Influence of Unsteady and Kinematic Parameters on Aerodynamic Characteristics of a Pitching Airfoil
Bibliographic record
Abstract
Most unmanned aerial vehicles which fly with moving wings or encounter gusts at low Reynolds number conditions make pitching oscillation motions. The aerodynamic forces and flow field around the wings vary dramatically with the unsteady motion parameters (reduced frequency, mean angle of attack, and amplitude). This study conducts numerical simulations to investigate the influence of such parameters on the aerodynamic characteristics of a pitching NACA 0012 airfoil. The respective cases are researched through computational fluid dynamics (CFD) based on the finite-volume method (FVM). The governing equations are the unsteady, incompressible two-dimensional Navier–Stokes (N-S) equations. The airfoil performs sinusoidal pitching oscillations with respect to the quarter chord at the Reynolds number 2.53×105. A detailed analysis of the force coefficients and how their evolution is affected by the dynamics of flow structures generated during pitch oscillations is presented. The results show that these parameters change the instantaneous force coefficients quantitatively and qualitatively. The effective angle of attack is different at various locations of the airfoil chord during the oscillation motion, which determines the deviations of forces at the same angle of attack during pitch-up and pitch-down periods. It is also observed that the strength, interaction, and convection of the vortex surrounding the airfoil are significantly affected by the variations of these parameters.
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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 | 0.002 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".