On the Performance of a Modified Triple Stack Blade Savonius Wind Turbine as a Function of Geometrical Parameters
Bibliographic record
Abstract
The Savonius wind turbine is one of the most well-known vertical axis wind turbines with insensitivity to wind direction, flow turbulence, and high torque generation. These turbines can extract up to 20% of the energy from the wind. This study numerically analyzes the performance of a modified Savonius wind turbine equipped with secondary blades and slots. The k-ε standard method is used to simulate the turbulence flow around the turbine, and the simulation is performed using the ANSYS FLUENT 18.2 commercial code. The effects of distance between the main blade and the secondary blade, position of the secondary blade, the width of the main blade’s slot, and the profile of the secondary blade on the produced torque are studied and analyzed. The simulation is performed at four wind velocities: 3, 4, 5, and 6 m/s. The results showed that the output torque at the secondary blade angular position β = 130 is higher than other angles. Furthermore, by increasing the radius of the additional blade from R = 25 to 43 mm, the torque is improved, and the area below the output torque curve is increased. Moreover, the results showed that creating a slot on the main blade equipped with a secondary blade has a significant impact on the produced torque; however, the geometrical parameters of the proposed rotors should be adjusted accurately to find the best case in terms of the produced torque.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 | 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".