Controlled Lab-Scale Evaluation of the Secondary Permeability Represented in a 3D Printed Discrete Fracture Network (DFN) Model
Bibliographic record
Abstract
Abstract The evaluation of fluid flow through fractured media is essential for many applications. In hard rocks, fluid flow depends on fracture aperture and connectivity, as fractures are the preferential flow paths within the rock mass. Previous research studied fluid flow, using Discrete Fracture Networks (DFN) and numerical modelling methods, with fewer lab-scale experiments. Advancements in 3D printing technology allows for generating valuable lab-scale physical models representing fractured media. In this work, a DFN model is built using the DFN software MoFrac and a 3D physical model is generated with a 3D printer. The 3D printed DFN model is fixed in an experimental set-up, which functions as a differential pressure meter by restricting airflow through a transition duct. The objectives of the experiment are to establish the behavior of the changing pressure to fluid flow through fractures. This laboratory experiment is part of an ongoing research project investigating the constructability of a Natural Heat Exchange Engineering Technology system. This system uses natural means to provide economically significant thermal regeneration capacity through a volume of rocks for ventilating mine workings. The major contribution of the lab-scale experiment is to verify whether the secondary permeability of a rockmass can admit sufficient flow.
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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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".