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Approximately 25 % of a passenger vehicle's aerodynamic drag comes directly or indirectly from its wheels, indicating that the rim geometry is highly relevant for increasing the vehicle's overall energy efficiency. An extensive experimental study is presented where a parametric model of the rim design was developed, and statistical methods were employed to isolate the aerodynamic effects of certain geometric rim parameters. In addition to wind tunnel force measurements, this study employed the flowfield measurement techniques of wake surveys, wheelhouse pressure measurements, and base pressure measurements to investigate and explain the most important parameters' effects on the flowfield. In addition, a numerical model of the vehicle with various rim geometries was developed and used to further elucidate the effects of certain geometric parameters on the flow field. The results showed that the most important parameter was the coverage area, and it was found to have a linear effect on the aerodynamic drag. Interestingly, parameters associated with the outer radial region of wheel (rim cover) were also found to be significant, along with the wheel depth of center (flatness). The flowfield measurements showed, again, that the coverage area had the most significant effect, with it directly affecting how much flow passes through the front rim and subsequently affecting features like the near-ground jetting vortex and vortices out of the wheelhouse. In addition, the coverage area also affected the pressure recovery at the base of the vehicle and the wheelhouse pressure. The effects of other parameters are also detailed in the paper. The effects of different coverage area at the front and rear rims on the drag coefficient were investigated, where having a high coverage at the rear reduced drag the most.

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Autonomous anti-submarine warfare (ASW) sonars require robust automatic target classification algorithms. In conventional systems with human operators, the main role of such algorithms is to simplify the work of the sonar operator, while in autonomous systems, automatic target classification is crucial for the operative value of the systems. The emergence of the autonomous underwater vehicle (AUV), coupled with ongoing increase in computational power allowing more advanced realtime processing, has increased the interest in automatic target classification in the naval community.Detailed knowledge of the environment and an acoustic model may be used to estimate the probability that contacts are generated due to the signal processing induced phenomenon called false alarm rate inflation (FARI). This is a phenomenon often encountered in the littorals in presence of bathymetric features such as sea mounts and ridges. In this paper, we propose combining FARI information with track information, using two different machine learning techniques, k-Nearest neighbours and ID3.