Steady and unsteady characteristics of circular arc airfoils for water pumping windmills
Bibliographic record
Abstract
Accurate airfoil lift and drag data at low Reynolds number, Re, and high angles of attack, α, are needed to analyze the performance of small wind turbines, particularly their starting. In the current study, the steady and unsteady aerodynamic characteristics of circular arc airfoils (CAAs) with and without spars, as used in water-pumping windmills, were examined in a wind tunnel of 1 m2 cross section at Re<106. The tunnel was configured as an open jet and a closed section. The effects of varying geometrical characteristics on the CAA performance were investigated using a combination of thickness, camber, aspect ratio, and airfoil chord-to-tunnel height ratio. Using force transducers, the aerodynamic forces acting on the airfoils were measured directly for both increasing and decreasing α. The decreasing α measurements produce a higher lift–drag ratio than the increasing measurements, mostly in the post-stall region. In addition, “second stall,” was observed at large α in both the open and closed tunnels depending on the Re and tunnel blockage, but was much more prominent in the closed tunnel due to its wall constraining the wake and preventing the flow from switching from one regime to another. It was shown that the performance of all tested airfoils was sensitive to low Re under steady and unsteady conditions. The latter measurements for an airfoil oscillated at reduced frequencies, k≤0.06, are the first for CAAs. Results from this investigation provide a comprehensive airfoil dataset for the accurate blade element theory modeling of CAAs aerodynamic and starting performances.
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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".