Results for “Cooperative Operating Practices” — Page 6

NASA NTRS2013

NASA Activity Update for the 2013 Unmanned Vehicle Systems International (UVSI) Yearbook

This year s report offers a high level perspective on some of the UAS related activities in which NASA is involved, both internal and external to the agency. Internally, NASA issued UAS operational policy on certification of NASA UAS and aircrew. A team of NASA UAS experts and operators analyzed all current procedures and best practices to design the policy. An update to the agencies Aircraft Operations Management Manual incorporated a new chapter to address UAS planning, preflight operations, flight operations, flight crew requirements, airworthiness and flight safety reviews. NASA UAS are classified into three categories based on weight and airspeed. Aircrews, including observers, are classified by how they interface with the UAS, and the policy defines qualifications, training, and currency. The NASA flight readiness approval process identifies risks and mitigations in order to reduce the likelihood and/or consequence of the risk to an acceptable level. The UAS operations process incorporates all aspects of airworthiness, flight standards and range safety exactly the same processes used for NASA manned aircraft operations. NASA has two internal organizations that routinely operate UAS. The Science Mission Directorate utilizes UAS as part of its Airborne Science Program and is the most frequent operator of NASA UAS in both national and international airspace. The Aeronautics Research Mission Directorate conducts UAS flight operations in addition to conducting research important to the UAS community. This past year the Science Mission Directorate supported the Hurricane and Severe Storm Sentimental (HS3) Mission with two NASA Global Hawk platforms. HS3 is a five-year mission specifically targeted to investigate the processes that underlie hurricane formation. During the 2012 portion of this mission the Global Hawk overflew hurricanes Leslie and Nadine in the Atlantic Ocean completing 6 flights and accumulating more than 148 flight hours. Another multi-year mission was initiated last year when the Sensor Integrated Environmental Remote Research Aircraft (SIERRA) UAS began surveying faults in California s Surprise Valley. A team of scientists and engineers from the United States Geological Survey (USGS), NASA Ames Research Center, Central Washington University, and Carnegie Mellon University will measure magnetic fields using ground surveys and the SIERRA to map the geophysics below the surface of Surprise Valley. The data collected will be used to generate 3D maps of the geophysical data of the area. The Aeronautics Mission Directorate continues its collaboration with Boeing to conduct UAS flight operations of the X-48C, a modified version of the X-48B originally built by Cranfield Aerospace, United Kingdom. The Aeronautics Mission Directorate utilizes vehicles of this size for a wide variety of research studies. Most of these operations are conducted within restricted airspace. The Aeronautics Research Mission Directorate also sponsors the UAS in the National Airspace System (NAS) Project, which is working in close cooperation with the Federal Aviation Administration (FAA) to address critical challenges associated with routine UAS operations in civil airspace. The project is focused on separation assurance and collision avoidance systems and algorithms, command and control for non-military operations including spectrum allocation requirements, human system interaction issues, and safety and certification topics.

Bauer, Jeffrey E.↗

NASA NTRSDate not supplied

Demand Capacity Balancing at Vertiports for Initial Strategic Conflict Management of Urban Air Mobility Operations

Urban Air Mobility (UAM) is a new transportation concept that enables highly automated, cooperative, passenger or cargo-carrying air transportation services in and around urban areas. To achieve the high level of operational density and complexity desired by the UAM community, an airspace system that allows UAM operators to readily access and operate safely and efficiently in the airspace is needed. This airspace system will require air traffic management designed to reduce the risk of conflicts and loss of separation between UAM flights. In general, strategic conflict management is considered as the first layer of conflict management for safe flight operations to condition the traffic to reduce the need for airborne separation provision, the second layer of conflict management. Demand Capacity Balancing (DCB) is one of the concept components to achieve strategic conflict management. DCB strategically evaluates traffic demand and resource capacities to allow UAM operators to determine when, where and how they operate, while mitigating conflicting needs for airspace and vertiport capacity. DCB can be applied whenever UAM demand exceeds the capacity in airspace or at vertiports. As the UAM ecosystem evolves with advanced technologies and matured operational procedures, more complicated conflict management will likely be needed. In the current UAM ‘Concept of Operation (ConOps) 1.0’ operational stage defined by FAA, however, it will be meaningful to explore the demand capacity balancing at vertiports only, as an initial strategic conflict management approach for UAM operations because vertiport capacity seems to be a bottleneck of UAM traffic. For this research, we developed a demand-capacity imbalance detection and resolution service for UAM. This DCB service identifies the demand from operators and compares the demand to a given capacity at the shared resources (i.e., vertiports) over the upcoming time horizon which is divided into time bins having a constant interval. When a new flight plan is submitted, the algorithm embedded in the DCB service checks the available time bins based on the desired departure time and estimated arrival time at origin and destination vertiports, respectively. If the time bins for the originally desired times are already occupied by other flights (i.e., demand is at or above capacity), the algorithm finds the next available time bins for takeoff and landing and shifts the conflicting departure time to the earliest time that satisfies the capacity constraints at both origin and destination vertiports. The details of the algorithm will be described in the final manuscript. Figure 1 shows that the proposed DCB algorithm works well for a sample traffic scenario. In this example, a total of 144 flights, split between two operators, are planned over 2 hours, traveling 10 routes between five vertiports. In the heatmaps, the horizontal axis shows 12 time bins where each bin represents a 12-minute interval, and the vertical axis shows five vertiports. The number in each cell shows the number of operations, counting both departures and arrivals, at a specific vertiport in each time bin. For the given capacity of 2 operations/vertiport/bin, Figure 1 shows that the original demand sometimes exceeds the capacity, but the modified demand is reduced to the given capacity after resolving demand-capacity imbalances. When UAM flights are operated, it is expected that many practical issues would arise in the federated system architecture with multiple operators. UAM operators may experience a time synchronization issue due to communication delay between operator and vehicle. UAM vehicles would fly at different flight speeds, depending on vehicle models. Actual departure and arrival times can have large variations, compared to the schedule. The lead time from flight plan submission to desired departure time can vary by service type (e.g., regular shuttle service vs. on-demand service). Using the proposed DCB algorithm, we also investigated how the actual flight schedule and DCB performance are affected by these uncertainties such as unsynchronized times between operators, flight speed differences, lead time differences, and departure time errors. The final manuscript will include the background of this research work, the description of the DCB algorithm and its use cases with traffic scenarios. It will also provide the analytical results about the impact of various uncertainties that can occur in actual UAM operations on the DCB at vertiports, in terms of demand distribution changes, number of simultaneous operations, and delay propagation.

Urban Air Mobility↗