The AACV model combines a variety of new and existing technologies and procedures, many of which have been studied independently of the others. There are a number of people studying the ability of flight crews to self-separate using tools such as CDTI and CD&R. The use of ADS-B for surveillance, though still in its infancy, is also not novel: in addition to the Capstone project mentioned earlier, the FAA’s Safeflight 21 program13 and the North European ADS-B Network or NEAN/NUP project14 have collected a great deal of in-flight ADS-B performance data. There is also a lot of work on the use of GPS and GPS augmentation schemes such as WAAS to provide approach capability at airports currently lacking an instrument approach. The concepts of procedural separation and the one-in/one-out use of a volume of airspace near an airport are well proven in today's NAS. Since we were able to draw on this large body of research and operational experience, we have developed a batch simulation to validate the systemic attributes of the AACV operational concept.
The sequencer components of this batch simulation are designed to be compatible with pilot-in-the-loop simulations developed at NASA Langley15 for study of pilot workload and the evaluation of new procedures. These same software elements can be used in conjunction with other simulation elements for pilot/controller studies to validate procedures developed for transition from traditional approaches and operations to these automated procedures. A combination of this batch processing capability and piloted simulations are needed to identify improvements in the operational concept necessary for further development of the AACV.
3.1 Simulation Description
The automated airport simulation has four primary functions: traffic generation to introduce aircraft into the environment with an appropriate mix of initial conditions, trajectory estimation (and therefore calculation of time on approach), a sequencer for the determination and dissemination of sequence, a delay function that will insert service re-requests from aircraft initially denied service, and data collection.
The automated airport operation system was modeled as a single server queue as shown in Figure 2.
Aircraft are randomly introduced to the simulation based on an exponentially distributed average inter-arrival time, λ. Aircraft position at request time is assigned randomly within an annulus designated by the outer limits of the ACV plus some maneuvering space and the modeled ADS-B reception limit. An appropriate preferred IAF is assigned by the simulation based on geometric position relative to a standard T RNAV approach as defined by the FAA.16
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