Design, Development, and Analysis of Combined Darrieus and Savonius Wind Turbine
Bibliographic record
Abstract
This study concerns the design, development, and analysis of the combined Darrieus and Savonius wind turbine. The circumferential placement of three straight Darrieus blades with a NACA 0018 cross-section and a helically twisted Savonius blade ensures an even distribution of torque. Savonius can be used to self-start a wind turbine, something that the Darrieus cannot do on its own due to its unique design. All the wind turbine parts are designed using CAD software, and simulation data is obtained via the CFD approach. Also, the design is imported to FlashForge Finder to 3D print the wind turbine profile, and finally, testing is carried out. The plastic material used for Savonius is ABS, and that for Darrieus is PLA. Equipped with a hybrid design, the fabricated VAWT has exhibited notable characteristics during testing. Its cut-in wind speed, which is the minimum required for operation, is remarkably low, at 3 m/s. Under 6 m/s wind conditions, it achieves a maximum power output of 7.5537 watts. Additionally, the rotor blade has registered a peak rotational speed of 431 rpm at 6 m/s, showcasing its promising potential for wind energy harnessing. Furthermore, a graph plot analysis of the data collected from both processes reveals a comparable slope characteristic. Additionally, mechanical losses have been demonstrated by the discrepancy between the theoretical and experimental data. The study investigates the performance of a novel wind turbine model equipped with an innovative self-starting mechanism, eliminating reliance on external motors, across a range of wind velocities.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".