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UAS Airframe Design
Published in R. Kurt Barnhart, Douglas M. Marshall, Eric J. Shappee, Introduction to Unmanned Aircraft Systems, 2021
Michael T. Most, Michael Stroup
The flying-wing design affords certain advantages over other designs. Its construction is straightforward with fewer parts and qualities that simplify assembly and manufacture. The flying wing is attractive to designers because it offers the theoretical potential for high aerodynamic efficiency (low drag for the amount of lift produced), which leads to lower energy consumption and increased range. These characteristics can result in improved L/D ratios. Flying wings are robust, and sUASs are often constructed without landing gear, saving weight and reducing parasite drag. This design affords considerable flexibility in payload positioning, although aircraft CG is critical and the load must be judiciously distributed. Also, because UAS flying wings are most frequently of pusher design, the lack of a forward-mounted powerplant affords the same advantages as a twin boom—that is, greater fuselage volume for sensor packages and a clear view without the distorting influences of engine exhaust and heat. For military UASs, an added advantage of the flying wing is a low radar cross section (RCS), which is a measure of the detectability of the aircraft. The characteristic of minimal RCS is a significant reason why unmanned combat aerial vehicles, such as the Northrop UCAV X-47B, among other entries in the Navy’s Unmanned Carrier-Launched Airborne Surveillance and Strike (UCLASS) competition, and stealthy reconnaissance UASs, such as the Lockheed Martin RQ-3 DarkStar and the RQ-170 Sentinel, are of flying-wing design. Again, in these instances of military UAS applications, it can be seen that mission goals dictate design characteristics.
UAS Airframe and Powerplant Design
Published in Douglas M. Marshall, R. Kurt Barnhart, Eric Shappee, Michael Most, Introduction to Unmanned Aircraft Systems, 2016
The flying-wing design affords certain advantages over other designs. Its construction is straightforward with few parts, qualities that simplify assembly and manufacture. The flying wing is attractive to designers because it offers the theoretical potential for high aerodynamic efficiency (low drag for the amount of lift produced) and greater efficiency, which leads to lower energy consumption and increased range. These characteristics also imbue aircraft of this design with excellent gliding ratios. Flying wings are robust, and sUAS are often constructed without landing gear, saving weight and reducing parasite drag. This design affords considerable flexibility in payload positioning, although aircraft cg is critical and the load must be judiciously distributed. Also, because UAS flying wings are most frequently of pusher design, the lack of a forward-mounted powerplant affords the same advantages as a twin boom—that is, greater fuselage volume for sensor packages and a clear view without the distorting influences of engine exhaust and heat. For military UASs, an added advantage of the flying wing is a low radar cross section, or RCS, which is a measure of the detectability of the aircraft. The characteristic of minimal RCS is a significant reason why unmanned combat aerial vehicles, such as the Northrop UCAV X-47B, among other entries in the Navy’s Unmanned Carrier-Launched Airborne Surveillance and Strike (UCLASS) competition, and stealthy reconnaissance UASs, such as the Lockheed Martin RQ-3 DarkStar and the RQ-170 Sentinel, are of flying-wing design. Again, in these instances of military UAS applications, it can be seen that mission goals dictate design characteristics.
Training a Neural-Network-Based Surrogate Model for Aerodynamic Optimisation Using a Gaussian Process
Published in International Journal of Computational Fluid Dynamics, 2022
Yousef Ghazi, Nahla Alhazmi, Radek Tezaur, Charbel Farhat
The training methodology described in Section 4 is applied here to an efficient construction of an NN surrogate model for the fast prediction of (1) the lift-over-drag ratio L/D generated by a four-digit NACA airfoil and (2) the pressure coefficient around an mAEWing2 flying wing aircraft. For this purpose: All aerodynamic HDMs are constructed using the massively parallel, three-dimensional, compressible flow solver AERO-F, which was validated for many flow problems including aircraft flow problems (Geuzaine et al. 2003; Farhat, Geuzaine, and Brown 2003).In Algorithm 1, the GP is equipped with an RBF kernel (2) since the predicted integral quantities are expected to be smooth function of the chosen parameters.The Python package Scikit-learn (Pedregosa et al. 2011) is used to construct the GP model and optimise the hyper-parameters of the RBF kernel using the marginal likelihood method.The software package pyTorch (Paszke et al. 2019) is used to implement the NNs.