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Multi-dimensional transport modelling of corrosive agents through a bentonite buffer in a Canadian deep geological repository

2017· article· en· W2611518841 on OpenAlexafffundabout
Scott Briggs, Jennifer McKelvie, Brent E. Sleep, M. Król

Bibliographic record

VenueThe Science of The Total Environment · 2017
Typearticle
Languageen
FieldEnvironmental Science
TopicGroundwater flow and contamination studies
Canadian institutionsCanadian Institute for Advanced ResearchUniversity of TorontoYork University
FundersMitacsYork UniversityNuclear Waste Management Organization
KeywordsSpent nuclear fuelMultiphysicsRadioactive wasteBentoniteFlux (metallurgy)High-level wasteContainer (type theory)Environmental scienceNuclear engineeringCorrosionWaste managementMaterials scienceFinite element methodGeologyEngineeringMetallurgyGeotechnical engineeringComposite materialStructural engineering

Abstract

fetched live from OpenAlex

The use of a deep geological repository (DGR) for the long-term disposal of used nuclear fuel is an approach currently being investigated by several agencies worldwide, including Canada's Nuclear Waste Management Organization (NWMO). Within the DGR, used nuclear fuel will be placed in copper-coated steel containers and surrounded by a bentonite clay buffer. While copper is generally thermodynamically stable, corrosion can occur due to the presence of sulphide under anaerobic conditions. As such, understanding transport of sulphide through the engineered barrier system to the used fuel container is an important consideration in DGR design. In this study, a three-dimensional (3D) model of sulphide transport in a DGR was developed. The numerical model is implemented using COMSOL Multiphysics, a commercial finite element software package. Previous sulphide transport models of the NWMO repository used a simplified one-dimensional system. This work illustrates the importance of 3D modelling to capture non-uniform effects, as results showed locations of maximum sulphide flux are 1.7 times higher than the average flux to the used fuel container.

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 categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.847
Threshold uncertainty score1.000

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 studies0.0010.003
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.028
GPT teacher head0.225
Teacher spread0.197 · 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 designSimulation or modeling
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

Citations44
Published2017
Admission routes3
Has abstractyes

Explore more

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