Award Data

The USAF Material Command (AFMC) and the Defense Logistics Agency (DLA), Defense Microelectronics Activity (DMA) has a critical need to support military planners in establishing a cost effective approach to selecting components and identifying system behavior during the design process, minimizing expensive testing and design failures. Current test facilties are expensive to run as well as time co ...

Future USAF vehicles (missiles/fighter aircrafts) will have to operate under high altitude flight conditions in order to reach global targets and/or to meet unforeseen threats to the security of USA. A new accurate engineering code is needed to determine quickly the aerothermal characteristics of the vehicle and optimizeits shape, trajectory, and/or performance. The maneuvering vehicle will have ...

Station keeping operations of satellites are primarily planned and executed from the ground control centers. With future trends towards operating satellites as part of a larger constellation, both in geostationary orbit (collocation) and lower Earth orbits, the availability of autonomous station keeping will become more critical. This proposal envisions the design of one such innovative autonomo ...

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TMSSequoia proposes to develop a software system to allow conversion of paper-based legacy engineering drawings, technical manuals and reports, to a digital format. This process will include automatic extraction of data from the scanned images that is input into a Configuration Management Information System (CMIS) for storage, query and retrieval. TMSSequoia will then identify current software t ...

To field characterize laser systems the laser power, beam divergence, intensity profile and spot size of the laser must be measured with a robust, vibration insensitive system. While some of these parameters can be obtained using conventional components, beam divergence is difficult to measure because of the complexity and vibration sensitivity of typical interferometry based elements. We propos ...

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Simulation-based optimization techniques that enlist reinforcement learning controllers are ideally suited for complex and multi-objective optimization problems that cannot be solved easily using traditional techniques, especially when the stochastic natures or the environment, resources, and external interactive entities are taken into account. Reinforcement learning based on incremental value i ...