Fail-Safe Prediction for Bonded Composite Structures Using Discrete Damage Modeling
Bibliographic record
Abstract
View Video Presentation: https://doi.org/10.2514/6.2023-1318.vid Modern aircraft design uses composites for their outstanding strength, stiffness, and lightweight properties of these materials. The bonded composites require a fail-safe design, where the structure retains adequate strength for the service period between inspections or maintenance. This may take the form of alternate load paths and crack arrestment features. Since adhesive debonding is a critical failure method in bonded unitized structural components, understanding the behaviors of the bondline in both pure mode and mixed mode fracture is vital for understanding and predicting mechanical performance. The FASTBUCS program aims to develop a validation methodology for such structures through inspection methods, structural testing, and progressive damage analysis. This work uses Discrete Damage Modeling within the finite element code BSAM to capture structural elements with and without crack-arresting fasteners in a Pi-Joint stiffened panel under various types of loading. The results show that BSAM can replicate crack arrestment using fasteners in mode II loading. In addition, models without crack arresting features for Pi Cantilever Beam and a combined loading test article are also discussed.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 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) | 0.003 | 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".