Numerical Modeling of the Recession and Closure of Planar Hydraulic Fractures: Contact‐Based Versus Asymptotic‐Informed Schemes
Bibliographic record
Abstract
ABSTRACT We compare two algorithms to simulate the propagation, arrest, recession, and closure of a planar hydraulic fracture, focusing on their ability to capture the physical processes governing fracture recession and closure. The first algorithm is based on a fixed grid with contact detection during recession, while the second is based on a moving mesh that tracks the receding front using the appropriate receding near‐tip asymptotics. We assess the adequacy of fixed‐grid contact logic in simulating fracture recession, and evaluate the generality of the so‐called sunset solution for fracture closure, originally derived for plane‐strain and radial fractures and homogeneous properties of the elastic medium. Our comparison shows that both algorithms perform similarly for reasonable choices of fracture discretization and residual aperture parameters in the fixed‐grid case. The precision of both approaches is primarily influenced by discretization, while the minimum residual aperture parameter in the contact algorithm has a negligible effect. Using the fixed‐grid contact scheme, we also demonstrate the emergence of the sunset solution for receding hydraulic fractures under symmetry‐breaking configurations of the fracture front induced by heterogeneities, which are far beyond the constraints under which the sunset solution was originally derived.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.001 | 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".