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Record W3111371879 · doi:10.1002/nag.3170

A three‐dimensional multiscale damage‐poroelasticity model for fractured porous media

2020· article· en· W3111371879 on OpenAlexafffund
Mahdad Eghbalian, Mehdi Pouragha, Richard Wan

Bibliographic record

VenueInternational Journal for Numerical and Analytical Methods in Geomechanics · 2020
Typearticle
Languageen
FieldEngineering
TopicRock Mechanics and Modeling
Canadian institutionsCarleton UniversityUniversity of Calgary
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsPoromechanicsPorous mediumGeotechnical engineeringPorosityGeologyMaterials scienceMechanicsPhysics

Abstract

fetched live from OpenAlex

Abstract The paper investigates the failure of brittle rocks within a multiscale/multiphysics computational modeling framework so as to explicitly incorporate both microstructural and hydromechanical aspects in their overall (nonlinear) fracture behavior. Herein, the rock is idealized as a microfissured porous medium via a representative elementary volume (REV) containing distributed oblate spheroidal open microcracks and spheroidal nanopores at the lower scales. A two‐scale analytical homogenization procedure is then performed on the REV, assuming a saturated pore space across the scales. The end result is a microstructurally enriched continuum damage‐poroelasticity constitutive model within the generalized Biot's framework that inherits the underlying small‐scale characteristics. A set of damage tensors is derived based on the directional density distribution of microcracks that operate on both the poromechanical and hydraulic properties. Microstructural evolution is modeled following changes in microcrack length, aperture, and number density, including nanoporosity. To this end, a robust localization procedure is used that accurately captures the macroscopic softening due to microcracking events, leading to a nonlinear (but path‐independent) model. To investigate the baseline features of the model, including the salient effects of microcracking on induced anisotropy and deterioration of poroelastic properties, numerical results of material point‐based computations are discussed in detail. Finally, predictions of the current model are validated against experiments on Fontainebleau sandstone.

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)0.001
metaresearch head score (Gemma)0.001
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: Methods · Consensus signal: Methods
Teacher disagreement score0.369
Threshold uncertainty score0.777

Codex and Gemma teacher scores by category

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)0.0000.000

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 head0.056
GPT teacher head0.354
Teacher spread0.298 · 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
GenreMethods

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

Citations13
Published2020
Admission routes2
Has abstractyes

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