A computational fluid dynamics (CFD) method has been applied to gear configurations with and without shrouding. The goals of this work have been to validate the numerical and modeling approaches used for these applications and to develop physical understanding of the aerodynamics of gear windage loss. Several spur gear geometries are considered, for which experimental data are available. Various canonical shrouding configurations and free spinning (no shroud) cases are studied. Comparisons are made with experimental data from open literature, and data recently obtained in the NASA Glenn Research Center Gear Windage Test Facility, Cleveland, OH. The results show good agreement with the experiment. The parametric shroud configuration studies carried out in the Glenn experiments and the CFD analyses elucidate the physical mechanisms of windage losses as well as mitigation strategies due to shrouding and newly proposed tooth contour modifications.

A spiking neural network model is used to identify characters in a character set. The network is a two layered structure consisting of integrate-and-fire and active dendrite neurons. There are both excitatory and inhibitory connections in the network. Spike time dependent plasticity (STDP) is used for training. The winner take all mechanism is enforced by the lateral inhibitory connections. It is found that most of the characters are recognized in a character set consisting of 48 characters. The network is trained successfully with increased resolution of the characters. Also, addition of uniform random noise does not decrease its recognition capability.

The need for intelligent unmanned vehicles has been steadily increasing. These vehicles could be air-, ground-, space-, or sea-based. This paper will review some of the most common software systems and methods that could be used for controlling such vehicles. Early attempts at mobile robots were confined to simple laboratory environments. For vehicles to operate in real-world noisy and uncertain environments, they need to include numerous sensors and they need to include both reactive and deliberative features. The most effective software systems have been hierarchical or multi-layered. Many of these systems mimic biological systems. This paper reviews several software approaches for autonomous vehicles. While there are similarities, there are differences as well. Most of these software systems are very difficult to use, and few of them have the ability to learn. Autonomous vehicles promise remarkable capabilities for both civilian and military applications, but much work remains to develop intelligent systems software which can be used for a wide range of applications. In particular there is a need for reliable open-source software that can be used on inexpensive autonomous vehicles

This paper discusses the use of the Soar cognitive architecture to control gait selection of a six-legged robot using force sensors attached to its feet. The hardware platform also incorporated sonar sensors, a GPS receiver, and a Webcam. The Soar cognitive architecture was used to control the robot, and the Java programming language was used as middleware between Soar and the hardware components. The force sensors were attached and the force profile was examined. Soar productions for controlling the gait were developed. Experiments were performed on terrain which has random holes and obstacles. Two cases were conducted: walking using Soar to select the gait, and using Soar with the chunking turned on for learning.