Multiscale Roughness Influence on Conservative Solute Transport in Self-affine Fractures
Bibliographic record
Abstract
Abstract. In this article, the influence of multiscale roughness on transport of a conservative solute through a self-affine fracture was investigated. The fracture roughness was decomposed into two different scales (i.e., a small-scale stationary secondary roughness superimposed on a large-scale non-stationary primary roughness) by a wavelet analysis technique. The fluid flow in the single fracture was characterized by Forchheimer's law and exhibited nonlinear flow features such as eddies and tortuous streamlines. The results indicated that the small-scale secondary roughness was primarily responsible for the nonlinear flow features. Numerical simulations of asymptotic conservative solute transport showed non-Fickian transport characteristics (i.e., early arrivals and long tails) in breakthrough curves (BTCs) and in residence time distributions (RTDs) with and without consideration of the secondary roughness. Analysis of multiscale BTCs and RTDs showed that the small-scale secondary roughness played a significant role in enhancing the non-Fickian transport characteristics. Removing small-scale secondary roughness delayed the arrival time and shortened the tail. The peak concentrations in BTCs decreased as the secondary roughness was removed, implying that the secondary roughness could also enhance the solute dilution. Fitting the one-dimensional (1D) Fickian advection-dispersion equation (ADE) to the numerical BTCs resulted in considerable errors that decreased with the small-scale secondary roughness being removed. The 1D mobile-immobile model (MIM) provided a better fit to the numerical BTCs and inclusion of the small-scale secondary roughness in numerical simulations resulted in a decreasing MIM mobile domain fraction and an increasing mass exchange rate between immobile and mobile domains.
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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 | 0.000 |
| 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".