A major focus of current unmanned systems operations is assessing the inherent risk associated with a mission. Efforts to integrate unmanned systems into the national airspace require manufacturers be able to calculate the risk of a mission in terms of human safety. Threats to human safety from midair collisions and ground strikes are the focus of the risk model. The projects intent is to assist in determining applications that leverage the strengths of current unmanned aircraft technology while mitigating the weaknesses so as to meet or exceed the safety and economic viability of manned aircraft. The validity of the risk model is demonstrated by comparison to historical data when available. The intended use of the tool is discussed and risk assessments are presented for several example scenarios. Resources for gathering the required information are surveyed and material is developed to aid a general audience in performing a risk assessment. Nomenclature A b Average building area (km 2) ADS-B Automatic Dependent Surveillance-Broadcast A LH b , A LH p Lethal area for buildings and pedestrians in a horizontal crash (due to system failure) (km 2) A LV b , A LV p Lethal area for buildings and pedestrians in a vertical crash (due to midair collision) (km 2) A ops Operating area size (km 2) C midair Rate of aircraft crashes due to midair (transient & in-fleet) collisions (crashes/hour) C2 Command and Control D bldg Expected number of fatalities when a UA collides with a building D ped Expected number of fatalities when a UA collides with a pedestrian (in range [0, 1]) F f at Fatalities per flight hour F f at,p Fatalities due to system failures (fatalities/hour) F f at,midair Fatalities due to midair collisions (fatalities/hour) F ped Collision rate of UA fleet with pedestrians per hour (collisions/hour) F bldg,midair Collision rate with buildings due to midair collision (collision/hour) F bldg,p Collision rate with buildings due to system failure (collision/hour) F ped,midair Collision rate with pedestrians due to midair collision (collision/hour) F ped,p Collision rate with pedestrians due to system failure (collision/hour) F bldg Collision rate of UA fleet with buildings per hour (collisions/hour) F f leet Collision rate of UA fleet of other UA within fleet per flight hour (collisions/hour) F transient Collision rate of a single UA w/o avoidance & stationary transient AC (collisions/flight-hour) F transient Collision rate of UA fleet w/o avoidance & moving transient AC (collisions/hour) F transient Collision rate of UA fleet with transient AC per hour (collisions/hour) H Total aircraft flight hours recorded in historical data (aircraft-hours) H b Average building height (km) H p Average pedestrian height (km)

In order to operate in the national airspace, an aircraft system must have documentation and analysis to show that it can operate at a satisfactory level of safety. For traditional manned aircraft systems, this is equivalent to operating a reliable system. However with Unmanned Aerial Systems (UAS), a relatively unreliable system can safely be operated provided that the risk to bystanders on the ground is sufficiently low. This paper presents a set of design tools and methodologies which can be used to assess the risk associated with operating an UAS in a potentially populated area. The intended use of the tool is discussed and a risk assessment is provided for an existing UAS.

Artificial ice shapes of various geometric fidelity were tested on a wing model based on the Common Research Model. Low Reynolds number tests were conducted at Wichita State University's Beech Memorial Wind Tunnel, and high Reynolds number tests were conducted at ONERA's F1 wind tunnel. The aerodynamic performance data from the two facilities were compared at matched or similar Reynolds and Mach number to ensure that the results and trends observed at low Reynolds number could be applied and continued to high Reynolds number. For both clean and iced configurations, the data from Wichita State University and F1 agreed well at matched or similar Reynolds and Mach numbers. The lift and pitching moment curves agreed very well for most configurations. There appeared to be 0.2-0.3° offset in the angle of attack between the Wichita State University and F1 data, possibly due to different flow angularities in the test sections of the two facilities. There was also an offset in the drag values between the two facilities from an unknown cause. Overall, the data compared very well between the low Reynolds number test at Wichita State University tunnel and the high Reynolds number test at F1. This indicated that data from the low Reynolds number tests could be used to understand iced-swept-wing aerodynamics at high Reynolds number. Nomenclature b = Model span c = Model chord x = Model coordinate in chordwise direction y = Model coordinate in spanwise direction CD = Drag coefficient CD,0.6 = Drag coefficient at CL = 0.6 CD,min = Minimum drag coefficient CL = Lift coefficient CL.max = Maximum lift coefficient CL,use = Usable lift coefficient CM = Pitching moment coefficient Cp = Pressure coefficient CRM65 = Common Research Model 65% scale M = Mach number MAC = Mean aerodynamic chord ONERA = Office National d'Etudes et Recherches Aérospatiales p0 = Freestream total pressure q = Freestream dynamic pressure Re = Reynolds number RLE = Removable leading edge  = Angle of attack stall = Stall angle of attack  = Sweep angle

Aerodynamic assessment of icing effects on swept wings is an important component of a larger effort to improve three-dimensional icing simulation capabilities. An understanding of ice-shape geometric fidelity and Reynolds and Mach number effects on the iced-wing aerodynamics is needed to guide the development and validation of ice-accretion simulation tools. To this end, wind-tunnel testing was carried out for a 13.3%-scale semispan wing based upon the Common Research Model airplane configuration. The wind-tunnel testing was conducted at the ONERA F1 pressurized wind tunnel with Reynolds numbers of 1.6×10 6 to 11.9×10 6 and Mach numbers of 0.09 to 0.34. Five different configurations were investigated using fully 3D, high-fidelity artificial ice shapes that maintain nearly all of the 3D ice accretion features documented in prior icing-wind tunnel tests. These large, leadingedge ice shapes were nominally based upon airplane holding in icing conditions scenarios. For three of these configurations, lower-fidelity simulations were also built and tested. The results presented in this paper show that while Reynolds and Mach number effects are important for quantifying the clean-wing performance, there is very little to no effect for an iced-wing with 3D, high-fidelity artificial ice shapes or 3D smooth ice shapes with grit roughness. These conclusions are consistent with the large volume of past research on icedairfoils. However, some differences were also noted for the associated stalling angle of the iced swept wing and for various lower-fidelity versions of the leading-edge ice accretion. More research is planned to further investigate the key features of ice accretion geometry that must be simulated in lower-fidelity versions in order to capture the essential aerodynamics. Determine the level of ice-shape geometric fidelity required for accurate aerodynamic simulation of sweptwing icing effects. This paper, along with a series of companion papers, 5-7 provides initial results for these remaining objectives. Additional wind-tunnel testing and future publications are planned. The approach used to accomplish these objectives has been successfully carried out in previous icing aerodynamics studies of straight wings and airfoils.In past work, geometric representations of ice accretion have been attached to wings and models and tested in dry-air wind tunnels or in flight. These geometric representations are known as "artificial ice shapes" or "iceaccretion simulations." The various methods and geometric fidelities associated with developing artificial ice shapes have been investigated in a previous NASA-ONERA collaborative research effort called "SUNSET1." 8 Since that time, a new approach for producing high-fidelity artificial ice shapes have been developed using 3-D scanning and rapid-prototype manufacturing (RPM). 9 In past studies of icing performance effects on airfoils, systematic investigations of Reynolds and Mach number effects were conducted. [10][11][12][13][14][15][16] Over the course of many years, it was fo...