Analysis of flows past airfoils at very low Reynolds numbers
Bibliographic record
Abstract
This article presents the special features of airfoil flows at very low Reynolds numbers, which are of interest for unmanned micro-air-vehicles. Airfoil flow solutions at low Reynolds numbers are obtained with an efficient numerical analysis based on a pseudo-time integration method using artificial compressibility for solving accurately the Navier—Stokes equations. The flow problem is solved in a rectangular computational domain obtained by a coordinate transformation from the physical flow domain around the airfoil at incidence. This method uses a second-order central differencing approach on a stretched staggered grid. A special decoupling procedure using the continuity equation reduces the problem to the solution of scalar tridiagonal systems of equations, which enhances substantially the computational efficiency of the method. The pressure distribution, lift, and drag coefficients are presented for several NACA airfoils at various incidences and low Reynolds numbers between 400 and 6000. Streamline contours for the flow with separations past several airfoils at low Reynolds numbers have also been generated and compared. For a better understanding of the complex flow separation phenomena in the viscous flows past airfoils at very low Reynolds numbers, the onset of separation and reattachment locations have been calculated, and a detailed study is presented on the influence of the Reynolds number, angle of attack, relative thickness and camber, and the maximum camber position along the chord.
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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 | 0.000 | 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".