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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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NASA NTRS2014

Simulation Technology at NASA

NASA Ames Research Center is home to several high-fidelity research flight and air-traffic control simulation facilities which, together with an experienced workforce, produce high-quality research data and findings that have proven to be applicable in the real world. These assets include the Vertical Motion Simulator (VMS), Crew Vehicle Systems Research Facility (CVSRF), Future Flight Central (FFC) air traffic control tower simulator, and several air-traffic control (ATC) simulators. The VMS combines a high-fidelity simulation capability with an adaptable simulation environment, enabling customization for numerous human-in-the-loop research applications. The distinctive feature of the VMS is its unparalleled large amplitude, high-fidelity motion capability. In over 30 years of continuous operation, the VMS has contributed significantly to the body of knowledge in a range of disciplines directly benefiting several aerospace programs and flight safety, including the design and development of flight control systems for the Joint Strike Fighter, Space Shuttle Orbiter, and rotorcraft. It continues to be used for researching new vehicle configurations, vehicle control and safety, transfer-of-training, etc., by NASA, other government agencies, and Industry.The CVSRF includes two motion-based flight simulators: a Boeing 747-400 full flight simulator and the reconfigurable Advanced Concepts Flight Simulator (ACFS). These simulators are primarily used to research air-traffic management concepts and procedures, advanced navigation and avionics concepts, and cockpit human factors. FFC is a full-sized control tower simulator with a 360-degree external field-of-view display system and reconfigurable system architecture. FFC and the ATC simulators are used for testing air-traffic management automation and decision support tools and demonstrate their feasibility in a realistic environment prior to technology transfer for implementation in the National Airspace System (NAS).To support integrated simulations and flight-tests for NASA's Unmanned Aircraft Systems (UAS) in the National Airspace System (NAS) Project, NASA developed a distributed test environment incorporating Live, Virtual, Constructive, (LVC) concepts. Development of the software enabling the LVC is conducted primarily at the Distributed Simulation Research Lab (DSRL) at NASA Ames. The LVC components provide the core infrastructure supporting simulation of UAS operations by integrating live and virtual aircraft in a realistic air traffic environment. This provides the ability to conduct tests more efficiently by promoting the use of existing distributed assets. The LVC infrastructure was used in several human-in-the-loop simulations to evaluate acceptance of Detect and Avoid (DAA) advisories used by UAS pilots to maintain well clear of other virtual traffic and to negotiate maneuvers with air traffic control. It is currently being used to support testing of self-separation algorithms between unmanned and manned aircraft in live flight. Further simulations with more comprehensive air traffic scenarios mixing live and virtual aircraft is planned.In the current fiscal environment, maintaining and upgrading these high-fidelity simulation assets and retaining the skilled workforce necessary to meet future research needs is the primary non-technical challenge.

CVSRF↗
NASA NTRS2020

Comparing Virtual Reality to Conventional Simulator Visuals: Effects of Peripheral Visual Cues in Roll-Axis Tracking Tasks

This paper compares the effects of peripheral visual cues on manual control between a conventional fixed-base simulator and virtual reality. The results were also compared with those from a previous experiment conducted in a motion-base simulator. Fifteen participants controlled a system with second-order dynamics in a disturbance-rejection task. Tracking performance, control activity, simulator sickness questionnaire answers, and biometrics were collected. Manual control behavior was modeled for the first time in a virtual reality environment. Virtual reality did not degrade participants’ manual control performance or alter their control behavior. However, peripheral cues were significantly more effective in virtual reality. Control activity decreased for all conditions with peripheral cues. The trends introduced by the peripheral visual cues from the previous experiment were replicated. Finally, VR was not more nauseogenic than the conventional simulator. These results suggest that virtual reality might be a good alternative to conventional fixed-base simulators for training manual control skills.

Terenzi, Lorenzo↗
NASA NTRS2019

Investigation of Reduced-Order Modeling for Aircraft Stability and Control Prediction

High-fidelity computational fluid dynamics tools offer the potential to approximate increments for ground-to-flight scaling effects, as well as to augment the dynamic damping derivative data for motion-based flight simulators. Unfortunately, the computational expense is currently prohibitive for populating a complete simulator database. This work investigates an existing surrogate-based, indicial response reduced-order model methodology as a means to efficiently augment a flight simulator database with high-fidelity nonlinear aerodynamic damping derivatives. Creation of the reduced-order model is based on the superposition integrals of the step response with the derivative of its corresponding input signal. Step responses are calculated using a computational grid motion approach that separates the effects of angle of attack and sideslip angle from angular rates, and rates from angle of attack and sideslip. It is demonstrated that the transients produced during the start of a forced-oscillation motion are captured by the reduced-order model to the level of fidelity of a comparable computational solution. Aerodynamic coefficients computed within minutes by the reduced-order model for an aircraft undergoing an 18-second half Lazy-8 maneuver and a 25-second Immelmann turn maneuver are compared with those from full computational flight solutions that required days to complete. Finally, a cost-benefit assessment is included that demonstrates a compelling advantage for this approach. d for maneuvering, flexible vehicles.

Frink, Neal T.↗
NASA NTRSDate not supplied

Linear Mixed-Effects Models for Human-in-the-Loop Tracking Experiment Data

Linear mixed-effects models provide several benefits over more traditional statistical inference tests that are particularly useful for most human-in-the-loop tracking experiment data. However, surprisingly, mixed models are virtually not used for the analysis of tracking experiment data. This paper uses of linear mixed-effects models to analyze combined tracking data from two previous human-in-the-loop roll tracking experiments that compared control behavior metrics collected in both a research aircraft and a motion-base simulator. In the experiments, pilots' behavior under 10 different motion configurations with varying motion filter gains and break frequencies was evaluated and compared to that in the real aircraft. The linear mixed-effects model analysis on the combined dataset confirmed the main statistical outcomes of the individual experiments. The main benefits of mixed models for this type of data were demonstrated by successfully combining data from two experiments that used different experimental conditions and of which one had an additional apparatus and the other a missing participant. Finally, the mixed-model analysis was able to explicitly test for scientifically relevant statistical differences in the dependent measures between the aircraft and simulator, as well as both experiments.

manual control↗
NASA NTRSDate not supplied

Hazard Perception & Avoidance (HPA), Assured Vehicle Automation 1 Simulation (AVA-1h) Results Outbrief

In late 2022, the Human Autonomy Teaming Lab (NASA Ames Research Center) conducted a manned, human-in-the-loop (HITL) simulation. This HITL was the lab’s second Hazard Perception and Avoidance (HPA) technical work under NASA’s Advanced Air Mobility (AAM), Automated Flight and Contingency Management (AFCM) Sub-Project. The goals were to assess detect and avoid technology for manned, electric vertical takeoff and landing (eVTOL) aircraft. This simulation tested Resolution Advisory (RA) responses and return-to-course (RTC) maneuvers for the second version of the Airborne Collision Avoidance System’s (ACAS) rotary-wing (Xr) variant. This was conducted at the center's Vertical Motion Simulator (VMS), on a motion-based platform, and was configured to fly eVTOL aircraft while maneuvering for intruding traffic. Variables for this study included ACAS Xr modes (i.e., TA/RA and DAA) and phases of flight (i.e., Cruise, Hover, and Approach). The data collected included response times, losses of well clear, and pilots' noncompliances to alerts and guidance as well as subjective ratings like acceptability and perceived workload. Additional details and future anticipations are also discussed.

ACAS Xr↗
NASA NTRSDate not supplied

Manual Crew Override of Vehicle Landings Following G-Transitions

BACKGROUND Manual control during exploration spaceflight consists of both planned automated supervisory control and unplanned crew override. This crew override capability is critical to enable overall mission success during landing contingencies. However, the introduction of manual override capabilities must be implemented to enable crews to mitigate risks introduced by human error. Adaptive changes in the sensorimotor system can manifest during g-transitions as spatial disorientation. While training and landing aids enable successful landing through disorientation, these adaptive changes may increase cognitive demand that needs to be accounted for in the manual control strategy. It is important to characterize these effects as soon as possible following the G-transition to develop appropriate countermeasures. METHODS In this study, we will examine operational piloting tasks following International Space Station (ISS) missions in a simulated lunar landing using a six-degree-of-freedom (6DOF) motion base. The motion-based simulation will be implemented in our laboratory at the Johnson Space Center (JSC) and will be available within hours following the return from commercial crew landings. The primary goals of this study are (1) to understand the impact of spaceflight on crew ability to perform manual crew override tasks, (2) to examine how adaptive changes in vestibular and cognitive function relate to changes in manual crew override proficiency, and (3) compare performance during late “just-in-time” on-board training with early post-flight crew performance. The impact of spaceflight on piloting capability will be assessed from pre- versus post-flight changes in crewmembers assigned to either short duration (< 30 day) or long duration (~6- month) missions to the International Space Station (ISS). Individual differences in post-flight vestibular and cognitive changes include motion sickness reports, measures of tilt motion perception accuracy and precision, and dual-task tracking. During the 6DOF lunar simulation, the crew will manually takeover attitude and rate-of-descent to the nominal or re-designated landing aim point during the approach phase. The outcome measures for the lunar crew override tasks will be the percent time maintaining actual vehicle states, e.g., attitude and rate-of-descent, within recommended guidance during the landing approach, number and maximum deviation outside limits, and root mean square error (RMSE). Given that “just-in-time” (JIT) training is an operational expectation for the Human Landing System (HLS) program, all participants will perform late inflight JIT training for each manual crew override task in which they will participate. Crew proficiency will be captured inflight during JIT training that will be implemented on a laptop with hand controllers to allow the crewmember to practice the landing task procedures like the approach implemented for JIT training with Shuttle landing and ISS telerobotic tasks. COUNTERMEASURE UPDATES The lunar lander simulation has been implemented onto a two-laptop display system where subjects select from alternative landing points, based upon avoiding hazards that the on-board system identifies during the landing. The subject makes inputs using a rotational hand controller (i.e., joystick) and a translational hand controller, which are processed by simulated vehicle dynamics to update the vehicle attitude and rate of descent. Subjects will utilize a combination of flight, situation, and status displays to monitor the state of the simulated vehicle. The simulation exports tilt and translation movements to a 6DOF motion base synced to visual movements and provides representative vestibular cues to the subject while performing the task inside an enclosed cabin featuring a simulated lunar out-the-window view and a heads-down lander cockpit view. RELEVANCE This project will deliver an operational demonstration of crew override capability following spaceflight and identify potential deficits that may require remediation. Comparison of individual vestibular and cognitive changes with crew override performance will help better characterize the manual control risks associated with sensorimotor alterations. The inclusion of “just-in-time” on-board training will ensure we are characterizing changes in override proficiency with this expected countermeasure in place. ACKNOWLEDGEMENTS: The authors acknowledge contributions from Draper, HLS Joint Test Panel, Crew HLS Interfaces for Piloting Working Group, and Dynamic Skills Trainer (DST) Lab toward the development of the lunar landing simulation. This project is funded by the Human Health Countermeasures Element.

Austin M Bollinger↗