Aerodynamic and Aeroacoustic Analyses of Vertical-Axis Wind Turbines
Bibliographic record
Abstract
In this thesis, aerodynamic and aeroacoustic analyses are performed for different configurations of Vertical Axis Wind Turbines (VAWTs).Performance of traditional Troposkien-Darrieus VAWTs and novel Shifted-Troposkien-Shape VAWTs (or STS-VAWTs) with two configurations (50% STS-VAWT and 100% STS-VAWT) were simulated numerically.These novel VAWTs try to reduce the Blade-Wake Interactions (BWIs) between the turbine blades and the wake generated during revolutions, which are known to reduce performance of traditional Troposkien-Darrieus VAWTs.Separate experimental investigations (not included in this thesis) in the current laboratory, indicated that a 0.75-m 50% STS-VAWT produced more power than a traditional 0.75-m Troposkien-Darrieus VAWTs, while requiring less material to be manufactured.In the current investigation, simulations were performed using an in-house code (GENUVP, GENeral Unsteady Vortex Particle code), which was available as a part of a research collaboration with the University of Athens.Aerodynamic characteristics of VAWTs (mainly power generation as a function of wind speed at a fixed turbine rpm, or rotations per minute) can be obtained by computing the torque on the blades using unsteady panel methods in combination with particle-vortex methods.The numerical aerodynamic data become an input for the aeroacoustic part of the same in-house code.Aeroacoustic noise is predicted using the Formulation 1C of Ffowcs Williams -Hawkings equation.Numerical simulation results of power coefficient for 2-m and 17-m Troposkien-Darrieus VAWTs were initially validated against experimental data obtained from the literature.Additional simulations were also performed for 50% STS-VAWT and 100% STS-VAWT, confirming Chapter 2: ...........
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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 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".