemistryStructural engineeringComposite materialNuclear physicsPhysics

Abstract

fetched live from OpenAlex

Models for the corrosion of spent nuclear fuel (fission and actinide-doped uranium dioxide) provide the essential source term for the release of radionuclides from within a failed nuclear waste container in a deep geologic repository. Although redox conditions within a repository are expected to be anoxic, exposure of the fuel to groundwater will cause the generation of oxidants at the fuel surface, leading to its corrosion and the release of radionuclides. The influence of these oxidants will be partially mitigated by the anoxic corrosion of the inner walls of the steel container to produce the oxidant scavengers, Fe2+ and H2. This review summarizes the development of a finite element model developed to determine the influence of the various redox-controlling species (H2O2, Fe2+, H2). Both one-dimensional and two-dimensional models are described, with the latter required to take into account the fractured geometry of the fuel.

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 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 score0.600
Threshold uncertainty score0.999

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)0.0010.001

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 head0.030
GPT teacher head0.252
Teacher spread0.222 · 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

Citations4
Published2018
Admission routes1
Has abstractyes

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