Performance Analysis of a Helical Savonius Rotor with Shaft at 45° Twist Angle Using CFD
Bibliographic record
Abstract
Helical Savonius rotor is considered to be superior to conventional Savonius rotor in terms of higher power coefficient (Cp) and better starting characteristic. However studies related to helical Savonius rotors is few. In view of this, in this paper, the performance of a helical Savonius rotor with shaft at 45° bucket twist angle for one complete cycle of rotation was analyzed using Computational Fluid Dynamics. A two-bucket helical Savonius rotor with shaft was designed using GAMBIT, having a height of 60 cm and diameter of 17 cm with 45° bucket twist angle. A three dimensional Computational Fluid Dynamics analysis using Fluent package was done to predict the performance of the rotor. Standard k-? turbulence model with second order upwind discretization scheme and standard wall condition was used. Grid independence test was also conducted to have the best meshing accuracy. Power coefficients (Cp) of the rotor at different tip speed ratios were evaluated for rotor angle variation from 0° to 180°. Cp at each rotor angle increased with increase of tip speed ratio up to an optimum tip speed ratio, but then decreased even if tip speed ratio was further increased. Moreover, the effect of rotor angle on Cp in a complete cycle of rotation was analyzed. Cp was found to be positive at all rotor angles, and higher values of Cp were obtained at rotor angles namely 45°, 90°, 225° and 270°, which would contribute maximum power production by the rotor. In addition to these, flow physics of the rotor was studied using tangential velocity plots w.r.t. rotor angle and path lines across the rotor. It was found that at 45°, 90° and 135° rotor angles, maximum concentration of the path lines near the tip of the blades in the upstream and downstream side of the rotor had occurred, which would be responsible for generation of maximum power coefficient in its clockwise rotation.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".