Fluid flow in synthetic rough‐walled fractures: Navier‐Stokes, Stokes, and local cubic law simulations
Bibliographic record
Abstract
The results of three‐dimensional Navier‐Stokes (NS) and Stokes simulations and two‐dimensional local cubic law (LCL) simulations of fluid flow through single rough‐walled fractures are presented. Synthetic rough‐walled fractures were created by combining random fields of aperture and the mean wall topography or midsurface, which quantifies undulation about the fracture plane. A finite volume formulation of the LCL that incorporates geometric corrections for fracture undulation is presented. Simulations of fluid flow through planar fractures with sinusoidal variation in aperture were compared to published results. The rough‐walled fracture simulations demonstrated that the total flow rates predicted by the corrected LCL were within 10% of those predicted by the Stokes equations for all the fractures examined in this work. Differences between the NS and Stokes simulations clearly demonstrated that inertial forces can significantly influence the internal flow field within a fracture and the total flow rate across a fracture. By limiting the total flow rate differences between the NS and Stokes simulations, constraints for three kinematic parameters were determined. For all the fractures presented in this work, the corrected LCL was determined to be an acceptable approximation to the NS equations, provided that the kinematic and geometric constraints were met.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".