An innovative augmentation technique of savonius wind turbine performance
Bibliographic record
Abstract
This work presents an innovative technique to enhance the performance of the Savonius wind turbine. The new technique is based on introducing an upstream deflector and downstream baffle. The shape and location of both devices are optimized using a genetic algorithm. The performance of the turbine with the optimized devices is compared with the single Savonius turbine performance. The study employs the finite volume solver (ANSYS-FLUENT) to solve unsteady Reynolds Averaged Navier–Stokes equations and turbulence model equations. The optimized configuration results in much higher power coefficient than the Savonius turbine. The average peak power coefficient using both deflector and baffle is 0.47 compared to 0.24 of the Savonius turbine. The peak power coefficient of the turbine corresponds to a speed ratio close to unity. This improved performance is attributed to the favorable aerodynamic interaction between the turbine and the downstream baffle which accelerates the flow around the rotor and generates larger turning torque. The baffle generates a jet effect on the advancing bucket and accelerates the flow behind the bucket creating a large zone of negative pressure and thereby increases the driving torque. Furthermore, the upstream deflector (also called shield or curtain) produces a shield for the returning bucket of the turbine which diminishes the adverse effect associated with the returning bucket on the aerodynamic torque of the turbine. This remarkable improvement of turbine performance will encourage the future application of the Savonius wind turbine in small power applications of wind energy.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".