Abstract
This paper proposes an efficient low-speed airfoil selection and design optimization process using multi-fidelity analysis for a long endurance Unmanned Aerial Vehicle (UAV) flying wing. The developed process includes the low speed airfoil database construction, airfoil selection and design optimization steps based on the given design requirements. The multi-fidelity analysis solvers including the panel method and computational fluid dynamics (CFD) are presented to analyze the low speed airfoil aerodynamic characteristics accurately and perform inverse airfoil design optimization effectively without any noticeable turnaround time in the early aircraft design stage. The unconventional flying wing UAV design shows poor reaction in longitudinal stability. However, It has low parasite drag, long endurance, and better performance. The multi-fidelity analysis solvers are validated for the E387 and CAL2463m airfoil compared to the wind tunnel test data. Then, 29 low speed airfoils for flying wing UAV are constructed by using the multi-fidelity solvers. The weighting score method is used to select the appropriate airfoil for the given design requirements. The selected airfoil is used as a baseline for the inverse airfoil design optimization step to refine and obtain the optimal airfoil configuration. The implementation of proposed method is applied for the real flying-wing UAV airfoil design case to demonstrate the effectiveness and feasibility of the proposed method.
Highlights
Airfoil plays an extremely important role for the aircraft aerodynamics, performance, and stability
Many airfoil aerodynamics data were tested at the 2.8×4.0 ft (0.853×1.219 m) low-turbulence wind tunnel in the Subsonic Aerodynamics Research Laboratory at the University of Illinois at Urbana-Champaign (UIUC) [1]
The flying wing Unmanned Aerial Vehicle (UAV) is well-known for high performance due to the low parasite drag with the same engine power
Summary
Airfoil plays an extremely important role for the aircraft aerodynamics, performance, and stability. Many researchers currently implement the reliable and accurate prediction analysis tools such as panel method, Reynolds-averaged Navier-Stokes (RANS), and in-house CFD solvers to analyze and design airfoil. These different analysis methods are required for the different flow conditions. Silisteanu et al introduced a method for estimating the transition onset and extension based on the temporal parameter of the skin friction coefficient and flow vorticity at the wall [2] This method shows that the relative error in the drag coefficient is lower than 8% when a fully turbulent model can introduce error up to 50%. The flying wing UAV is well-known for high performance due to the low parasite drag with the same engine power
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