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Record W2474932375

Evaluation of the Importance of Thermo-hydro-mechanical Couplings On the Performance Assessment of a Deep Underground Storage Design

2003· article· en· W2474932375 on OpenAlexaff
A. Millard, A. Rejeb

Bibliographic record

Venue10th ISRM Congress · 2003
Typearticle
Languageen
FieldEngineering
TopicSoil and Unsaturated Flow
Canadian institutionsAtomic Energy (Canada)
Fundersnot available
KeywordsSaturation (graph theory)MoistureShrinkageGeotechnical engineeringAllowance (engineering)Pore water pressureEnvironmental scienceWater contentStress (linguistics)ThermalPorous mediumPorosityMaterials scienceEngineeringMechanical engineeringComposite materialThermodynamics
DOInot available

Abstract

fetched live from OpenAlex

An evaluation of the importance of the thermo-hydro-mechanical couplings (THM) on the performance assessment of a deep underground storage design has been made as part of the international DECOVALEX III project. It is a numerical study that simulates a generic repository configuration in the near field in a granite medium. The thermo-hydro-mechanical evolution of the whole configuration is simulated over a period of 100 years. The model used to represent the unsaturated behaviour of the various porous media makes allowance for moisture transfers through the effect of thermal and water gradients. The paper presents a comparison of the temperature, water pressure and stress fields obtained by TM, TH, HM and THM coupled calculations. The results demonstrate that temperature is hardly affected by the couplings. In contrast the influence of the couplings on the mechanical stresses is considerable. This is attributed to the key role that water has on bentonite swelling or shrinkage effects that are dependent on its saturation level variations.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.002
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
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)0.0010.000

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 head0.034
GPT teacher head0.265
Teacher spread0.232 · 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 source (direct Gemma or distilled Codex), 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

Citations0
Published2003
Admission routes1
Has abstractyes

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