Integration of Fatigue R-Curve Effects into VCCT for Durability Predictions, Part 1: Buckled Composite Single-Stringer Stiffened Panels
Bibliographic record
Abstract
View Video Presentation: https://doi.org/10.2514/6.2023-0952.vid Composite structures have become popular in modern aircraft because they help reduce weight and increase durability. In addition, hat-stiffened panels provide the stability that the airframe skin needs. However, they can be subject to delamination in the post-buckling regime. Progressive damage analysis (PDA) methods can help predict interlaminar and intralaminar failure events. Many aircraft structures are subject to cyclic loading in the post-buckling regime. Hence, fatigue life prediction becomes essential for design and sustainment purposes. Under the NASA Advanced Composites Project (ACP), composite panels stiffened with single hat-stringers were subject to a cyclic loading sequence from a pre-buckling state to a post-buckling state. This test campaign aimed to provide damage initiation and growth data for three initial damage scenarios: nominally pristine, initial Teflon inserts representing manufacturing defects, and impact-induced damage. This work uses the Abaqus FEA Virtual Crack Closure Technique (VCCT), capabilities enhanced through a novel empirical method integrating fatigue R-curve effects (different from R-ratio) into the Paris Law via user-defined subroutine to simulate the fatigue response of the panels with single hat-stringers. The analysis predictions were within 5% of the test results using this novel method.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 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".