A meta aircraft is an air vehicle comprised of a set of independent aircraft that are connected together in flight to form a larger composite aircraft. Previous research has shown that certain configurations of meta aircraft are statically and dynamically unstable. The focus of this paper is to analyze the controllability of three types of configurations. The three types of configurations studied here are wingtip to wingtip, tip to tail and lattice configurations. It is found that the controllability matrix of all configurations studied is full rank leading to fully controllable multi-body systems. With this knowledge a simple PID controller is created that seeks to move the composite center of mass to a desired altitude and heading. The gains in the controller are known a priori rather than computed adaptively. It is found that simple gain scheduling rules can be used to update the gains depending on the configuration. These results are obtained using a sophisticated non-linear multi-body dynamics tool using rotational springs and dampers to model connection joints. In addition, a vortex lattice method is used to model wing tip vortex interaction between aircraft.

This paper analyzes the performance of a micro-airship fleet (0.5 m diameter) to navigate indoors with waypoint control while tolerating collision between airships and the environment. Very little focus has been placed on studying airships in groups or how well they can rebound back into formation after a collision. With a micro-airship fleet, it is possible to remove the major problem of collision avoidance in multi-unmanned aerial vehicle missions, which can result in damage or even mission failure when other types of aircraft are used. These vehicles could be a viable option for missions where speed and precise control are not an important design constraint, such as indoor reconnaissance or long-term surveillance. A three degree of freedom simulation is created in which five airships are commanded to waypoints throughout a hall way. The control logic used involves independent proportional-derivative control without any communication between airships. Collisions occur during missions; thus, a contact model is included in the simulation to model these effects. Airship parameters were estimated using an actual airship to assure the simulation is accurate. The results show that the airships are able to navigate to their destinations even after several collisions.

The accurate measurement of adsorbed gas up to high pressures (∼100 bars) is critical for the development of new materials for adsorbed gas storage. The typical Sievert-type volumetric method introduces accumulating errors that can become large at maximum pressures. Alternatively, gravimetric methods employing microbalances require careful buoyancy corrections. In this paper, we present a combination gravimetric and volumetric system for methane sorption measurements on samples between ∼0.5 and 1 g. The gravimetric method described requires no buoyancy corrections. The tandem use of the gravimetric method allows for a check on the highest uncertainty volumetric measurements. The sources and proper calculation of uncertainties are discussed. Results from methane measurements on activated carbon MSC-30 and metal-organic framework HKUST-1 are compared across methods and within the literature.

Radial Basis Functions are a modern way of creating a regression model of a multivariate function when sampled data points are not uniformly distributed in a perfect grid. Radial Basis Functions are well suited to atmospheric characterization when unmanned aerial vehicles (UAVs) are used to sample the given space. Multiple UAVs reduce the time for the Radial Basis Functions to yield a suitable solution to the measured data while data from all aircraft are aggregated and sent to Radial Basis Functions to fit the data. The research presented here focuses on the requirements for a high correlation value between the sampled data and the actual data. It is found that the number of centers is a large driver of the goodness of fit in the Radial Basis Function routine, much like aliasing is an issue in sampling a sinusoidal function. These centers act like a sampling rate for the spatially varying wind field. If the centers are dense enough to fully capture the spatial frequency of the wind field, the Radial Basis Functions will produce a suitable fit. This also requires the number of data points to be larger than the number of centers. The ratio between the number of centers and number of sampled data points declines as the number of centers increases. The results presented here are revealed using a two-dimensional Fourier series analysis coupled to a spatially varying atmospheric wind model and a Radial Basis Function regression model.

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