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An overview of the simulation facilities at SimLabs at NASA Ames. Main focus is on the Vertical Motion Simulator or VMS, also brief descriptions of other simulators. Discussion of the history and general specifications of the VMS. Also, provide an overview of past research and training conducted on the simulators, and some future developments with a focus on AR/VR technologies.
NASA Ames SimLabs is home to unique simulation facilities capable of a wide range of aerospace systems research. This presentation will focus on the Vertical Motion Simulator with thoughts on future AR/VR applications and research.
Informal presentation to group of Masters students about the work at NASA SimLabs. Talking about FFC/VMS and my contributions there.
The Flight Systems and Simulation Research Laboratory (Simlab) at the NASA Ames Research Center, utilizes an extensive network of video image generation, delivery, processing, and display systems coupled with a large amplitude Vertical Motion Simulator (VMS) to provide a high fidelity visual environment for flight simulation research. This paper will explore the capabilities of the current Simlab video distribution system architecture with a view toward technical solutions implemented to resolve a variety of video interface, switching, and distribution issues common to many simulation facilities. Technical discussions include a modular approach to a video switching and distribution system capable of supporting both coax and fiber optic video signal transmission, video scan conversion and processing techniques for lab observation and recording, adaptation of image generation and display system video interfaces to industry standards, an all raster solution for 'glass cockpit' configurations encompassing Head up, Head-down, and Out-the-Window display systems.
The Simulation Laboratories (SimLabs) at NASA Ames support a wide range of research in aerospace vehicles, aerospace systems and operations, human factors, and aviation safety. Our state-of-the-art simulation facilities are available to develop your future concepts and technologies.
The Vertical Motion Simulator (VMS) facility at the NASA Ames Research Center combines the largest vertical motion capability in the world with a flexible real-time operating system allowing research to be conducted quickly and effectively. Due to the diverse nature of the aircraft simulated and the large number of simulations conducted annually, the challenge for the simulation engineer is to develop an accurate real-time simulation in a timely, efficient manner. The SimLab facility and the software tools necessary for an operating simulation will be discussed. Subsequent sections will describe the development process through operation of the simulation; this includes acceptance of the model, validation, integration and production phases.
"Gate Turnaround" plays a key role in the National Air Space (NAS) gate-to-gate performance by receiving aircraft when they reach their destination airport, and delivering aircraft into the NAS upon departing from the gate and subsequent takeoff. The time spent at the gate in meeting the planned departure time is influenced by many factors and often with considerable uncertainties. Uncertainties such as weather, early or late arrivals, disembarking and boarding passengers, unloading/reloading cargo, aircraft logistics/maintenance services and ground handling, traffic in ramp and movement areas for taxi-in and taxi-out, and departure queue management for takeoff are likely encountered on the daily basis. The Integrated Gate Turnaround Management (IGTM) concept is leveraging relevant historical data to support optimization of the gate operations, which include arrival, at the gate, departure based on constraints (e.g., available gates at the arrival, ground crew and equipment for the gate turnaround, and over capacity demand upon departure), and collaborative decision-making. The IGTM concept provides effective information services and decision tools to the stakeholders, such as airline dispatchers, gate agents, airport operators, ramp controllers, and air traffic control (ATC) traffic managers and ground controllers to mitigate uncertainties arising from both nominal and off-nominal airport gate operations. IGTM will provide NAS stakeholders customized decision making tools through a User Interface (UI) by leveraging historical data (Big Data), net-enabled Air Traffic Management (ATM) live data, and analytics according to dependencies among NAS parameters for the stakeholders to manage and optimize the NAS performance in the gate turnaround domain. The application will give stakeholders predictable results based on the past and current NAS performance according to selected decision trees through the UI. The predictable results are generated based on analysis of the unique airport attributes (e.g., runway, taxiway, terminal, and gate configurations and tenants), and combined statistics from past data and live data based on a specific set of ATM concept-of-operations (ConOps) and operational parameters via systems analysis using an analytic network learning model. The IGTM tool will then bound the uncertainties that arise from nominal and off-nominal operational conditions with direct assessment of the gate turnaround status and the impact of a certain operational decision on the NAS performance, and provide a set of recommended actions to optimize the NAS performance by allowing stakeholders to take mitigation actions to reduce uncertainty and time deviation of planned operational events. An IGTM prototype was developed at NASA Ames Simulation Laboratories (SimLabs) to demonstrate the benefits and applicability of the concept. A data network, using the System Wide Information Management (SWIM)-like messaging application using the ActiveMQ message service, was connected to the simulated data warehouse, scheduled flight plans, a fast-time airport simulator, and a graphic UI. A fast-time simulation was integrated with the data warehouse or Big Data/Analytics (BAI), scheduled flight plans from Aeronautical Operational Control AOC, IGTM Controller, and a UI via a SWIM-like data messaging network using the ActiveMQ message service, illustrated in Figure 1, to demonstrate selected use-cases showing the benefits of the IGTM concept on the NAS performance.
An Integrated Gate Turnaround Management (IGTM) prototype was developed at NASA Ames Simulation Laboratories (SimLabs) using Dallas Ft. Worth International Airport (DFW) to demonstrate the IGTM concepts feasibility and benefits. The simulation architecture includes: the IGTM controller, an Airline Operations Control (AOC) application, Big DataAnalytics Input (BAI) application, a terminal traffic simulation or known as NASA-developed Surface Operation Simulator and Scheduler (SOSS), and a Database Server. ActiveMQ, a Java messaging service, was used to emulate the System Wide Information Management (SWIM) data network messaging. This paper describes the modeling and simulation of the IGTM concept, and illustrates selected use cases to demonstrate the feasibility and benefits of the IGTM concept for optimizing gate turnaround operations.
Characterization of the performance of Air Traffic Management Exploration (ATM-X) TestBed integration environment has been investigated and documented for one system configuration for progressively increasing traffic. Several statistical parameters were used to assess the performance of the TestBed distributed system such as mean, standard deviation, skewness, and kurtosis of latency, and update rate for aircraft state messages that are transmitted through the simulated system under investigation. It is necessary to assess the performance characteristics of distributed systems in terms of the indicated statistical parameters mentioned above. It is critical to verify the system performance with respect to a researcher’s required system performance. Computer host specifications are documented in terms of Central Processing Unit (CPU) clock speed and core count. Transmission Control Protocol/ Internet Protocol (TCP/IP) message protocol was used for data transmission. The system network topology also contributes to the latency and update rate variations from the one imposed by the data source. The motivation for selecting the TestBed infrastructure as the focus of this study can be attributed to the number of services and capabilities it provides that help simplify the process of preparing and conducting a simulation. These capabilities include an easy to use GUI for simulation configuration, access to TestBed library by the end-user of other simulation software components, a modular adapter paradigm that allows simple connectivity of external software to TestBed, connectivity with other simulation laboratories, and a Software Development Kit (SDK) for quicker development. Two types of traffic generators, Air Traffic Generator (ATG) and Multi Aircraft Control System (MACS) were used to generate messages that were injected into the TestBed distributed environment. Eight different air traffic scenarios with progressively increasing loads were generated for each air traffic simulator. The corresponding air traffic loads between the two simulators had an identical number of aircraft per scenario, but different flight plans. It was observed that the performance of MACS degraded for air traffic scenarios containing more than 200 aircraft (37.5 KB/s nominal throughput). However, ATG performed adequately under all tested air traffic loads up to 1200 aircraft (225. KB/s nominal throughput). The tests show that MACS exhibits better latency performance with smaller aircraft loads when compared to ATG. The tests also show that the TestBed infrastructure successfully transmits 1200 aircraft without significant degradation of its performance. From the latency trends for both MACS and ATG, it is clear that as aircraft load increases, the latency in the system increases as well as its standard deviation. Likewise, the trends for the update data rate for both MACS and ATG show that as the aircraft load increases, so does the standard deviation and mean of the update rates which can be attributed to the performance of MACS and ATG applications. The analysis of the results of this study have proven that the overall system performance is dependent on the individual performance of each system component that is connected to TestBed, which subsequently propagates into the system. All TestBed characterization tests were conducted in SimLabs at NASA Ames Research Center in November 2019. This study addresses the need for a baseline TestBed characterization, and the results will serve as a reference for more complex simulation systems.