A three‐dimensional multiscale damage‐poroelasticity model for fractured porous media
Bibliographic record
Abstract
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.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".