A dual fracture model to simulate large-scale flow through fractured rocks
Bibliographic record
Abstract
Discrete fracture network models can be used to study groundwater flow in fractured rock masses. However, one may find that it is not easy to apply such models to practical projects as it is difficult to investigate every fracture and measure its hydraulic parameters. To overcome such difficulties, a dual fracture model is proposed. Taking into account the hydraulic characteristics of the various elements of the fracture system, a hydrogeological medium is assumed to consist of two components: the dominant fracture network and the fractured rock matrix. As the dominant fracture network consists of large fractures and faults, it controls the groundwater flow in rock masses. Depending on the permeabilities of the in-fill materials, these fractures and faults may serve as channels or barriers of the flow. The fractured rock matrix, which includes rock blocks and numerous small fractures, plays a secondary role in groundwater flow in such medium. Although the small fractures and rock blocks possess low permeability, their numbers and their total porosity are relatively large. Therefore, they provide large volume for groundwater storage. In this paper, the application of the proposed model to simulate the groundwater flow for a hydropower station before and after reservoir storage is reported. The implications of the results on the design of the station are also highlighted.Key words: seepage flow, dual fracture model, dominant fracture, fractured rock matrix, case studies, rock-filled dam.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".