Quadrilateral Mesh-Based Reactive Transport Modeling in Non-Orthogonal Random Fracture-Matrix Systems
Bibliographic record
Abstract
Reactive transport modeling (RTM) is a critical tool for understanding complex fluid-rock interactions in fractured rock systems. However, applying RTM in such systems remains challenging due to the wide disparity of scales spanning from millimeters to kilometers. While existing RTM codes effectively address fracture-matrix interactions at smaller scales, few can handle irregular fracture networks on larger scales while explicitly incorporating matrix processes. To bridge this gap, we introduce a novel discrete fracture-matrix (DFM) reactive transport model based on the MIN3P code, utilizing an anisotropic quadrilateral mesh. This approach enables coarser discretization along fractures (advection-dominated zones) and refined discretization perpendicular to fractures (diffusion-dominated zones), significantly improving computational efficiency without compromising accuracy. The model’s capabilities are demonstrated through simulations of conservative tracer transport and dissolved oxygen migration in fractured crystalline rock at both intermediate (hundred-meter) and large (kilometer) scales. Comparative analyses with traditional triangular mesh methods reveal that the proposed approach achieves comparable or superior accuracy while drastically reducing computational demands. The model’s ability to efficiently simulate large-scale fractured rock systems makes it a powerful tool for applications such as assessing geochemical stability in crystalline rock with extensive fracture networks.
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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 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".