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Traditional User/Maintenance Manuals provide useful information when dealing with simple machines. However, when dealing with complex systems of systems and highly miniaturized technologies, like UAVs, or with machines with millions of parts, a commercial aircraft is a case in point, new technologies taking advantage of Augmented Reality can rapidly and effectively support the maintenance operations. This paper presents a User/Maintenance Manual based on Augmented Reality to help the operator in the detection of parts and in the sequence to be followed to assemble/disassemble systems and subsystems. The proposed system includes a handheld device and/or an head mounted display or special goggles, to be used by on-site operators, with software management providing data fusion and overlaying traditional 2D user/maintenance manual information with an augmented reality software and appropriate interface.

The maturity reached in the development of Unmanned Air Vehicles (UAVs) systems is making them more and more attractive for a vast number of civil missions. Clearly, the introduction of UAVs in the civil airspace requiring practical and effective regulation is one of the most critical issues being currently discussed. As several civil air authorities report in their regulations “Sense and Avoid” or “Detect and Avoid” capabilities are critical to the successful integration of UAV into the civil airspace. One possible approach to achieve this capability, specifically for operations beyond the Line-of-Sight, would be to equip air vehicles with a vision-based system using cameras to monitor the surrounding air space and to classify other air vehicles flying in close proximity. This paper presents an image-based application for the supervised classification of air vehicles.

The innovative highly flexible wings made of extremely light structures, yet still capable of carrying a considerable amount of non- structural weights, requires significant effort in structural simulations. The complexity involved in such design demands for simplified mathematical tools based on appropriate nonlinear structural schemes combined with reduced order models capable of predicting accurately their aero-structural behaviour. The model presented in this paper is based on a consistent nonlinear beam-wise scheme, capable of simulating the unconventional aeroelastic behaviour of flexible composite wings. The partial differential equations describing the wing dynamics are expanded up to the third order and can be used to explore the effect of static deflection imposed by external trim, the effect of gust loads and the one of nonlinear aerodynamic stall.

In this paper the finite element model of an Unmanned Aerial Vehicle is updated by using experimental data coming from a standard ground vibration test in order to improve the numerical-experimental correlation. A sensitivity-based updating methodology that iteratively minimizes a residual vector, defined on the modal parameters (e.g. natural frequencies and mode shapes), is considered to identify the unknown values of the updating parameters. The structure under investigation is the Clarkson University Golden Eagle UAV. An initial numerical model of the structure is obtained by assembling the individual components previously updated which included wings, fuselage, horizontal tail, vertical tails and tail booms. As a result the identification procedure shifts its focus on the joints between UAV elements which could not be modeled accurately in earlier investigations.

A kinetic energy recover system (KERS) captures the kinetic energy that results when brakes are applied to a moving vehicle. The recovered energy can be stored in a flywheel or battery and used later, to help boost acceleration. KERS helps transfer what was formerly wasted energy into useful energy. In 2009, the Federation Internationale de l’Automobile (FIA) began allowing KERS to be used in Formula One (F1) competition. Still considered experimental, this technology is undergoing development in the racing world but has yet to become mainstream for production vehicles. The Introduction of this book details the theory behind the KERS concept. It describes how kinetic energy can be recovered, and the mechanical and electric systems for storing it. Flybrid systems are highlighted since they are the most popular KERS developed thus far. The KERS of two racing vehicles are profiled: the Dyson Lola LMP1 and Audi R18 e-tron Quattro.