Airspace concept evaluations using FASTE-CNS as a simulation analysis tool

As NASA speculates on and explores the future of aviation, the technological and physical aspects of our environment increasingly become hurdles that must be overcome for success. Several NASA research partners have purposed research into methods for overcoming some of these selected hurdles. The task of establishing a common evaluation environment was placed on NASA's Virtual Airspace Simulation Technologies (VAST) project (sub-project of Virtual Airspace Modeling and Simulation Project). Their response was the development of the Airspace Concept Evaluation System (ACES). As one examines the ACES environment from a communication, navigation or surveillance (CNS) perspective, the simulation environment made no provisions for realism in the simulation of CNS. To truly evaluate these concepts in a realistic sense, the contributions/effects of CNS must be part of the ACES. NASA Glenn Research Center (GRC) has supported the Virtual Airspace Modeling and Simulation (VAMS) project through the continued development of CNS models and analysis capabilities that supports the ACES environment. NASA GRC initiated the development a communications traffic loading analysis tool, called the future aeronautical subnetwork traffic emulator for communications, navigation and surveillance (FASTE-CNS), as part of this support. This tool allows for forecasting of communications load with the understanding that, there is no single, common source for loading models used to evaluate the existing and planned communications channels, and that consensus and accuracy in the traffic load models is a very important input to the decisions being made on the acceptability of communication techniques used to fulfil the aeronautical requirements. Leveraging off the existing capabilities of the FASTE-CNS tool, GRC has called for FASTE-CNS to have the functionality to pre- and post-process the simulation runs of ACES to report on instances when traffic density, frequency congestion or aircraft spacing/distance violations have occurred. The integration of these functions require that the CNS models used to characterize these avionics systems be of higher fidelity and better consistency then is present in FASTE-CNS system. This paper explores the capabilities of FASTE-CNS with renewed emphasis on the enhancements added to perform these processing functions; the fidelity and reliability of CNS models necessary to make the enhancements work.