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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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At least 19 records
All sourcesNASA NTRSDOE OSTI
NASA NTRS1993

A high fidelity video delivery system for real-time flight simulation research

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.

Wilkins, Daniel A.↗
NASA NTRS1981

The role of the research simulator in the systems development of rotorcraft

The potential application of the research simulator to future rotorcraft systems design, development, product improvement evaluations, and safety analysis is examined. Current simulation capabilities for fixed-wing aircraft are reviewed and the requirements of a rotorcraft simulator are defined. The visual system components, vertical motion simulator, cab, and computation system for a research simulator under development are described.

Statler, I. C.↗
NASA NTRS1981

The role of the research simulator in the systems development of rotorcraft

Over the last 20 years, flight simulators became widely accepted as training tools. Moreover, research simulators were used extensively by the fixed-wing industry: in the design, testing, and certification of new aircraft. The rotorcraft industry, however, was slow to use man-in-the-loop simulation to solve its design problems, primarily because of the difficulty of modeling complex rotorcraft for realtime simulation and because of the need for a wide-angle visual system for low-level flight. A joint U.S. Army and NASA program was initiated to provide this simulation capability for exploitation by both government and industry. The potential application of the research simulator to future rotorcraft systems design, development, product improvement evaluations, and safety analysis is discussed.

Statler, I. C.↗
NASA NTRS2011

2011 Highlights: Advanced Simulation Research at NASA Ames

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.

simulation research↗
NASA NTRS2016

From Rural to Urban Environments: Human/Systems Simulation Research for Low Altitude UAS Traffic Management (UTM)

This paper describes the human/systems simulation research within NASA's UAS (Unmanned Aircraft System) Traffic Management (UTM) project. The paper starts with a short description of the UTM project, then presents the UTM development schedule briefly, before leading into more detailed discussions of the simulation test beds current capabilities, as well as the ongoing and planned human/systems research activities.

unmanned aircraft systems↗
NASA NTRS1993

Flight research simulation takes off

The simulation configurations used in research flight test, including nonreal-time, real-time all-software, hardware-in-the-loop, iron bird, and aircraft-in-the-loop, are reviewed. It is concluded that progress in simulation technology will demonstrate new concepts, evaluate designs, enhance the quality of flight test, and minimize flight risks.

Schilling, Lawrence J.↗
NASA NTRSDate not supplied

Evaluating the Use of High-Fidelity Simulator Research Methods to Study Airline Flight Crew Resilience

As it evolves, aviation will continue to require integration of a wide range of safety systems and practices, some of which are already in place and others that are yet to be developed. New concepts in system safety thinking have emerged to consider not only what may go wrong, but also what can be learned when things go right during commercial flight operations. Taken together, these complementary perspectives form a more comprehensive approach to systemsafety thinking that can help to recognize and preserve the resilient performance capabilities currently provided by humans. A need exists, however, for research methods to enable better understanding of the human contributions to aviation safety. NASA’s System-Wide Safety Project supports research on using flight simulation methods to study operator resilience and safety-producing behaviors. Building on prior NASA efforts investigating procedural non-adherences during area navigation standard terminal route arrivals, a high-fidelity commercial aviation line operational simulation (LOS) experiment has been designed to study how flight crews anticipate, monitor for, respond to, and learn from expected and unexpected disturbances during these operations. A diverse set of LOS scenarios were developed to simulate highly realistic, complex, but routinely encountered operational situations. Each scenario provided multiple opportunities to collect data on how flight crews manage threats and errors, as well as novel opportunities to observe resilient and safety-producing behaviors. The experimental design, implications for the study of safety-producing behaviors using simulation, and considerations for airline pilot training will be discussed.

Chad L Stephens↗
NASA NTRS1965

Solar simulation research

Improved measurement techniques for solar simulators, support equipment, and solar cells - pressure arc chamber and vortex stabilized radiation source - radiometry

ARC CHAMBER↗
NASA NTRS2009

NASA's Large-Eddy Simulation Research for Jet Noise Applications

Research into large-eddy simulation (LES) for application to jet noise is described. The LES efforts include in-house code development and application at NASA Glenn along with NASA Research Announcement sponsored work at Stanford University and Florida State University. Details of the computational methods used and sample results for jet flows are provided.

DeBonis, James R.↗
NASA NTRS2004

Proposed Development of NASA Glenn Research Center's Aeronautical Network Research Simulator

Accurate knowledge and understanding of data link traffic loads that will have an impact on the underlying communications infrastructure within the National Airspace System (NAS) is of paramount importance for planning, development and fielding of future airborne and ground-based communications systems. Attempting to better understand this impact, NASA Glenn Research Center (GRC), through its contractor Computer Networks & Software, Inc. (CNS, Inc.), has developed an emulation and test facility known as the Virtual Aircraft and Controller (VAC) to study data link interactions and the capacity of the NAS to support Controller Pilot Data Link Communications (CPDLC) traffic. The drawback of the current VAC test bed is that it does not allow the test personnel and researchers to present a real world RF environment to a complex airborne or ground system. Fortunately, the United States Air Force and Navy Avionics Test Commands, through its contractor ViaSat, Inc., have developed the Joint Communications Simulator (JCS) to provide communications band test and simulation capability for the RF spectrum through 18 GHz including Communications, Navigation, and Identification and Surveillance functions. In this paper, we are proposing the development of a new and robust test bed that will leverage on the existing NASA GRC's VAC and the Air Force and Navy Commands JCS systems capabilities and functionalities. The proposed NASA Glenn Research Center's Aeronautical Networks Research Simulator (ANRS) will combine current Air Traffic Control applications and physical RF stimulation into an integrated system capable of emulating data transmission behaviors including propagation delay, physical protocol delay, transmission failure and channel interference. The ANRS will provide a simulation/stimulation tool and test bed environment that allow the researcher to predict the performance of various aeronautical network protocol standards and their associated waveforms under varying density conditions. The system allows the user to define human-interactive and scripted aircraft and controller models of various standards, such as (but not limited to) Very High Frequency Digital Link (VDL) of various modes.

Nguyen, Thanh C.↗
DOE OSTI2025

FAIR Surrogate Benchmarks Supporting AI and Simulation Research (Final Report)

Computational Science is being revolutionized by integrating AI and simulation and, in particular, by deep learning surrogate models that can replace all or part of traditional large‐scale HPC computations. Such surrogates can achieve remarkable performance improvements, as much as several orders of magnitude, and save both compute time and energy. The Surrogate Benchmark Initiative (SBI) project creates a community repository and FAIR (Findable, Accessible, Interoperable, and Reusable) data ecosystem for HPC application surrogate benchmarks. The SBI team comes from Argonne National Laboratory (ANL), Indiana University (IU), Rutgers University, the University of Tennessee, Knoxville (UTK), and the University of Virginia (UVA). SBI repositories include data, code, and all relevant collateral artifacts that the science and engineering community need to use and reuse these data sets and surrogates. SBI repositories generate active research from both the participants in SBI and the broad community of AI and domain scientists. This project develops surrogates that use several different neural nets to learn and quickly infer the results of simulations and data systems and captures them as surrogate benchmarks with a rich set of metadata covering: Data; Model; Metrics specification; Machine specification; and Science, Speed, and Power Results. We research FAIR metadata for these benchmarks. We develop application surrogate examples as benchmarks across many fields (ANL, UTK, IU, UVA). We also study non-Surrogate benchmarks that have many common features and similar issues as regards FAIRness. We work with MLCommons (UVA, UTK), which is a major machine learning benchmarking activity where we get metadata ontologies, software, and benchmarks, Benchmarks have datasets, models, and metadata and they need a technical framework developed by UTK and Rutgers and deployed by UVA. We study features of Surrogates including performance, training set size, and uncertainty quantification (Rutgers, UVA and IU).

97 MATHEMATICS AND COMPUTING↗
NASA NTRS1991

Rotorcraft In-Flight Simulation Research at NASA Ames Research Center: A Review of the 1980's and plans for the 1990's

A new flight research vehicle, the Rotorcraft-Aircrew System Concepts Airborne Laboratory (RASCAL), is being developed by the U.S. Army and NASA at ARC. The requirements for this new facility stem from a perception of rotorcraft system technology requirements for the next decade together with operational experience with the Boeing Vertol CH-47B research helicopter that was operated as an in-flight simulator at ARC during the past 10 years. Accordingly, both the principal design features of the CH-47B variable-stability system and the flight-control and cockpit-display programs that were conducted using this aircraft at ARC are reviewed. Another U.S Army helicopter, a Sikorsky UH-60A Black Hawk, was selected as the baseline vehicle for the RASCAL. The research programs that influence the design of the RASCAL are summarized, and the resultant requirements for the RASCAL research system are described. These research programs include investigations of advanced, integrated control concepts for achieving high levels of agility and maneuverability, and guidance technologies, employing computer/sensor-aiding, designed to assist the pilot during low-altitude flight in conditions of limited visibility. The approach to the development of the new facility is presented and selected plans for the preliminary design of the RASCAL are described.

Aiken, Edwin W.↗
DOE OSTI2025

DOE FAIR Surrogate Benchmarks Supporting AI and Simulation Research (SBI Surrogate Benchmark Initiative) (Final Report)

Computational Science is being revolutionized by integrating AI and simulation and, in particular, by deep learning surrogate models that can replace all or part of traditional large‐scale HPC computations. Such surrogates can achieve remarkable performance improvements, as much as several orders of magnitude, and save both compute time and energy. The Surrogate Benchmark Initiative (SBI) project creates a community repository and FAIR (Findable, Accessible, Interoperable, and Reusable) data ecosystem for HPC application surrogate benchmarks. The SBI team comes from Argonne National Laboratory (ANL), Indiana University (IU), Rutgers University, the University of Tennessee, Knoxville (UTK), and the University of Virginia(UVA). SBI repositories include data, code, and all relevant collateral artifacts, that the science and engineering community needs to use and reuse these data sets and surrogates. SBI repositories generate active research from both participants in SBI and the broader AI and domain science communities. This project develops surrogates that use several different neural nets to learn and quickly infer the results of simulations and data systems and capture them as surrogate benchmarks with a rich set of metadata, covering. Data; Model; Metrics specification; Machine specification; Science, Speed, Power Results, We research FAIR metadata for these benchmarks. We develop application surrogate examples as benchmarks across many fields (ANL, UTK, IU, UVA). We also study non Surrogate benchmarks that have many common features and similar issues regarding FAIRness. We work with MLCommons (UVA, UTK), which is a major machine learning benchmarking activity where we get metadata ontologies, software, and benchmarks, benchmarks have datasets, models, and metadata, and they need a technical framework developed by UTK and Rutgers and deployed by UVA. We study features of Surrogates, including performance, training set size, and uncertainty quantification (Rutgers, UVA and IU).

97 MATHEMATICS AND COMPUTING↗