Probabilistic First Ply Failure Analysis of Wind Turbine Blade Laminates
Bibliographic record
Abstract
This work presents an integrated methodology for probabilistic analysis of first ply failure (FPF) of composite materials used in wind turbine blades using a multi-scale approach, M-SaF (Micromechanics based failure Analysis under Static loading) and Bayesian statistical framework. The M-SaF approach was developed to predict failure in heterogeneous materials by analyzing each constituent failure at the micro level, i.e. the fiber, the matrix and the interface. M-SaF is composed of three sub models to account for the stresses in the constituents, namely: the Stassi Equivalent stress model for the matrix; fiber breakage based on Tsai-Wu failure criterion . The analysis is carried out on a three dimensional representative unit cell of the composite. Use of M-SaF in practise requires the constituent’s properties (fiber, matrix, and interface) which are difficult to fully characterize and can have significant statistical variation. A Bayesian framework was therefore developed to afford probabilistic failure estimates tuned to available test coupon data. An academic problem of a cantilever beam was used to demonstrate the parameter calibration procedure. Lamina level test data are then used to calibrate the constituent’s properties within the Bayesian framework, computing posterior probability distributions of fiber and matrix properties. The posterior distributions were then used to predict probabilistic FPF of a range of composite laminates layups for OptiDaT and WWFE test data base values.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".