Simulation of diffusive solute transport in heterogeneous porous media with dipping anisotropy
Bibliographic record
Abstract
Dipping anisotropy is a common feature in heterogeneous porous media that can substantially affect solute transport. For problems with complex geometry the influence of dipping anisotropy must be analyzed using numerical models, since suitable analytical solutions are not available. The most straightforward approach is to use a Cartesian coordinate system aligned with the material coordinate system. However, this approach is usually not practical, especially in 3D simulation domains with dipping layers and heterogeneous material properties. Furthermore, in the case of diffusion-dominated transport, the effect of anisotropy is often neglected. In this research, a general-purpose, fully 3-D unstructured grid code was developed to simulate diffusion-dominated solute transport in systems with dipping anisotropy, while accounting for complex geometry. The code has been verified against both 2-D and 3-D analytical solutions and has then been applied to two anisotropic diffusion problems, including an in-situ diffusion experiment and a hypothetical deep geologic repository, respectively. The simulation results indicate that consideration of anisotropy is required if the solute distribution in the rock matrix is of importance, in particular for assessing long-term evolution in layered systems. The formulation presented provides a versatile method for assessing diffusion-dominated solute transport in systems with dipping anisotropy subject to complex geometry.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| 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".