Effect of struts and central tower on aerodynamics and aeroacoustics of vertical axis wind turbines using mid-fidelity and high-fidelity methods
Bibliographic record
Abstract
This study investigates the impact of struts and a central tower on the aerodynamics and aeroacoustics of Darrieus Vertical Axis Wind Turbines (VAWTs) at chord-based Reynolds numbers of 8.12e4. A 2-bladed H-Darrieus VAWT is used, featuring a 1.5m diameter, a solidity of 0.1 and a blade cross-section of symmetrical NACA 0021. The turbine design is kept simple and straight-bladed which is essential for isolating and analyzing the specific effects of struts and a tower. The high-fidelity Lattice Boltzmann Method (LBM) in PowerFLOW 6-2020 and the mid-fidelity Lifting Line Free Vortex Wake (LLFVW) method in QBlade 2.0 are employed, with the mid-fidelity method providing a faster analytical tool for insights into the turbine performance. Firstly, both the LLFVW (mid-fidelity) and LBM (high-fidelity) methods effectively capture the general trends observed in VAWT power performance. However, the former predicts mean thrust values that are approximately 10% higher, and mean torque values that are approximately 19% higher, in comparison to the latter. Subsequently, the former predicts lower streamwise wake velocities relative to those predicted by the latter. These differences increase in configurations that include struts and a tower (to 30% - 31%). Secondly, the presence of struts and a tower leads to a reduction in both mean power (by 15% to 55%) and thrust (by 3% to 3.6%), with a further small decrease observed when doubling the tower diameter (power and thrust both by 0.5% to 3%). The struts predominantly affect the spanwise distribution of blade loading, while the tower impacts the azimuthal variation of blade loading. Additionally, the addition of struts and a tower reduces low-frequency noise (50-200 Hz) while increasing high-frequency noise (> 300 Hz). The observed decrease in mean blade loading results in reduced low-frequency noise, while the increase in high-frequency noise is ascribed to the increased intensity of BWI/BVI leading to higher unsteady loading fluctuations on blades.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".