Complex Dispersion in Simple Fractured Media
Bibliographic record
Abstract
[1] Previous studies noted situations with simple networks of fractured media where dispersion was complex. Certain network geometries provided principal directions of spreading at angles to the direction of mean flow and occasions where transverse spreading greatly exceeded longitudinal spreading. This article is concerned with understanding this complex dispersion in the context of more variable networks and to evaluate the scaling between dispersion and characteristics of the network. The approach involves modeling flow and particle transport through networks consisting of equally spaced finite fractures. Simulation trials examined how changes to the orientation of the overall network, fracture aperture, fracture connectivity, and fracture length influenced mass transport. With these geometries, particle spreading was due to a grid orientation effect and variability related to apertures and connectivity. The grid orientation effect inherently gives rise to the complex dispersion described in previous work. Spreading caused by variability in aperture and connectivity produces the more familiar, elliptical pattern of spreading. In the various model trials we created situations where either or both of these spreading mechanisms could dominate. When both grid orientation and heterogeneity effects were comparable, particle swarms took on a curious hybrid shape reflecting the outcome of two different spreading mechanisms. With sufficient heterogeneity the grid orientation effect was swamped and disappeared. As the block size increases so does transverse dispersion. Further studies are necessary to investigate whether the grid effects associated with the simplified fracture networks can be extrapolated to real fractured media.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| 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".