Bayesian Flexural Rigidity Identification of Small Wind Turbine Blades Using Unscented Kalman Filter
Bibliographic record
Abstract
This study presents a comprehensive approach to estimating the flexural rigidity and mechanical behavior of small wind turbine blades through the application of an Unscented Kalman Filter (UKF), making significant contributions to the fields of structural mechanics and material identification. The methodology is based on Euler-Bernoulli beam theory and utilizes the Galerkin-Ritz technique for discretization. Focusing on in-plane bending vibrations while omitting axial vibration, this approach estimates the material properties of interest. To validate the proposed method, both simulation studies and experimental tests are conducted. The simulations involve a uniform cantilever beam, and lab-scale experimental tests are carried out on a non-rotating small-scale wind turbine blade. Experimental modal analysis is used to extract the blade’s modal properties, and a finite element model (FEM) is constructed using the material properties estimated by the UKF. A comparison between the FEM results and experimental data demonstrates the effectiveness and accuracy of the adapted UKF in real-world applications. This integrated experimental-numerical framework not only verifies the UKF's performance but also offers valuable insights for material identification and structural assessment, applicable to both small and large-scale wind turbines. These findings could lead to significant advancements in wind turbine blade optimization for enhanced sustainability and performance.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.001 |
| 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".