Abstract
Weight reduction is an effective parameter in the efficiency of flying vehicles. Fabric-covered membrane wings are used in ultralight aircraft to reduce weight and cost. This paper used machine learning methods (Random Forest, XGBoost, Extra Tree, Bayesian Regression, and Support Vector Machine) to estimate the aerodynamic coefficients of membrane wings instead of the conventional numerical and experimental approaches to save time and costs. The obtained data from the wind tunnel test (angle of attack and wind speed) on the fabrics parameters (air permeability, thickness, weave, weight per square meter, and surface drag coefficients) were considered as input parameters to quickly and accurately estimate the Lift and Drag coefficients of the membrane wings as outputs parameters. The results revealed that the air permeability, weight per square meter, and weave pattern of fabric had the greatest effect on the estimation of aerodynamic coefficients, which were confirmed by the previous numerical and experimental results.
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Abbreviations
- U :
-
Air relative speed (m/s)
- AOA :
-
Angle of attack (˚)
- B :
-
Cross-section of air permeability sample (mm2)
- ρ :
-
Density (kg/m3)
- C d :
-
Drag coefficient
- F d :
-
Drag force (N)
- C l :
-
Lift coefficient
- F l :
-
Lift force (N)
- A :
-
Reference area (m2)
- GSM :
-
Weight per square meter (g/m2)
- V :
-
Wind speed (m/s)
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Acknowledgements
Great appreciation from the Amirkabir wind tunnel laboratory of Iran for wind tunnel tests and Yazd University textile laboratory for measuring the air permeability.
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Hadipour-Gudarzi, S., Ekhtiyari, E. & Sefid, M. Estimating the Aerodynamic Coefficients of Membrane Wings Using Wind Tunnel: A Machine-Learning Approach. Iran J Sci Technol Trans Mech Eng (2023). https://doi.org/10.1007/s40997-023-00699-x
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DOI: https://doi.org/10.1007/s40997-023-00699-x