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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 253 records · Page 14
All sourcesNASA NTRSDOE OSTI
NASA NTRS1982

Simulation for STS flight crew training

Training activities for flights of the Space Shuttle Orbiter are performed in the most complicated flight simulation facility yet assembled. The Shuttle Mission Simulator (SMS) provides training for the entire mission from prelaunch to postlanding. An extensive pilot-cues environment is provided in which visual, aural, and motion cues are presented. The planning and execution of training on the simulator must take into account the complexity of the Shuttle systems, the complexity of the procedures and crew interface on the Shuttle, and the complexity of the mission, itself. The major points of this training program are highlighted.

Hughes, F. E.↗
DOE OSTI2023

Iterative sampling of expensive simulations for faster deep surrogate training

Deep neural network (DNN) surrogates of expensive physics simulations are enabling a rapid change in the way that common experimental design and analysis tasks are approached. Surrogate models allow simulations to be performed in parallel and separately from downstream tasks, thereby enabling analyses that would be impossible with the simulation in-the-loop; surrogates based on DNNs can effectively emulate diverse non-scalar data of the types collected in fusion and laboratory-astrophysics experiments. The challenge is in training the surrogate model, for which large ensembles of physics simulations must be run, preferably without wasting computational effort on uninteresting simulations. Here, in this paper, we present an iterative sampling scheme that can preferentially propose simulations in interesting regions of parameter space without neglecting unexplored regions, allowing high-quality and wide-ranging surrogate models to be trained using 2–3 times fewer simulations compare to space-filling designs. Our approach uses an explicit importance function defined on the simulation output space, balanced against a measure of simulation density which serves as a proxy for surrogate accuracy. It is easy to implement and can be tuned to find interesting simulations early in the study, allowing surrogates to be trained quickly and refined as new simulations become available; this represents an important step towards the routine generation of deep surrogate models quickly enough to be truly relevant to experimental work.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
NASA NTRS2014

Transfer of Training on the Vertical Motion Simulator

This paper describes a transfer-of-training study in the NASA Ames Vertical Motion Simulator (VMS). Sixty-one general aviation pilots, divided in four groups, trained on four challenging commercial transport tasks with four motion conditions: no motion, small hexapod, large hexapod, and VMS motion. Then, every pilot repeated the tasks with VMS motion to determine if training with different motion conditions had an effect. New objective motion criteria guided the selection of the motion parameters for the small and large hexapod conditions. Considering results that were statistically significant, or marginally, the motion condition used in training affected 1) longitudinal and lateral touchdown location; 2) the number of secondary stall warnings in a stall recovery; 3) pilot ratings of motion utility and maximum load factor obtained in an overbanked upset recovery; and 4) pilot ratings of motion utility and pedal input reaction time in the engine-out-on-takeoff task. Since the motion condition revealed statistical differences on objective measures in all the tasks, even with some in the direction not predicted, trainers should be cautious not to oversimplify the effects of platform motion. Evidence suggests that the new objective motion criteria may offer valid standardization benefits, as instances arose when the higher-fidelity hexapod motion, as predicted by the criteria, provided better cues in training than the lower-fidelity hexapod motion.

Zaal, Peter M. T.↗
NASA NTRS1993

Evaluation of computer-aided instruction techniques for the crew interface coordination position

The Crew Interface Coordinator (CIC) is responsible for real-time voice and procedural communication between the payload crew on the orbiter and the payload operations team on the ground. This function is dedicated to science activities and operations, and may also include some responsibilities for crew training. CIC training at Marshall Space Flight Center (MSFC) consists of mission-independent training, mission simulations, and line-organization training. As identified by Schneider, the program provides very good generic training; however position-specific training may be obtained in a very unstructured way. A computer-based training system, identified as Mac CIC, is currently under development to address this issue. Mac CIC is intended to provide an intermediate level of training in order to prepare the CIC for the more intensive mission simulations. Although originally intended as an intelligent tutoring system, Mac CIC currently exists as a hypertext-based application. The objectives of this research is to evaluate the current system and to provide both recommendations and a detailed plan for Mac CIC's evolution into an intelligent tutoring system.

Moynihan, Gary P.↗
DOE OSTI2024

Hyperparameter Studies for Vision Transformers Trained on High-Fidelity Simulations

This library is a collection of python modules that define, train, and analyze vision-transformer (ViT) machine learning models. The code implements, with mild modifications, ViT models that have been made publicly available through publication and GitHub code. The training data for these models is hydrodynamic simulation output in the form of numpy arrays. This library contains code to train these ViT models on the hydrodynamic simulation output with a variety of hyperparameters, and to compare the results of such models. Furthermore, the library contains definitions of simple convolutional neural network (CNN) machine learning architectures which can be trained on the same hydrodynamic simulation output. These are included as a reference point to compare the ViT models to. Additionally, the library includes trained ViT and CNN models and example input data for demonstration purposes. The code is based on the PyTorch python library.

Callis, Skylar↗
NASA NTRS1989

Flight Simulator Platform Motion and Air Transport Pilot Training

The influence of flight simulator platform motion on pilot training and performance was examined In two studies utilizing a B-727-200 aircraft simulator. The simulator, located at Ames Research Center, Is certified by the FAA for upgrade and transition training in air carrier operations. Subjective ratings and objective performance of experienced B-727 pilots did not reveal any reliable effects of wide variations In platform motion de- sign. Motion platform variations did, however, affect the acquisition of control skill by pilots with no prior heavy aircraft flying experience. The effect was limited to pitch attitude control inputs during the early phase of landing training. Implications for the definition of platform motion requirements in air transport pilot training are discussed.

Lee, Alfred T.↗
NASA NTRS2008

Application of the NASA A-Train to Evaluate Clouds Simulated by the Weather Research and Forecast Model

The CloudSat Mission, part of the NASA A-Train, is providing the first global survey of cloud profiles and cloud physical properties, observing seasonal and geographical variations that are pertinent to evaluating the way clouds are parameterized in weather and climate forecast models. CloudSat measures the vertical structure of clouds and precipitation from space through the Cloud Profiling Radar (CPR), a 94 GHz nadir-looking radar measuring the power backscattered by clouds as a function of distance from the radar. One of the goals of the CloudSat mission is to evaluate the representation of clouds in forecast models, thereby contributing to improved predictions of weather, climate and the cloud-climate feedback problem. This paper highlights potential limitations in cloud microphysical schemes currently employed in the Weather Research and Forecast (WRF) modeling system. The horizontal and vertical structure of explicitly simulated cloud fields produced by the WRF model at 4-km resolution are being evaluated using CloudSat observations in concert with products derived from MODIS and AIRS. A radiative transfer model is used to produce simulated profiles of radar reflectivity given WRF input profiles of hydrometeor mixing ratios and ambient atmospheric conditions. The preliminary results presented in the paper will compare simulated and observed reflectivity fields corresponding to horizontal and vertical cloud structures associated with midlatitude cyclone events.

Molthan, Andrew L.↗
NASA NTRS2019

Verification of a Motion Cueing Strategy for Stall Recovery Training in a Commercial Transport Simulator

This paper verifies a motion cueing strategy for improved pilot stall recovery training in commercial transport simulators. Eight airline transport pilots flew a high-altitude stall recovery task in the NASA B747 level-D-certified full flight simulator under three different motion configurations: no motion, baseline motion, and enhanced motion. For each motion condition, pilots performed the task with both baseline aircraft dynamics and aircraft dynamics enhanced with lateral-directional characteristics of the airplane at angle of attack approaching stall. Motion configuration significantly affected: 1) pilot opinions on the helpfulness of motion in performing the task, 2) the maximum roll angle in the stall maneuver, 3) the minimum load factor in the recovery, 4) the number of secondary stick shakers in the stall recovery, and 5) the maximum airspeed in the recovery. The two different aircraft dynamics significantly affected: 1) pilot opinions on the noticeability of the banking roll off near the stall and 2) the maximum roll angle in the stall maneuver. These results indicate that the relatively minor enhancements to the motion logic of heritage commercial transport simulators presented here can significantly improve pilot performance in simulated stall recoveries, and potentially improve stall recovery training.

Pilot Performance↗
NASA NTRS2007

EVA Task Timing and Timeline Planning

EVA timeline development occurs using task execution data generated through underwater training and simulation. This project collected task time data during final training events for several Space Shuttle and International Space Station missions and compared like task time data collected during on-orbit execution. Analysis was performed to compare types of activities and times required for each looking specifically for how activities can be accurately trained from a timeline planning perspective. The data revealed two significant aspects of flight timeline planning; Zero-g task times will match training times for activities that can be accurately simulated with appropriate fidelity hardware; and not all activities can be simulated sufficiently to produce training task times that will reflect required zero-g times. An approach for timeline planning utilizing this knowledge is also presented.

Looper, Christopher A.↗
NASA NTRS2019

Verification of a Motion Cueing Strategy for Stall Recovery Training in a Commercial Transport Simulator

This presentation verifies a motion cueing strategy for improved pilot stall recovery training in commercial transport simulators. Eight airline transport pilots flew a high-altitude stall recovery task in the NASA B747 level-D-certified full flight simulator under three different motion configurations: no motion, baseline motion, and enhanced motion. For each motion condition, pilots performed the task with both baseline aircraft dynamics and aircraft dynamics enhanced with lateral-directional characteristics of the airplane at angle of attack approaching stall. Motion configuration significantly affected: 1) pilot opinions on the helpfulness of motion in performing the task, 2) the maximum roll angle in the stall maneuver, 3) the minimum load factor in the recovery, 4) the number of secondary stick shakers in the stall recovery, and 5) the maximum airspeed in the recovery. The two different aircraft dynamics significantly affected: 1) pilot opinions on the noticeability of the banking roll off near the stall and 2) the maximum roll angle in the stall maneuver. These results indicate that the relatively minor enhancements to the motion logic of heritage commercial transport simulators presented here can significantly improve pilot performance in simulated stall recoveries, and potentially improve stall recovery training.

Zaal, Petrus M. T.↗
DOE OSTI2025

A Gigaparsec-scale Hydrodynamic Volume Reconstructed with Deep Learning

The next generation of spectroscopic surveys will map the large-scale structure of the Universe at high redshifts (2 ≤ z ≤ 5) using millions of quasar spectra, enabling major advances in constraining both the standard cosmological model and its extensions. Robust cosmological analyses of these data sets require numerical simulations that both cover gigaparsec volumes and resolve features on ∼10 kpc scales and smaller. However, running such large-volume, high-resolution hydrodynamic simulations is computationally prohibitive. We present a generative deep learning model that enhances a low-resolution, gigaparsec-scale (960 h −1 Mpc) hydrodynamic simulation using a smaller (80 h −1 Mpc) high-resolution input hydrodynamic simulation as training data. The resulting enhanced simulation reproduces the line-of-sight power spectrum to within ∼10% and the three-dimensional power spectrum at the ∼20% level at intermediate to small scales (k ≲ 2 h Mpc −1 ). Our method shows strong promise for producing realistic simulations for cosmological analyses with current surveys such as the Dark Energy Spectroscopic Instrument and upcoming next-generation experiments, but further improvements are needed to accurately recover the large-scale modes. We publicly release the enhanced hydrodynamic simulation, along with a halo catalog from a companion N-body dark matter simulation to support the calibration of data analysis pipelines for these large-scale surveys.

Convolutional neural networks↗
NASA NTRS1997

Interactive Media and Simulation Tools for Technical Training

Over the last several years, integration of multiple media sources into a single information system has been rapidly developing. It has been found that when sound, graphics, text, animations, and simulations are skillfully integrated, the sum of the parts exceeds the individual parts for effective learning. In addition, simulations can be used to design and understand complex engineering processes. With the recent introduction of many high-level authoring, animation, modeling, and rendering programs for personal computers, significant multimedia programs can be developed by practicing engineers, scientists and even managers for both training and education. However, even with these new tools, a considerable amount of time is required to produce an interactive multimedia program. The development of both CD-ROM and Web-based programs are discussed in addition to the use of technically oriented animations. Also examined are various multimedia development tools and how they are used to develop effective engineering education courseware. Demonstrations of actual programs in engineering mechanics are shown.

Gramoll, Kurt↗
NASA NTRS2020

Flight Simulation Scenarios for Commercial Pilot Training and Crew State Monitoring

NASA Langley researchers addressed the Commercial Aviation Safety Team Safety Enhancement 211 through a series of studies to address "Attention-related Human Performance Limiting States" which include channelized attention, diverted attention, startle/surprise, and confirmation bias. The present report focuses on the development of improved training scenarios for operationally realistic Line-Oriented Flight Training scenarios. Areas addressed in the report include: (1) Highlights of events in the LOFT scenario used; (2) Interesting findings with implications for simulator motion; (3) Eye-tracking heat maps in proximity to failure events; (4) Researcher observations of crews as test subjects versus a pilot and a research team co-pilot; and (5) The results of a follow-up questionnaire completed by pilot participants regarding their usual training as well as the scenarios employed in the SHARP studies. These pilot ratings and comments are of value to simulation training developers.

James R. Comstock, Jr.↗
NASA NTRSDate not supplied

Modeling Shed Vorticity from Coaxial Blade Interactions

Coaxial counter-rotating rotors operate in a flowfield different from single rotors. Aerodynamic interactions such as blade crossing and shed vorticity result in potential sources of noise and impulsive blade loads. In previous research, the authors simulated two trains of airfoils traveling in opposite directions for specified speeds, airfoil thickness and vertical separation distances, using the compressible Navier-Stokes solver OVERFLOW. Previously, the effects of circulation, thickness, and compressibility were explored. This work continues the previous research by exploring downwash and shed vorticity effects. These phenomena are explored by simulating two trains of eight airfoils vertically separated traveling in opposite directions. The effects of downwash are simulated by introducing a vertical flow. Vorticity shed from the upper train of airfoils is shown to interact with the lower train, affecting the loading on the lower airfoils. Furthermore, viscid and inviscid calculations are performed to further understand the behavior of shed vorticity.

Natasha L Schatzman↗
DOE OSTI2025

Evolution at the Edge: Real-Time Evolution for Neuromorphic Engine Control

Neuromorphic computing systems are attractive for real-time control at the edge because of their low power operation, real-time processing capabilities and their potential ability to do online learning. In this work, we describe an approach for performing real-time evolution of spiking neural networks for neuromorphic systems at the edge called Neuromorphic Optimization using Dynamic Evolutionary Systems or NODES. We apply this approach to real-time combustion engine control and develop an engine-specific hardware platform for NODES called FireBox. We demonstrate how the real-time evolution approach works in simulation and the performance of networks trained in simulation on the physical engine.

Maldonado Puente, Bryan [ORNL] (ORCID:000000033880↗
NASA NTRS2001

Apollo 10 - 11

This video gives overviews of the Apollo 10 and Apollo 11 missions to the moon, including footage from the launches and landings of the Command Module Columbia, which is used for both flights. The Apollo 10 crewmembers, Commander Thomas Stafford, Command Module Pilot John Young, and Lunar Module Pilot Eugene Cernan, are seen as they suit-up in preparation for launch and then as they experiment with the microgravity environment on their way to the moon. The moon's surface is seen in detail as the Command Module orbits at an altitude of 69 miles. The Apollo 11 crewmembers, Commander Neil Armstrong, Command Module Pilot Michael Collins, and Lunar Module Pilot Buzz Aldrin, are seen during various training activities, including simulated lunar gravity training, practicing collecting lunar material, and using the moonquake detector. Footage shows the approach and landing of the Lunar Module Eagle on the moon. Armstrong and Aldrin descend to the moon's surface, collect a sample of lunar dust, and erect the American flag. Eagle's liftoff from the moon is seen.

Source record↗
NASA NTRS2002

STS-109 Crew Training

Footage shows the crew of STS-109 (Commander Scott Altman, Pilot Duane Carey, Payload Commander John Grunsfeld, and Mission Specialists Nancy Currie, James Newman, Richard Linnehan, and Michael Massimino) during various parts of their training. Scenes show the crew's photo session, Post Landing Egress practice, training in Dome Simulator, Extravehicular Activity Training in the Neutral Buoyancy Laboratory (NBL), and using the Virtual Reality Laboratory Robotic Arm. The crew is also seen tasting food as they choose their menus for on-orbit meals.

Source record↗