A method to discretize non‐planar fractures for 3D subsurface flow and transport simulations
Bibliographic record
Abstract
Abstract A method is presented to discretize inclined non‐planar 2D fractures within a 3D finite element grid for subsurface flow and transport simulations. Each 2D fracture is represented as a triangulated surface. Each triangle is then discretized by 2D fracture elements that can be horizontal, vertical or inclined and that can be triangular or rectangular. The 3D grid representing a porous rock formation consists of hexahedra and can be irregular to allow grid refinement. An inclined fracture was discretized by (a) inclined triangles and (b) orthogonal rectangles and flow/transport simulations were run to compare the results. The comparison showed that (i) inclined fracture elements must be used to simulate 2D transient flow, (ii) results of 2D/3D steady‐state and 3D transient flow simulations are identical for both discretization methods, (iii) inclined fracture elements must be used to simulate 2D/3D transport because orthogonal fracture elements significantly underestimate concentrations, and (iv) orthogonal elements can be used to simulate 2D/3D transport if fracture permeability is corrected and multiplied by the ratio of fracture surface areas (orthogonal to inclined). Groundwater flow at a potential site for long‐term disposal of spent nuclear fuel was simulated where a complex 3D fracture network was discretized with this technique. The large‐scale simulation demonstrates that the proposed discretization procedure offers new possibilities to simulate flow and transport in complex 3D fracture networks. The new procedure has the further advantage that the same grid can be used for different realizations of a fracture network model with no need to regenerate the grid. Copyright © 2007 John Wiley & Sons, Ltd.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".