Shape memory alloy‐based centrifugal stiffening for response reduction of horizontal axis wind turbine blade
Bibliographic record
Abstract
The aim of this study is to reduce the deformation of large horizontal axis wind turbine blades using shape memory alloy (SMA)-based centrifugal stiffening. A discrete model considering dominant modes of the tower, drive train and blades is developed in this study to demonstrate the performance of the proposed stiffening strategy. Here, super-elastic behaviour of SMA is characterized by Graesser-Cozzarelli model. Aerodynamic loads acting on the blades are evaluated using blade element momentum theory. The response is simulated using aerodynamic damping, which is estimated in each mode of vibration. Numerical results presented in this paper clearly show the significance of the proposed SMA-based stiffening to reduce blade vibration. Sensitivity analysis is also carried out to demonstrate the performance envelop of the proposed stiffening strategy over the operational range of the benchmark 5-MW wind turbine. The study clearly highlights the performance enhancement in terms of deformation in two orthogonal directions and design in terms of longitudinal stress that ultimately improve the serviceability of the blade.
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.
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 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".