Estimation of lift characteristics of a subscale fighter using low-cost experimental methods

Abstract

Purpose 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. Design/methodology/approach 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. Findings 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. Practical implications Physical models can, even though low-cost experiments, expand the capabilities of other aerodynamic tools and contribute to reducing uncertainty when other estimations diverge. Originality/value 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.

Experimental evaluation of the drag curves of small fixed wing UAVs

“…The need for a dedicated scale model of the airframe (necessary whenever the size of the working section and the risk of significant wall effects conspire to exclude tests of the actual vehicle in the tunnel) may add further costs and, again, increase the need for skilled personnel [16]. The data collected through a single wind tunnel run may also be subjected to calibration errors [17], resulting in the need to perform repeats on multiple different days, significantly increasing the cost of this already expensive method. Additionally, the clean, clinical conditions created by wind tunnels may offer less by way of real world operational insight in the case of small UAVs.…”