Numerical investigation of the dynamic stall reduction on the UAS-S45 aerofoil using the optimised aerofoil method
Bibliographic record
Abstract
Abstract This paper investigates the effect of the optimised morphing leading edge (MLE) and the morphing trailing edge (MTE) on dynamic stall vortices (DSV) for a pitching aerofoil through numerical simulations. In the first stage of the methodology, the optimisation of the UAS-S45 aerofoil was performed using a morphing optimisation framework. The mathematical model used Bezier-Parsec parametrisation, and the particle swarm optimisation algorithm was coupled with a pattern search with the aim of designing an aerodynamically efficient UAS-45 aerofoil. The $\gamma - R{e_\theta }$ transition turbulence model was firstly applied to predict the laminar to turbulent flow transition. The morphing aerofoil increased the overall aerodynamic performances while delaying boundary layer separation. Secondly, the unsteady analysis of the UAS-S45 aerofoil and its morphing configurations was carried out and the unsteady flow field and aerodynamic forces were analysed at the Reynolds number of 2.4 × 10 6 and five different reduced frequencies of k = 0.05, 0.08, 1.2, 1.6 and 2.0. The lift ( ${C_L})$ , drag ( ${C_D})$ and moment ( ${C_M})\;$ coefficients variations with the angle-of-attack of the reference and morphing aerofoils were compared. It was found that a higher reduced frequencies of 1.2 to 2 stabilised the leading-edge vortex that provided its lift variation in the dynamic stall phase. The maximum lift $\left( {{C_{L,max}}} \right)$ and drag $\left( {{C_{D,max}}} \right)\;$ coefficients and the stall angles of attack are evaluated for all studied reduced frequencies. The numerical results have shown that the new radius of curvature of the MLE aerofoil can minimise the streamwise adverse pressure gradient and prevent significant flow separation and suppress the formation of the DSV. Furthermore, it was shown that the morphing aerofoil delayed the stall angle-of-attack by 14.26% with respect to the reference aerofoil, and that the ${C_{L,max}}\;$ of the aerofoil increased from 2.49 to 3.04. However, while the MTE aerofoil was found to increase the overall lift coefficient and the ${C_{L,max}}$ , it did not control the dynamic stall. Vorticity behaviour during DSV generation and detachment has shown that the MTE can change the vortices’ evolution and increase vorticity flux from the leading-edge shear layer, thus increasing DSV circulation. The conclusion that can be drawn from this study is that the fixed drooped morphing leading edge aerofoils have the potential to control the dynamic stall. These findings contribute to a better understanding of the flow analysis of morphing aerofoils in an unsteady flow.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 teacher head, 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".