A review of aeroelastic instabilities and resonance effects in wind turbine blade dynamics
Bibliographic record
Abstract
Over the years, wind turbine blades have become increasingly larger and more flexible to achieve higher efficiency and lower energy costs, which brings more issues related to forced resonance and aeroelastic instabilities because of higher dynamic loads and complex inflow conditions. The manuscript systematically reviews the literature, covering experimental versus computational studies, reduced-order models (ROM), machine learning (MI) based aeroelastic models, Euler-Bernoulli beam models, Timoshenko beam models, geometrically exact beam formulations, and computational fluid dynamics (CFD) models coupled with structural dynamics models to investigate primary and internal resonances and dynamic stalls for National Renewable Energy Laboratory (NREL) 5-MW and International Energy Agency (IEA) 15-MW wind turbines. The article also addresses vibration mitigation techniques, including passive, active, and semi-active control, to resolve aeroelastic instabilities. Most studies have assumed linear aeroelastic models and isotropic blade material for initial structural dynamics analysis. The higher mode frequencies computed using the Euler-Bernoulli model differ by approximately 5.23%, those using the Timoshenko model by 3.13%, and those through the Rayleigh model by 3.4% from the geometrically exact formulations employed. Euler-Bernoulli models significantly overestimated flutter speeds compared to the geometrically exact beam model for the NREL 5-MW blades. A key takeaway is that modern, prolonged, flexible blades are sensitive to flutter instabilities, where aerodynamic damping can drop significantly at certain operational speeds. The Euler-Bernoulli beam model proved to be a valuable tool at the initial design stage due to its simplicity and computational efficiency. Future research on managing forced resonance and dynamic stalls in ultra-large blades should focus on integrating nonlinear modeling, cutting-edge materials and structures, artificial intelligence (AI)-powered digital twins, and exploring targeted active control techniques.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".