Investigating the Effect of Crack Shape on the Interaction Behavior of Noncoplanar Surface Cracks Using Finite Element Analysis
Bibliographic record
Abstract
In the last two decades, multiple cracks are often found in aging aerospace and mechanical structures. The interaction and coalescence of multiple cracks may significantly affect the service lives of these aging structures. Knowledge of the behavior of interacting cracks is still limited. The calculation of the crack-tip stress intensity factor, SIF, along the interacting crack fronts is considered a major contribution for the application of any linear fracture mechanics model to investigate the growth life of these cracks. In this paper, a parametric study is presented for two parallel surface cracks in an infinite plate subjected to remote tension or to pure bending loads. This study focuses on constructing a finite element (FE) model that combines the submodeling technique with its ability to generate crack submodels of different lengths and depths, and a mesh generator that can build up a mesh grid based on the size, depth, and orientation of the interacting crack sub-models. The stress intensity factors for these cracks are calculated as a function of the crack front position, depth, shape, and plate thickness. In this paper, the values of the studied crack depth to length ratio, a/c, are 0.33, 0.5, 0.67, and 1.0. Where possible, a comparison of the 3-D with 2-D results is also considered.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 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".