Low Reynolds Number Vertical Axis Wind Turbine for Mars
Bibliographic record
Abstract
A low Reynolds number wind turbine is designed to extract the power from wind energy on Mars. As compared to solar cells, wind turbine systems have an advantage on Mars, as they can continuously produce power during dust storms and at night. The present work specifically addresses the design of a 500 W Darrieus-type straight-bladed vertical-axis wind turbine (S-VAWT) considering the atmospheric conditions on Mars. The thin atmosphere and wind speed on Mars result in low Reynolds numbers (2000–80000) representing either laminar or transitional flow over airfoils, and influences the aerodynamic loads and performance of the airfoils. Therefore a transitional model is used to predict the lift and drag coefficients for transitional flows over airfoils. The transitional models used in the present work combine existing methods for predicting the onset and extent of transition, which are compatible with the Spalart-Allmaras turbulence model. The model is first validated with the experimental predictions reported in the literature for an NACA 0018 airfoil. The wind turbine is designed and optimized by iteratively stepping through the following tasks: rotor height, rotor diameter, chord length, and aerodynamic loads. The CARDAAV code, based on the “Double-Multiple Streamtube” model, is used to determine the performances and optimize the various parameters of the straight-bladed vertical-axis wind turbine.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".