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The Oculus sensor deployment platform research and development was conducted by the Center for Industrial Research Applications (CIRA) at West Virginia University (WVU). Oculus is a cost-effective reconnaissance platform available for use on a C-130 aircraft variants B through H. This system has a base of two standard MIL-463L pallets, an operators station and sensor pallet, that are loaded and rolled into place. The fore pallet or operator’s station holds three controllers, processing equipment for sensor data, and power regulation equipment. The aft pallet, or sensor pallet, located on the cargo ramp, contains sensors that, when in position, can be pointed at the ground. These reconnaissance sensors are situated in a pod that is mounted to four arms to allow more than 200 degrees of rotation, and are mounted to a movable plate that allow for linear movement.

The development of a standardized roll-on, roll-off (RoRo) sensor pallet system for a C-130 aircraft was conceived by the National Guard and the Counter Narco-Terrorism Technology Development Office to assist in counterdrug reconnaissance activities within the United States and surveillance and reconnaissance missions worldwide. West Virginia University was contracted to perform the design and development of this system because of their innovative design ideas. Before development, the design parameters were established by these two DoD agencies, their mission requirements and by the limitations of the C-130 aircraft. These limitations include using Commercial off the Shelf (COTS) and Government off the Shelf (GOTS) items when developing the system that must be universal on all C-130 aircrafts variants B thru H. Further design criteria are by the limitations of the C-130 aircraft and its existing mission requirements.

Enhancing the capabilities of established airframes to meet expanded mission requirements is preferential to the design of specialized aircraft. The high cost associated with the research and development of a specialized aircraft platform has shifted the concentration towards the modification of existing aircraft to support multiple C4ISR missions. The recently developed Oculus sensor deployment system is one such example of this trend, providing a fully integrated aerial visual enhancement platform with multi-mission capabilities. This paper provides a short survey of the Oculus sensor pallet system and overviews some of the multiple guidelines used which ensure that various remote sensing technologies may be securely and simultaneously deployed.

The downwash of the prop-rotor blades of the Bell/Boeing V-22 “Osprey” in hover mode creates an undesirable negative lift on the wing of the aircraft. This downforce can be reduced through a number of methods. Neglecting all other effects, such as power requirements, this research investigated the feasibility of using circulation control, through blowing slots on the leading and trailing edge of the airfoil to reduce the wake profile under the wing. A model was built at West Virginia University (WVU) and tested in a Closed Loop Wind Tunnel. The airfoil was placed normal to the airflow using the tunnel air to simulate the vertical component of the downwash experienced in hover mode. The standard hover mode flap angle of 67 degrees was used throughout the testing covered in this paper. All of these tests were conducted at a free stream velocity of 59 fps, and the baseline downforce on the model was measured to be 5.45 lbs.

In this analysis the structural integrity of the rotational system of a standardized roll-on, roll-off sensor pallet system was authenticated. The driving force behind this analysis was to ensure the structural integrity of the system and to locate the areas with optimization potential. This process will ideally lead to the weight reduction of individual components thereby allowing for the transportation of greater cargo during flight. Scaling down of these excessive areas will also allow for a reduced production cost and an increase in efficiency of the system. The study was comprised of the failure susceptibility of the individual components of the system. The major results include the optimization potential of individual components, as well as strategically rating and categorizing the failure capability of the components.