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Record W3173077900 · doi:10.1051/epjconf/202124911010

Anisotropic nature of the capillary stress tensor

2021· article· en· W3173077900 on OpenAlexaff
Mojtaba Farahnak, Richard Wan, Mehdi Pouragha

Bibliographic record

VenueEPJ Web of Conferences · 2021
Typearticle
Languageen
FieldEngineering
TopicGranular flow and fluidized beds
Canadian institutionsCarleton UniversityUniversity of Calgary
Fundersnot available
KeywordsCapillary actionDimensionless quantityCauchy stress tensorAnisotropyStress (linguistics)MechanicsShear stressTensor (intrinsic definition)Viscous stress tensorEffective stressMaterials sciencePhysicsClassical mechanicsGeotechnical engineeringMathematicsGeometryGeologyComposite materialOptics

Abstract

fetched live from OpenAlex

The paper describes a micromechanical approach that explores the anisotropic nature of the capillary stress tensor and its evolution in pendular granular materials via Discrete Element Modeling (DEM) simulations. Dimensionless parameters are used to address the conditions under which the contribution of capillarity (or cohesive interparticle forces) to the stress transmission within a Representative Elementary Volume (REV) is expected to be considerable. From a series of suction-controlled conventional triaxial tests, numerical results show that the significance of the capillary stress and the relative magnitude of its mean to deviatoric components is directly connected to the characteristic particle size and applied stress. In addition, it is shown that the anisotropic character of the capillary stress tensor intensifies with increasing suction. Furthermore, a simple shear test is conducted at constant mean stress to reveal the development of deviatoric capillary stresses in the absence of any change in mean stress, which cannot be captured by the commonly used Bishop’s stress expression.

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)0.000
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: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0010.000
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 head0.007
GPT teacher head0.201
Teacher spread0.194 · 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 designTheoretical or conceptual
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
Published2021
Admission routes1
Has abstractyes

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Same venueEPJ Web of ConferencesSame topicGranular flow and fluidized bedsFrench-language works237,207