Distinct element modeling of hydraulically fractured Lac du Bonnet granite
Bibliographic record
Abstract
The aim of this study is to better understand the mechanics of fracture development and propagation during hydraulic fracturing. This paper presents some development and applications of discrete particle modeling of this problem. A discontinuum modeling approach idealizes the material as separate particles bonded together at their contact points and utilizes the breakage of individual structural units or bonds to represent damage. The numerical models are correlated with existing hydrofracture laboratory experiments, which are presented in other publications. A simulation of a laboratory‐scale hydrofracture experiment and the acoustic emission (AE) data from the experiment is used to validate the synthetic AEs produced in the hydrofracture model. This technique has been used to examine the mechanics of fracture initiation and time and spatial distributions of AE. The modeling results demonstrate that the mechanism of hydraulically induced fracture in the Lac du Bonnet (LdB) granite core sample is predominantly tensile failure and that the shear cracks recorded in the hydrofracture experiment were due to slip on preexisting fractures. Numerical modeling of hydrofracture on homogeneous and heterogeneous synthetic samples seems to capture much of the behavior observed in the laboratory hydrofracture experiments.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".