A Dynamic Three‐Field Finite Element Model for Wave Propagation in Linear Elastic Porous Media
Bibliographic record
Abstract
ABSTRACT A three‐field finite element (FE) model for dynamic porous media considering the de la Cruz and Spanos (dCS) theory is presented. Due to fluid viscous dissipation terms, wave propagation in the dCS theory yields an additional rotational wave compared to Biot (BT) theory. In addition, introducing porosity as a dynamic variable in the dCS model allows solid‐fluid nonreciprocal interactions. Due to the volume‐averaging technique, the dCS model further accounts for a macroscopic shear modulus and adds a new macroscopic constant. The porous media governing equations are formulated in terms of solid displacement, fluid pressure, and fluid displacement. Space and time convergence rates for the FE dCS model are demonstrated in a one‐dimensional case. A dimensionless analysis performed in the dCS framework led to negligible differences between BT and dCS models except when assuming high fluid viscosity. Domains with small characteristic lengths resulted in BT and dCS damping terms in the same order of magnitude. One‐ and two‐dimensional examples showed that dCS nonreciprocal interactions and the macroscopic shear modulus are responsible for modifying wave patterns. A two‐dimensional injection well simulation with water and slickwater showed higher wave attenuation for the latter. High frequencies in dCS model were noticed to yield more significant changes in wave patterns. The numerical results highlight the contributions of the dCS porous media model and its importance in simulations of laboratory scale experiments, ultrasonic frequencies, and highly viscous fluids.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".