The new Federal Aviation Administration (FAA) Small Unmanned Aircraft rule (Part 107) marks the first national regulations for commercial operation of small unmanned aircraft systems (sUAS) under 55 pounds within the National Airspace System (NAS). Although sUAS flights may not be performed beyond visual line-of-sight or over nonparticipant structures and people, safety of sUAS operations must still be maintained and tracked at all times. Moreover, future safety-critical operation of sUAS (e.g., for package delivery) are already being conceived and tested. NASA's Unmanned Aircraft System Traffic Management (UTM) concept aims to facilitate the safe use of low-altitude airspace for sUAS operations. This paper introduces the UTM Risk Assessment Framework (URAF) which was developed to provide real-time safety evaluation and tracking capability within the UTM concept. The URAF uses Bayesian Belief Networks (BBNs) to propagate off-nominal condition probabilities based on real-time component failure indicators. This information is then used to assess the risk to people on the ground by calculating the potential impact area and the effects of the impact. The visual representation of the expected area of impact and the nominal risk level can assist operators and controllers with dynamic trajectory planning and execution. The URAF was applied to a case study to illustrate the concept.

In this paper, a novel hybrid electric regional aircraft is presented that strategically locates multiple electric and hybrid electric propulsors to obtain aerodynamic benefits. This concept is called the Parallel Electric-Gas Architecture with Synergistic Utilization Scheme (PEGASUS) aircraft. The use of the alternative propulsive systems coupled with their potential aerodynamic benefits presents modeling challenges for conventional aircraft analysis tools. These challenges are addressed by two methods that quantify the potential benefits of the PEGASUS concept. The results of both methods suggest that when compared to other hybrid electric regional aircraft, the PEGASUS concept has the potential to decrease the total energy required to complete a mission while also reducing the vehicle gross weight.

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The expected diversity in the kinds of vehicles that are currently appearing in the commercial space transportation sector raises questions about the possibility of improvements to current methodologies for licensing launch and reentry operations in use by the Federal Aviation Administration (FAA). These licensing procedures are designed to limit risks to public health and safety to acceptable levels and have served us well until now. Concerns may arise because the majority of methods in use are derived from expendable launch vehicles (ELVs) and the space shuttle era, and the possibility exists that they may be overly conservative. To investigate the extent to which the current methodology may be improved, an open source analysis environment for assessing the safety of the uninvolved public on the ground has been developed. The safety analysis environment is called Range Safety Assessment Tool (RSAT), it can be used for launch and reentry operations and, in principle, can be applied for all kinds of vehicle configurations that may be proposed. RSAT uses random sampling techniques to calculate statistics of interest associated with ground risks due to vehicle malfunctions. The risks being modeled include inert and explosive debris effects, debris toxic gas dispersion, and blast overpressure due to vehicle explosions. Special consideration is given to quantifying the risk to the uninformed public by computing the appropriate safety metrics, which in this case is the value of the expected casualty per mission (ET C). As part of this analysis environment, a 3-degree-of-freedom (DOF) trajectory optimization tool that uses a pseudospectral collocation method has been developed and is used to establish nominal trajectories for a number of different kinds of missions. In this paper, we present RSAT's major components and characteristics, and its predictive capabilities to analyze realistic launch and reentry accident scenarios. The output of the environment is verified and validated with existing data and previous calculations done with existing tools.

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