| 2006 Technology
Symposium > Modeling, Simulation, and Training
Modeling, Simulation, and Training
This area focuses on information technology to support training, and
technology and innovative application of modeling and simulation. The
information revolution is fueling changes in the workplace at an unprecedented
rate, and these changes are threatening to overwhelm conventional education
and training approaches. Fortunately, advanced instructional technologies
like embedded training and collaborative learning environments can help
warfighters and intelligence analysts adapt to these changes. Advances
in simulation infrastructure, interoperability architectures, and modeling
paradigms, have simplified the application of simulation, demonstrated
the feasibility of building simulations from reusable components, and
otherwise facilitated a revolution in simulation application.
Airport Capacity Through Simulation (ACATS) Transition
John Barrer, Principal Investigator
Location(s): Washington
Problem
Existing approaches cannot model airports with complex runway layouts or new procedures for handling traffic. Most generalized simulation models are too complex for analyzing runway capacity in a short timeframe. Previous research examined a generalized runway capacity model, based on simulation, that could be set up quickly and be flexible enough to model any airport.
Objectives
Technical enhancements to ACATS will permit better modeling of delay, multiple arrival streams, and multiple airport interactions. These enhancements require that the basic algorithms be modified to address specific scenarios. The ACATS model will allow analysts to study the effects on throughput of changes in procedures and new runway configurations, and to do this more rapidly and precisely than before.
Activities
We will modify ACATS to handle a more complex schedule, compute the resulting delays, and accommodate multiple arrival streams. We will also resolve a technical problem that is causing misleading visual representations of the results.
Impact
Our analysts will use the new ACATS model in developing airport capacity estimates for our sponsors. The model provides a graphical representation of the operations, giving users confidence that the capacity estimates are feasible and valid. The model's ability to balance traffic between runways in complex layouts provides both a better estimate of capacity and insight into correct runway usage.
^TOP
Application of Cognitive Agents to NAS Models and Real-Time Simulations
Steven Estes, Principal Investigator
Location(s): Washington
Problem
CAASD is commonly asked to determine what impacts new technologies may have on the NAS. While metric-based analysis can resolve many such questions, cognitive agents embedded in system models and real-time simulations must ultimately be used to answer what are essentially cognitive questions. Specifically, what are the effects of new technologies on controller/pilot workload, efficiency, and error rates?
Objectives
To address these issues we will create a library of autonomous cognitive agents: representations of humans that interact with and react to the environment in the same way a human would. These will be implemented within NAS models or real-time simulations.
Activities
We will begin by gathering requirements. The first milestone is the development and testing of a cognitive architecture. This will be followed by the creation of our first cognitive agent, which will perform one or two very simple controller tasks. Finally, we will construct tools that allow this agent to communicate with the simulation environment.
Impact
Cognitive agents allow for macro-level system results based in part on micro-level cognitive data. These human-like agents permit us to predict, for example, if using enhanced delegated separation concepts can increase controller productivity to a point that allows controllers to handle additional sectors. Within real-time environments, cognitive agents can facilitate evaluations by acting as pilots, controllers, or traffic managers.
^TOP
Characterizing Operational Impacts of UAS
Matt DeGarmo, Principal Investigator
Location(s): Washington
Problem
Unmanned aircraft (UA) operations being proposed for flights in the NAS create uncertainties as to their potential effects on existing air traffic flows, airspace capacity, NAS infrastructure, manned aircraft business models, federal regulations, and air traffic procedures.
Objectives
The objective of this research is to determine a range of impacts on the future NAS. The work will be based on a set of underlying assumptions concerning UA activities and system linkages with the NAS, coupled with an understanding of economic demand predictions.
Activities
Activities will include a detailed review and analysis of past and ongoing forecasts concerning UA industry demands. Further, we will develop a taxonomy of impact areas. This taxonomy will be aligned with the economic forecast data to determine the range of potential impacts.
Impact
This research will assist the FAA and other government policy makers to understand possible impacts UA may have on the future NAS and to prepare, plan, and prioritize activities and budgets associated with UA operations. Additionally, results will provide aviation stakeholders with indicators concerning the potential scope and effects of UA operations on their respective organizations.
^TOP
Enhanced Future Air Traffic Timetable Estimator
Michael Wells, Principal Investigator
Location(s): Washington
Problem
When and where in the United States will air traffic (in terms of either operations or enplanements) eventually exceed system capabilities, requiring additional investments or staffing? How do forecasts vary with differing assumptions about local economic growth and changes in demographics, airframe technology, airport hubs, and changing networks? How do day-to-day and season-by-season forecasts change, and affect airport schedules?
Objectives
The Future Air Traffic Timetable Estimator (FATE) generates future daily timetables from origin-destination markets using forecasts of local demographics and economies, and information on people and airlines. We will extend FATE to include endogenous choice of airports, improved choice of aircraft, variable load factor assumptions, and airport capacity constraints. We will also streamline the computer code to enhance speed and transparency.
Activities
Passenger data (from the T-100 and 10% Ticket Survey) combined with network and aircraft choices will be analyzed and modeled. Forecast airport capacity will constrain the allocation of market share and hourly flight scheduling. We will consolidate the existing flight scheduling algorithm into a single program and combine it with the code for choice of route and aircraft type.
Impact
FATE has been used in the FAA's Future Airport Capacity Task and in CAASD's analyses of the FAA's Operational Evolution Plan and Controller-Pilot Data Link Communications program. Within the FAA, the FATE team is working with the Air Traffic Organization and the Office of Aviation Policy and Plans on designing and supplementing their existing forecasting methodologies.
^TOP
Globally Distributed ATC Simulation Environment
Patti Liguori, Principal Investigator
Location(s): Washington and Bedford
Problem
In prior work, this project led the development of AviationSimNet(TM), in collaboration with NASA, FAA, United Parcel Service, and others in the aviation community. There is a need now to extend this standard and apply it toward building the next-generation air transportation system.
Objectives
This work will enable the aviation community to collaboratively extend the AviationSimNet specification and to implement it in their laboratories so that simulation assets can be shared among the community members.
Activities
This project will bring additional flight simulators into the AviationSimNet network, extend the specification to include new classes of simulators, and develop tools to aid in building, configuring, replaying, testing, running, monitoring, and coordinating simulations in the AviationSimNet environment. To help accomplish this the project will form and moderate two working groups: one focused on the standard itself and one on strategy and applications.
Impact
This work will enable new types of research across the global aviation community by providing opportunities to conduct real-time distributed aviation simulations. The standard, once mature, will be of value to non-aviation simulations as well.
^TOP
Modeling and Simulation of Large-Scale Networks
Gary Comparetto, Principal Investigator
Location(s): Washington
Problem
To ensure that the Global Information Grid (GIG) can meet its requirements for secure, reliable mobile communications, the DoD must model and simulate the end-to-end performance of large-scale heterogeneous communications networks, using realistic operational scenarios and traffic loads. Limitations on run time and system memory have so far made this impossible for scenarios involving more than about 100 nodes and traffic loads greater than about 10 Mbps.
Objectives
We will investigate, quantify, and document methods to improve simulation run time and memory footprint performance. Additionally, we will develop a modeling and simulation (M&S) testbed that can simulate the behavior of large-scale, heterogeneous communications networks and enable analyses of critical design and engineering issues.
Activities
This project will investigate and quantify the improvements achievable by using various simulation kernels and their parallel processing capability, co-simulation techniques and ways to share information between simulation kernels, and OPNET's High-Level Architecture interface. We will develop an M&S testbed that incorporates our findings and demonstrate it using realistic large-scale communications network scenarios based on the DoD Transport vision.
Impact
This research effort will result in the development and demonstration of a large-scale communications network M&S testbed that exhibits the best achievable run time and system memory performance using a new combination of processes, algorithms, and technology. Timely, accurate, and effective simulation-based analyses will help the DoD to achieve its vision for the GIG.
^TOP
|
