Radial flow in an anisotropic aperture field in a rough-walled fracture
Bibliographic record
Abstract
Preferential flow channels with low hydraulic resistance in a single fracture can transmit large fluid volumes in fractured rock. Characterizing these channels and the associated flow channeling at the field scale, however, remains challenging. In this study, a hybrid iterative computational workflow was used to generate a field-scale (30 m diameter) synthetic fracture with self-affine surfaces and a high-resolution (2 mm spatial resolution) aperture field. We investigate the effects of anisotropic surface roughness, shearing displacement, and closure ratio on flow channeling in a radial flow system using advective Lagrangian particle tracking and advection–dispersion numerical simulations. Results show a transition from local microscale channeling effects to a homogenous flow pattern at the field-scale within rough-walled fractures. The transition is enhanced by the increasing microscale channels which occur via surface roughness anisotropy. Surface shearing displacement and anisotropic surface roughness control the fracture closure area and principal orientations, aligning with the tortuosity and direction of preferential flow pathways. The solute peak arrival times in synthetic fractures are similar to those in smooth fractures with an equivalent Cubic Law aperture. This demonstrates the applicability of the Cubic Law aperture in predicting bulk arrival times, even in the presence of flow channeling within sheared synthetic fractures. The strong flow channeling behavior observed in some field studies is therefore likely related to local geological conditions (stress included) and post-fracturing processes, underscoring the need to integrate initial fracturing and subsequent weathering history obtained using rock sample properties and outcrop analysis when investigating channeling effects.
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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 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".