While computational methods are prevalent in aircraft conceptual design, recent advances in mechatronics and manufacturing are lowering the cost of practical experiments. Focussing on a relatively simple property, the lift curve, this study aims to increase understanding of how basic aerodynamic characteristics of a complex stealth configuration can be estimated experimentally using low-cost equipment, rapid prototyping methods and remotely piloted aircraft.
Lift curve estimates are obtained from a wind tunnel test of a three-dimensional-printed, 3.8%-scale model of a generic fighter and from flight testing a 14%-scale demonstrator using both a simple and a more advanced identification technique based on neural networks. These results are compared to a computational fluid dynamics study, a panel method and a straightforward, theoretical approach based on radical geometry simplifications.
Besides a good agreement in the linear region, discrepancies at high angles of attack reveal the shortcomings of each method. The remotely piloted model manages to provide consistent results beyond the physical limitations of the wind tunnel although it seems limited by instrumentation capabilities and unmodelled thrust effects.
Physical models can, even though low-cost experiments, expand the capabilities of other aerodynamic tools and contribute to reducing uncertainty when other estimations diverge.
This study highlights the limitations of commonly used aerodynamic methods and shows how low-cost prototyping and testing can complement or validate other estimations in the early study of a complex configuration.
Estimation of lift characteristics of a subscale fighter using low-cost experimental methods
Aircraft Engineering and Aerospace Technology ; 94 , 8 ; 1379-1389
2022-04-06
1 pages
Article (Journal)
Electronic Resource
English
SYSTEM IDENTIFICATION OF A SUBSCALE FIGHTER AIRCRAFT
British Library Conference Proceedings | 2018
|ICAS2016_0390: SUBSCALE FLIGHT TESTING OF A GENERIC FIGHTER AIRCRAFT
British Library Conference Proceedings | 2016
|