An efficient method for discretizing 3D fractured media for subsurface flow and transport simulations
Bibliographic record
Abstract
Abstract We introduce a new method to discretize inclined non‐planar two‐dimensional (2D) fractures in three‐dimensional (3D) fractured media for subsurface flow and transport simulations. The 2D fractures are represented by ellipsoids. We first discretize the fractures and generate a 2D finite element mesh for each fracture. Then, the mesh of fractures is analyzed by searching and treating critical geometric configurations. Based on that search, the method generates a quality mesh and allows for including finer grids. A solute transport problem in fractured porous media is solved to test the method. The results show that the method (i) adequately represents the fractured domain by maintaining the geometric integrity of input surfaces and geologic data, (ii) provides accurate results for both simple and complex fractured domains, (iii) is insensitive to spatial discretization, and (iv) is computationally very efficient. For inclined and vertical fractures, analytical and numerical solutions are shown to be in good agreement. The method is therefore suitable to discretize fracture networks for flow and transport simulations in fractured porous media. Copyright © 2010 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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.003 | 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".