Computational Homogenization of Nonlinear Hydromechanical Coupling in Poroplasticity
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".