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Record W4404394455 · doi:10.1115/1.4067173

Numerical Analysis of Gas Generation and Migration in a Radioactive Waste Disposal Cell of a Deep Geological Repository

2024· article· en· W4404394455 on OpenAlexaffabout
Lan Sun

Bibliographic record

VenueJournal of Nuclear Engineering and Radiation Science · 2024
Typearticle
Languageen
FieldEngineering
TopicNuclear and radioactivity studies
Canadian institutionsCanadian Nuclear Laboratories
Fundersnot available
KeywordsRadioactive wasteWaste managementWaste disposalEnvironmental scienceVitrificationHigh-level wasteNuclear engineeringGeologyMaterials scienceNuclear physicsEngineering

Abstract

fetched live from OpenAlex

Abstract In a deep geological repository (DGR) for the long-term disposal of radioactive waste, gases (e.g., hydrogen (H2), carbon dioxide (CO2) and methane (CH4)) can be generated through a number of processes, such as corrosion of various metals and alloys and degradation of organic materials. If gas induced pressure exceeds the containment capacity of the engineered barrier systems (EBS) or the host rock, the gases could migrate through these barriers and potentially expose people and the environment to radiation. Therefore, a good understanding on the long-term performance of these barriers against gas migration is an important component in DGR design and safety assessment. In the present work, a numerical model has been developed to simulate the diffusion of CO2 (one of the gas species) in the near field of a DGR with the generation from related chemical reactions. The generation of CO2 was investigated to determine if it is a critical factor to impact the DGR safety under Canadian geological formations. A commercial computational fluid dynamics (CFD) code, ANSYS Fluent was used for the calculations. The model considered pH and temperature effects on CO2 migration under Canadian DGR geochemistry conditions.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.265
Threshold uncertainty score0.209

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
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 head0.005
GPT teacher head0.205
Teacher spread0.200 · 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 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

Citations1
Published2024
Admission routes2
Has abstractyes

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