An aeroelastic model for vertical-axis wind turbine blades
Bibliographic record
Abstract
This study develops a two-dimensional aeroelastic model for the blades of vertical-axis wind turbines. This model is theoretically traceable and computationally efficient owing to its simple form. The structural dynamics of the blade is reduced to a two-dimensional section with heave and pitch degrees of freedom. The equations of motion are derived using Lagrange's equations. The effects of centrifugal stiffening are added to the model using Southwell's coefficients. Aeroelastic stability is investigated in both the frequency and time domains. Theodorsen's unsteady aerodynamic theory is used in the frequency domain formulation, where the stability analysis is conducted using the standard p-k method. For time domain analysis, a free-wake unsteady vortex lattice method is coupled with the structural dynamics equations using a conventional serial staggered scheme. The root-mean-square of the normalized potential energy of the system is used to pinpoint the onset of flutter. Comparison of flutter speed and frequency from frequency- and time-domain solutions with experimental data shows close agreement, validating the model’s predictive capability. Additionally, a parametric study is carried out to investigate the effects of mass ratio, frequency ratio, and the dimensionless center-of-mass offset from the mid-chord on the flutter speed. These analyses are essential for validating the design of larger and lighter blades currently being developed for offshore wind energy applications.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 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".