Optical Properties of Brown-outs from Rotocraft Landings in Desert Terrain

Project: Research project

Project Details


Optical Properties of Brown-outs from Rotocraft Landings in Desert Terrain Rotorcraft landings in desert terrain can be dangerous due to dust lofted by rotor downwash. Swirling dust clouds can envelope the aircraft, leading to pilot disorientation. Various technological solutions have been developed, including"see and remember" ladar systems, and dust penetrating radars. The basic premise of this proposal is that the morphological (size and shape) details of the aerosols in rotorcraft dust clouds should be investigated to explore their effect on sensors during desert landings. A combination of in situ particle measurements, including electron microscopy, and lidar remote sensing will be performed for landing approaches of a UH-H1"Super Huey" in light brownout conditions in the Arizona desert. A modern laser radar using 1.5 micron wavelength will produce spatially-resolved (20 m range gates) radial velocities and backscatter parameters. Although the fluid dynamics of rotorcraft dust clouds has been studied photogrammatically at the Yuma Proving Ground, we expect that three-dimensional, scanning Doppler lidar can be used to obtain an improved picture of the interior fluid dynamics of the dust cloud (though not full brownout). We expect that the air flow within the dust cloud leads to variations of the aerosols and that morphology may be a strong function of position. For example, predicting clear zones or regions of diminished visibility for future sensor systems may also depend upon particle shapes. This proposal would perform a 9- month demonstration field experiment and initial data analysis, laying the foundation for a multiyear research program to study how optical properties are related to aerosol morphology for brownout scenarios.
Effective start/end date9/1/145/31/15


  • DOD-ARMY-ARL: Army Research Office (ARO): $50,000.00


Explore the research topics touched on by this project. These labels are generated based on the underlying awards/grants. Together they form a unique fingerprint.