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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 109 records · Page 6

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NASA NTRS2019

Motion Cueing for Stall Recovery Training in Commercial Transport Simulators

Starting in 2019, airline pilots will be required to perform full stall recovery training in flight simulators. Historically, training simulators weren't required to provide training at conditions outside their normal flight envelope. Post-stall aircraft models are generally required to be implemented to simulate the aircraft response after the stall point. In addition, motion cues need to adequately represent this response to ensure the skills learned in simulator training are directly usable in real flight. This paper provides and overview of six simulator experiments conducted at NASA Ames Research Center to develop a motion cueing strategies for stall recovery training in commercial transport simulators. One of the experiments verified an enhanced motion cueing strategy for stall recovery training on a level-D-certified full flight simulator. This study showed that the enhanced motion results in lower maximum roll angles in the stall maneuver, lower minimum load factors in the recovery, lower numbers of secondary stick shakers in the stall recovery, and a lower maximum airspeed in the recovery. These results indicate that relatively minor enhancements to the motion logic of heritage commercial transport simulators can significantly improve pilot performance in simulated stall recoveries, and potentially improve stall recovery training.

stall recovery↗

DOE OSTI2023

QUOTAS: A New Research Platform for the Data-driven Discovery of Black Holes

We present QUOTAS, a novel research platform for the data-driven investigation of supermassive black hole (SMBH) populations. While SMBH data—observations and simulations—have grown in complexity and abundance, our computational environments and tools have not matured commensurately to exhaust opportunities for discovery. To explore the BH, host galaxy, and parent dark matter halo connection—in this pilot version—we assemble and colocate the high-redshift, z > 3 quasar population alongside simulated data at the same cosmic epochs. As a first demonstration of the utility of QUOTAS, we investigate correlations between observed Sloan Digital Sky Survey (SDSS) quasars and their hosts with those derived from simulations. Leveraging machine-learning algorithms (ML), to expand simulation volumes, we show that halo properties extracted from smaller dark-matter-only simulation boxes successfully replicate halo populations in larger boxes. Next, using the Illustris-TNG300 simulation that includes baryonic physics as the training set, we populate the larger LEGACY Expanse dark-matter-only box with quasars, and show that observed SDSS quasar occupation statistics are accurately replicated. First science results from QUOTAS comparing colocated observational and ML-trained simulated data at z3 are presented. QUOTAS demonstrates the power of ML, in analyzing and exploring large data sets, while also offering a unique opportunity to interrogate theoretical assumptions that underpin accretion and feedback models. QUOTAS and all related materials are publicly available at the Google Kaggle platform. (The full data set—observational data and simulation data—are available at: https://www.kaggle.com/ and the codes are available at:https://www.kaggle.com/datasets/quotasplatform/quotas)

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

NASA NTRS1977

Summary report of Committee B

Topics discussed in this summary include: (1) general aviation and services; (2) aircraft design; and (3) simulation. It was concluded that private pilots need to be more knowledgeable about weather. Improvement is needed in providing general aviation pilots with changes in the weather reporting and forecasting systems. There should also be some simulation of various severe shear profiles in training simulators, although there is still a problem in simulating shear conditions using mathematical models and data.

Lenschow, D. H.↗

NASA NTRS1981

Issues related to line-oriented flight training

The use of a training simulator along with carefully structured, detailed, line trip scenarios was envisioned by NASA as a means of providing a controllable, repeatable way to observe line crews in a highly realistic simulation of their working environment and obtain better understanding operationally significant human factors problems and issues. Relevant research done by the agency and the results of full-mission simulation scenarios revealed potential implications for flight training. Aspects to be considered in creating training programs closely related to the actual line environment with a total crew application in real world incident experiences include: (1) operational, environmental, equipment, and crew problems in scenario design; (2) real time line oriented flight training operation; (3) performance assessment and debriefing; (4) instructor qualification and training; and (5) other issues such as ub un initial, transition, and upgrade training; procedures developent and evaluation, and equipment evaluation.

Lauber, J. K.↗

NASA NTRS2006

Utilizing a Common Model Architecture for the Simulation of On-Orbit Human Spaceflight Operations

Advances in computing and simulation technology during the 1980 s promoted the development of multiple simulations for on-orbit human spaceflight operations at the National Aeronautics and Space Administration (NASA) Johnson Space Center (JSC). By the late 1980 s, it became increasingly clear that redundancy risked leading to inefficiencies in dissemination and incorporation of models across simulations, institutional organizations, and spaceflight programs. Software development tools, such as the Trick Simulation Environment, began to emerge providing a generalized framework for the development, integration, and operation of simulations. Throughout the 1990s, engineering, operations, and training simulations began to migrate to Trick in order to take advantage of a common simulation environment. Due to forecasted reductions in funding to the Shuttle and International Space Station (ISS) programs in the early 2000s, the common framework that arose from this simulation migration was formalized as the Common Model Architecture (CMA). The CMA was established in order to further reduce redundancy, while promoting model sharing and enhancing model integrity. A minimal set of standards for data flows, functional interactions, and model organization based on Trick were defined for those features found to be most important in facilitating model interaction. Model-providing organizations within NASA JSC took ownership of models for their specific areas of expertise, and agreements were reached on common model sharing and usage. This paper describes the evolution of commonality enabled by the Trick Simulation Environment at JSC leading to the CMA, the standards defined by the CMA, a recent CMA simulation example, and potential application of the CMA to NASA s new Exploration program.

Leslie J Quiocho↗

DOE OSTI2022

Personalized learning via task load optimization

A method for providing task load-optimized computer-generated training experiences to a user of a training system that includes: a display, a training simulator, a prediction program (ML1), and a training optimization program (ML2). In response to receiving a predicted optimal task load, ML2 provides a first training experience recommendation related to the training content and/or training conditions that, if utilized in providing a training experience to the user, is predicted to result in the predicted actual task load of the user equaling the predicted optimal task load. In response to receiving biometric information or performance metric information, ML1 determines the predicted actual task load. If the predicted actual task load does not match the predicted optimal task load, ML2 provides a second training experience recommendation and a second training experience is provided where at least one of the training content or the training conditions is changed.

Bertolli, Michael G.↗

NASA NTRS2003

Development and Utility of a Piloted Flight Simulator for Icing Effects Training

A piloted flight simulator called the Ice Contamination Effects Flight Training Device (ICEFTD), which uses low cost desktop components and a generic cockpit replication is being developed. The purpose of this device is to demonstrate the effectiveness of its use for training pilots to recognize and recover from aircraft handling anomalies that result from airframe ice formations. High-fidelity flight simulation models for various baseline (non-iced) and iced configurations were developed from wind tunnel tests of a subscale DeHavilland DHC-6 Twin Otter aircraft model. These simulation models were validated with flight test data from the NASA Twin Otter Icing Research Aircraft, which included the effects of ice on wing and tail stall characteristics. These simulation models are being implemented into an ICEFTD that will provide representative aircraft characteristics due to airframe icing. Scenario-based exercises are being constructed to give an operational-flavor to the simulation. Training pilots will learn to recognize iced aircraft characteristics from the baseline, and will practice and apply appropriate recovery procedures to a handling event.

Ratvasky, Thomas P.↗

NASA NTRS1990

Space Station Simulation Computer System (SCS) study for NASA/MSFC. Concept document

Source record↗

DOE OSTI2022

Suppressing simulation bias in multi-modal data using transfer learning

Abstract Many problems in science and engineering require making predictions based on few observations. To build a robust predictive model, these sparse data may need to be augmented with simulated data, especially when the design space is multi-dimensional. Simulations, however, often suffer from an inherent bias. Estimation of this bias may be poorly constrained not only because of data sparsity, but also because traditional predictive models fit only one type of observed outputs, such as scalars or images, instead of all available output data modalities, which might have been acquired and simulated at great cost. To break this limitation and open up the path for multi-modal calibration, we propose to combine a novel, transfer learning technique for suppressing the bias with recent developments in deep learning, which allow building predictive models with multi-modal outputs. First, we train an initial neural network model on simulated data to learn important correlations between different output modalities and between simulation inputs and outputs. Then, the model is partially retrained, or transfer learned, to fit the experiments; a method that has never been implemented in this type of architecture. Using fewer than 10 inertial confinement fusion experiments for training, transfer learning systematically improves the simulation predictions while a simple output calibration, which we design as a baseline, makes the predictions worse. We also offer extensive cross-validation with real and carefully designed synthetic data. The method described in this paper can be applied to a wide range of problems that require transferring knowledge from simulations to the domain of experiments.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

NASA NTRS1987

Simulator systems integration

The implementation of available wind shear data into general aviation flight training simulators is discussed. Currently, there are 11 simulators with wind shear models installed involving some 9 different aircraft models. Retrofits to other systems that were put out earlier are currently underway, and all the new simulators will have wind shear available for the instructors to use for demonstrations and training. There are three types of computer systems involved, and two different types of instructor stations. Most of the systems with wind shear are the CRT-type displays. The integration of wind shear models in flight simulators is discussed in detail.

Shipman, Keith W.↗

DOE OSTI2022

Enabling Predictive Scale-Bridging Simulations through Active Learning (Institutional Computing Annual Report (Project w20_alscalebridging)) [Slides]

The goal of this project was to develop, demonstrate, and provide a new capability to achieve greater physical fidelity in large-scale simulations, rather than the usual brute-force increases in the number of mesh elements or particles. This was done by using machine learning (ML) techniques to develop emulators for subscale physics that can be used in coarse-scale continuum simulations, trained on fine-scale molecular dynamics (MD) simulations that are launched on-the-fly via active learning. raditional inertial confinement fusion (ICF) simulations rely on numerical diffusion to simulate molecular effects such as non-local transport and mixing without truly accounting for molecular interactions; our approach directly accounts for this physics.

74 ATOMIC AND MOLECULAR PHYSICS↗

NASA NTRS1989

Evaluating the effectiveness of cockpit resource management training

The concept of providing flight crews with intensive training in crew coordination and interpersonal skills (cockpit resource management training - CRM) is outlined with emphasis on full mission simulator training (line-oriented flight training - LOFT). Findings from several airlines that have instituted CRM and LOFT are summarized. Four types of criteria used for evaluating CRM programs: observer ratings of crew behavior, measures of attitudes regarding cockpit management, self-reports by participants on the value of the training, and case studies of CRM-related incidents and accidents are covered. Attention is focused on ratings of the performance of crews during line flights and during simulator sessions conducted as a part of LOFT. A boomerang effect - the emergence of a subgroup that has changed the attitudes in the opposite direction from that desired is emphasized.

Helmreich, Robert L.↗

DOE OSTI2021

Deep learning simulations of the microwave sky

Here we present 500 high-resolution, full-sky millimeter-wave deep learning (DL) simulations that include lensed CMB maps and correlated foreground components. We find that these MillimeterDL simulations can reproduce a wide range of non-Gaussian summary statistics matching the input training simulations, while only being optimized to match the power spectra. The procedure we develop in this work enables the capability to mass produce independent full-sky realizations from a single expensive full-sky simulation, when ordinarily the latter would not provide enough training data. We also circumvent a common limitation of high-resolution DL simulations that they be confined to small sky areas, often due to memory or GPU issues; we do this by developing a “stitching” procedure that can recover the large-scale, high-order statistics and avoid discontinuities or repeated features in the maps. In addition, since our network takes as input a full-sky lensing convergence map, it can in principle take a full-sky lensing convergence map from any large-scale structure (LSS) simulation and generate the corresponding lensed CMB and correlated foreground components at millimeter wavelengths; this is especially useful in the current era of combining results from both CMB and LSS surveys, which require a common set of simulations.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

NASA NTRS1990

Space Station Simulation Computer System (SCS) study for NASA/MSFC. Operations concept report

NASA's Space Station Freedom Program (SSFP) planning efforts have identified a need for a payload training simulator system to serve as both a training facility and as a demonstrator to validate operational concepts. The envisioned MSFC Payload Training Complex (PTC) required to meet this need will train the Space Station payload scientists, station scientists, and ground controllers to operate the wide variety of experiments that will be onboard the Space Station Freedom. The Simulation Computer System (SCS) is made up of computer hardware, software, and workstations that will support the Payload Training Complex at MSFC. The purpose of this SCS Study is to investigate issues related to the SCS, alternative requirements, simulator approaches, and state-of-the-art technologies to develop candidate concepts and designs.

Source record↗

NASA NTRS1990

Space Station Simulation Computer System (SCS) study for NASA/MSFC. Phased development plan

NASA's Space Station Freedom Program (SSFP) planning efforts have identified a need for a payload training simulator system to serve as both a training facility and as a demonstrator to validate operational concepts. The envisioned MSFC Payload Training Complex (PTC) required to meet this need will train the Space Station payload scientists, station scientists and ground controllers to operate the wide variety of experiments that will be onboard the Space Station Freedom. The Simulation Computer System (SCS) is made up of computer hardware, software, and workstations that will support the Payload Training Complex at MSFC. The purpose of this SCS Study is to investigate issues related to the SCS, alternative requirements, simulator approaches, and state-of-the-art technologies to develop candidate concepts and designs.

Source record↗

NASA NTRS1980

Synthesized voice approach callouts for air transport operations

A flight simulation experiment was performed to determine the effectiveness of synthesized voice approach callouts for air transport operations. Flight deck data was first collected on scheduled air carrier operations to describe existing pilot-not-flying callout procedures in the flight context and to document the types and amounts of other auditory cockpit information during different types of air carrier operations. A flight simulation scenario for a wide-body jet transport airline training simulator was developed in collaboration with a major U.S. air carrier and flown by three-man crews of qualified line pilots as part of their normally scheduled recurrent training. Each crew flew half their approaches using the experimental synthesized voice approach callout system (SYNCALL) and the other half using the company pilot-not-flying approach callout procedures (PNF). Airspeed and sink rate performance was better with the SYNCALL system than with the PNF system for non-precision approaches. For the one-engine approach, for which SYNCALL made inappropriate deviation callouts, airspeed performance was worse with SYNCALL than with PNF. Reliability of normal altitude approach callouts was comparable for PNF on the line and in the simulator and for SYNCALL in the simulator.

Simpson, C. A.↗

NASA NTRS1989

Space station Simulation Computer System (SCS) study for NASA/MSFC. Volume 1: Overview and summary

NASA's Space Station Freedom Program (SSFP) planning efforts have identified a need for a payload training simulator system to serve as both a training facility and as a demonstrator to validate operational concepts. The envisioned Marshall Space Flight Center (MSFC) Payload Training Complex (PTC) required to meet this need will train the space station payload scientists, station scientists, and ground controllers to operate the wide variety of experiments that will be onboard the Space Station Freedom. The Simulation Computer System (SCS) is the computer hardware, software, and workstations that will support the Payload Training Complex at MSFC. The purpose of this SCS study is to investigate issues related to the SCS, alternative requirements, simulator approaches, and state-of-the-art technologies to develop candidate concepts and designs. This study was performed August 1988 to October 1989. Thus, the results are based on the SSFP August 1989 baseline, i.e., pre-Langley configuration/budget review (C/BR) baseline. Some terms, e.g., combined trainer, are being redefined. An overview of the study activities and a summary of study results are given here.

Source record↗

NASA NTRS1990

Space Station Simulation Computer System (SCS) study for NASA/MSFC. Volume 2: Baseline architecture report

NASA's Space Station Freedom Program (SSFP) planning efforts have identified a need for a payload training simulator system to serve as both a training facility and as a demonstrator to validate operational concepts. The envisioned MSFC Payload Training Complex (PTC) required to meet this need will train the Space Station payload scientists, station scientists, and ground controllers to operate the wide variety of experiments that will be onboard the Space Station Freedom. The Simulation Computer System (SCS) is the computer hardware, software, and workstations that will support the Payload Training Complex at MSFC. The purpose of this SCS Study is to investigate issues related to the SCS, alternative requirements, simulator approaches, and state-of-the-art technologies to develop candidate concepts and designs.

Source record↗