Skip Navigation
Small Business Innovation Research/Small Business Tech Transfer

Selecting Days for Concept and Technology Evaluation in SMART-NAS Test-Bed Scenario Generation

Completed Technology Project
122 views

Project Description

Final Summary Chart Image
Crown Consulting, Inc. (CCI) will investigate and demonstrate methods to enable rapid selection of days for scenario generation in the development and evaluation of Air Traffic Management (ATM) concepts and technologies (C&T) in the NASA developed Shadow Mode Assessment using Realistic Technologies for the National Airspace System (SMART-NAS) Testbed (SNTB). The proposed capability will enable the rapid generation of highly operationally relevant scenarios for use in the development and evaluation of technology demonstrators such as the NASA Airspace Technology Demonstrator (ATD)-2 and ATD-3, as well as future ATM concepts such as Unmanned Aerial System (UAS) Traffic Management (UTM), and Air Traffic Management Exploration (ATM-X). The latter includes new mid-term operational concepts such as Integrated Demand Management (IDM), and far-term operational concepts such as Urban Air Mobility (UAM) and Increasingly Diverse Operations (IDO), which considers the integration of supersonic aircraft, spacecraft and UAS into the National Airspace System (NAS) The proposed innovation seeks to augment the scenario generation capability of NASA’s SNTB with methods and tools for selecting traffic, winds and weather based on the needs of the experiment allowing for highly operationally relevant scenarios. These methods and tools would actively categorize incoming and historical data using advanced machine-learning algorithms, allowing fast access to NAS streaming and legacy data in a big-data warehouse such as the NASA’s ATM-Data-Warehouse through queries generated via a simple user interface for specifying desired characteristics. In addition to historical data, processed data such as benefit metrics, generated by SNTB simulations implementing the concept and technology of interest, can also be categorized by machine-learning algorithms for selecting days to generate scenarios for HITL tests to verify conditions for most or least C&T benefit. More »

Anticipated Benefits

Project Library

Primary U.S. Work Locations and Key Partners

Technology Transitions

Light bulb

Suggest an Edit

Recommend changes and additions to this project record.
^