Aerodynamic Analysis of Airfoils at Very Low Reynolds Numbers
Bibliographic record
Abstract
This paper presents an efficient numerical method for the flows past airfoils at very low Reynolds numbers, which are of interest for the micro-aircraft applications and the unmanned air vehicles (UAV). The flows at very low Reynolds numbers are dominated by viscous effects and by very thick boundary layers, with few computational or experimental results available for airfoils. The present analysis is based on a pseudo-time integration method using artificial compressibility to accurately solve the Navier-Stokes equations. This is done in a rectangular computational domain obtained by a coordinate transformation fiom the physical flow domain around the airfoil. The method uses a central differencing scheme on a stretched staggered grid in the computational domain. This method is first successfully validated for the flows with multiple separation regions past a downstream-facing step by comparison with previous experimental and computational results at very low Reynolds numbers between 400 and 1200. Then the method is used to obtain solutions for several NACA airfoils at very low Reynolds numbers between 400 and 6000. The present airfoil solutions are validated by comparison with the numerical results obtained by Kunz & Kroo for Reynolds numbers between 1000 and 6000 (no results were available for Reynolds numbers smaller than 1000). A very good agreement has been found between the two sets of results. The decrease in the low Reynolds number was found to lead to a marked increase in the pressure coefficient on the airfoil, which is more pronounced towards the trailing edge and for thinner airfoils.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".