An Experimental and Numerical Study of Three-Dimensional Fatigue Damage in Carbon Fibre Reinforced Polymers
Bibliographic record
Abstract
A phenomenological experimental study was presented for understanding the effect of cyclic loading, generally experienced by typical modern aircraft structures during flight, on the propagation of micro-mesoscopic damage in carbon fibre reinforced composite laminates.Testing was carried out by employing ultra-high resolution SkyScan 1173 XRmicro computed tomography to identify and assess damage progression during fatigue testing.It provided qualitative as well as quantitative assessments of the damage in the composites which supported the analytical investigations.An in-house solution was developed for statistically analyzing the occurrence, frequency and geometry of cracks throughout the fatigue life.This methodology was used to process the data from scan for the purposes of visualizing damage initiation and propagation.Hence, quantitative analysis could be performed.Analysis resulted in the definition of fatigue crack growth rates, da/dn for each of the 3 orthogonal planes, which was interpreted in terms of the 3 damage modes; opening, in-plane shear and out-of-plane shear.By applying linear elastic fracture mechanics (LEFM) laws, strain energy release rates were calculated, while differentiating between modes II and III in a novel manner.For verifying the parameters obtained, definite cracks were traced and analyzed.Finally, a methodology was implemented to import the damage model into a finite element analysis (FEA) tool to be used for crack growth analysis, and simulations were compared to experimental findings.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| 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".