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Computational Homogenization of Nonlinear Hydromechanical Coupling in Poroplasticity

2006· article· en· W2023061705 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueInternational Journal for Multiscale Computational Engineering · 2006
Typearticle
Languageen
FieldComputer Science
TopicAdvanced Mathematical Modeling in Engineering
Canadian institutionsASTER
Fundersnot available
KeywordsHomogenization (climate)Nonlinear systemMicroscale chemistryMicromechanicsMechanicsPorous mediumConstitutive equationDissipative systemGalerkin methodDiscretizationComputationDissipationClassical mechanicsMaterials scienceMathematical analysisMathematicsFinite element methodPhysicsGeotechnical engineeringPorosityGeologyThermodynamics

Abstract

fetched live from OpenAlex

In this paper, we propose a new two-scale model of fluid-saturated elastoplastic porous media based on micromechanical considerations. A formal nonlinear homogenization procedure using asymptotic expansion techniques is adopted to up-scale the microscopic constitutive behavior of an elastoplastic solid coupled with the movement of a Stokesian fluid. Considering the yield criterion at the microscale governed by the Mohr-Coulomb function and that the plastic deformation obeys the principle of maximum dissipation, we build up, computationally, a sharper macroscopic yield criterion and provide precise two-scale computations for the effective parameters of the homogenized medium. Within this context, we show that the homogenized results incorporate additional features inherent to the nonlinear hydromechanical coupling that have been overlooked by the purely macroscopic approaches. Variational principles along with the corresponding Galerkin approximations are proposed to discretize the local nonlinear closure problems leading to numerical effective constitutive laws. The influence of the new constitutive features obtained at the Darcy-scale effective model is propagated to the field-scale and illustrated numerically in a example of land subsidence caused by oil extraction of a weak heterogeneous reservoir with hydraulic conductivity characterized by long-range correlations displaying fractal character.

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.

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: Methods · Consensus signal: none
Teacher disagreement score0.466
Threshold uncertainty score0.758

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 studies0.0000.000
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 head0.011
GPT teacher head0.263
Teacher spread0.253 · 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