Probabilistic First Ply Failure Analysis of Wind Turbine Blade Laminates
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 it