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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 NTRS1983

Graphics enhanced computer emulation for improved timing-race and fault tolerance control system analysis

A computer simulation system has been developed for the Space Shuttle's advanced Centaur liquid fuel booster rocket, in order to conduct systems safety verification and flight operations training. This simulation utility is designed to analyze functional system behavior by integrating control avionics with mechanical and fluid elements, and is able to emulate any system operation, from simple relay logic to complex VLSI components, with wire-by-wire detail. A novel graphics data entry system offers a pseudo-wire wrap data base that can be easily updated. Visual subsystem operations can be selected and displayed in color on a six-monitor graphics processor. System timing and fault verification analyses are conducted by injecting component fault modes and min/max timing delays, and then observing system operation through a red line monitor.

Szatkowski, G. P.↗
NASA NTRS1991

Ground Systems Development Environment (GSDE) interface requirements analysis: Operations scenarios

This report is a preliminary assessment of the functional and data interface requirements to the link between the GSDE GS/SPF (Amdahl) and the Space Station Control Center (SSCC) and Space Station Training Facility (SSTF) Integration, Verification, and Test Environments (IVTE's). These interfaces will be involved in ground software development of both the control center and the simulation and training systems. Our understanding of the configuration management (CM) interface and the expected functional characteristics of the Amdahl-IVTE interface is described. A set of assumptions and questions that need to be considered and resolved in order to complete the interface functional and data requirements definitions are presented. A listing of information items defined to describe software configuration items in the GSDE CM system is included. It also includes listings of standard reports of CM information and of CM-related tools in the GSDE.

Church, Victor E.↗
NASA NTRS2011

An Investigation of Interaction of Saharan Dust and Atlantic ITCZ Using Cloudsat-Calipso and A-Train Data

In this study, we investigate the radiative forcing of Saharan dust, its interactions with the Atlantic Intertropical Convergence Zone (ITCZ), through African easterly waves (AEW), African easterly jets (AEJ), and its impacts in short term numerical forecasts of tropical cyclogenesis using the GOCART-GEOS5 forecast system. Our approach is to develop and use an A-Train satellite simulator (ATSS) to constrain the observed aerosol index of refraction and particle size distribution by finding the values that simultaneously minimize the difference between observed CALIOP, CloudSat, OMI, and MODIS radiances and simulated radiances inverted from atmospheric model output using procedures and physical principles consistent with those used in corresponding retrieval algorithms. We use observations from the A-train and TRMM to determine relationships among the Saharan dust layer, transport by the AEW, and possible responses to dust radiative forcing in developing tropical cyclones in the A-ITCZ. Preliminary model results showing physical processes associated with the generation and transport of the Saharan dust layer, their interactions with the incipient moisture, clouds and rainfall in developing tropical cyclones will be presented. Also presented will be results of a case study of possible radiative impacts on AEW and AEJ during the NAMMA field campaign.

Lau, W.↗
NASA NTRS2002

Crew Training STS-110

The crewmembers are shown being suited for the STS-110 flight. The STS-110 crews are shown in training for four EVA's on the International Space Station. The crewmembers consist of: Michael J. Bloomfield, mission commander; Stephen N. Frick, pilot; and mission specialists: Ellen Ochoa, Lee M.E. Morin, Rex J. Walheim, Steven L. Smith, and Jerry Ross. Crew ascent middeck operations and Orbiter Skills Training in a fixed Based Simulator are the training areas shown. The STS-110 crew and Expedition four are seen during training at the Johnson Space Center Space Station Training Facility (SSTF). A photo session of the crew is also presented.

Source record↗
NASA NTRS2004

Using an Extended Kalman Filter Learning Algorithm for Feed-Forward Neural Networks to Describe Tracer Correlations

In this study a new extended Kalman filter (EKF) learning algorithm for feed-forward neural networks (FFN) is used. With the EKF approach, the training of the FFN can be seen as state estimation for a non-linear stationary process. The EKF method gives excellent convergence performances provided that there is enough computer core memory and that the machine precision is high. Neural networks are ideally suited to describe the spatial and temporal dependence of tracer-tracer correlations. The neural network performs well even in regions where the correlations are less compact and normally a family of correlation curves would be required. For example, the CH4-N2O correlation can be well described using a neural network trained with the latitude, pressure, time of year, and CH4 volume mixing ratio (v.m.r.). The neural network was able to reproduce the CH4-N2O correlation with a correlation coefficient between simulated and training values of 0.9997. The neural network Fortran code used is available for download.

Lary, David J.↗
NASA NTRS2022

SoMoGym: A Toolkit for Developing and Evaluating Controllers and Reinforcement Learning Algorithms for Soft Robots

Soft robotsoffer a host of benefits over traditional rigid robots, including inherent compliance that lets them passively adapt to variable environments and operate safely around humans and fragile objects. However, that same compliance makes it hard to use model-based methods in planning tasks requiring high precision or complex actuation sequences. Reinforcement learning (RL) can potentially find effective control policies, but training RL using physical soft robots is often infeasible, and training using simulations has had a high barrier to adoption. To accelerate research in control and RL for soft robotic systems, we introduce SoMoGym ( So ft Mo tion Gym ), a software toolkit that facilitates training and evaluating controllers for continuum robots. SoMoGym provides a set of benchmark tasks in which soft robots interact with various objects and environments. It allows evaluation of performance on these tasks for controllers of interest, and enables the use of RL to generate new controllers. Custom environments and robots can likewise be added easily. We provide and evaluate baseline RL policies for each of the benchmark tasks. These results show that SoMoGym enables the use of RL for continuum robots, a class of robots not covered by existing benchmarks, giving them the capability to autonomously solve tasks that were previously unattainable.

Moritz A. Graule↗
NASA NTRS2015

Ultrafast High Accuracy PCRTM_SOLAR Model for Cloudy Atmosphere

An ultrafast high accuracy PCRTM_SOLAR model is developed based on PCA compression and principal component-based radiative transfer model (PCRTM). A fast algorithm for simulation of multi-scattering properties of cloud and/or aerosols is integrated into the fast infrared PCRTM. We completed radiance simulation and training for instruments, such as IASI, AIRS, CrIS, NASTI and SHIS, under diverse conditions. The new model is 5 orders faster than 52-stream DISORT with very high accuracy for cloudy sky radiative transfer simulation. It is suitable for hyperspectral remote data assimilation and cloudy sky retrievals.

Yang, Qiguang↗
NASA NTRS2016

Development of a Fast and Accurate PCRTM Radiative Transfer Model in the Solar Spectral Region

A fast and accurate principal component-based radiative transfer model in the solar spectral region (PCRTMSOLAR) has been developed. The algorithm is capable of simulating reflected solar spectra in both clear sky and cloudy atmospheric conditions. Multiple scattering of the solar beam by the multilayer clouds and aerosols are calculated using a discrete ordinate radiative transfer scheme. The PCRTM-SOLAR model can be trained to simulate top-of-atmosphere radiance or reflectance spectra with spectral resolution ranging from 1 cm(exp −1) resolution to a few nanometers. Broadband radiances or reflectance can also be calculated if desired. The current version of the PCRTM-SOLAR covers a spectral range from 300 to 2500 nm. The model is valid for solar zenith angles ranging from 0 to 80 deg, the instrument view zenith angles ranging from 0 to 70 deg, and the relative azimuthal angles ranging from 0 to 360 deg. Depending on the number of spectral channels, the speed of the current version of PCRTM-SOLAR is a few hundred to over one thousand times faster than the medium speed correlated-k option MODTRAN5. The absolute RMS error in channel radiance is smaller than 10(exp −3) mW∕cm)exp 2)∕sr∕cm(exp −1) and the relative error is typically less than 0.2%.

Liu, Xu↗
NASA NTRS2016

The Man-machine Integration Design and Analysis System (MIDAS) Software Training Documentation

This document is a training manual for MIDAS v5 that takes an user through the hardware and software requirements for the MIDAS V5 software, the steps required to download the software, and the steps that a user needs to take to create a MIDAS simulation. The training guide also illustrates how the models interact to generate MIDAS predictions of operator performance along task, workload, and situation awareness timelines and provides the test routines that were conducted to verify the operation of the integrated MIDAS models. The training guide shows the user how the MIDAS task model interacts with and controls an anthropometric model through its use of behavioral primitives. The training documentation also illustrates one approach that has been used to filter and analyze MIDAS output.

Brian F. Gore↗
NASA NTRS1967

Subgravity simulator Patent

Apparatus for training astronaut crews to perform on simulated lunar surface under conditions of lunar gravity

Johnson, H. I.↗
NASA NTRS1997

Virtual Collaborative Simulation Environment for Integrated Product and Process Development

Deneb Robotics is a leader in the development of commercially available, leading edge three- dimensional simulation software tools for virtual prototyping,, simulation-based design, manufacturing process simulation, and factory floor simulation and training applications. Deneb has developed and commercially released a preliminary Virtual Collaborative Engineering (VCE) capability for Integrated Product and Process Development (IPPD). This capability allows distributed, real-time visualization and evaluation of design concepts, manufacturing processes, and total factory and enterprises in one seamless simulation environment.

Gulli, Michael A.↗
NASA NTRS2005

Engineering Effort Needed to Design Spacecraft with Radiation Constraints

A roadmap is articulated that describes what is needed to allow designers, to include researchers, management, and engineers, to investigate, design, build, test, and fly spacecraft that meet the mission requirements yet, be as low cost as possible. This roadmap describes seven levels of tool fidelity and application: 1) Mission Speculation, 2) Management Overview, 3) Mission Design, 4) Detailed Design, 5) Simulation and Training, 6) Operations, and 7) Research. The interfaces and output are described in top-level detail along with the transport engines needed, and deficiencies are noted. This roadmap, if implemented, will allow Multidisciplinary Optimization (MDO) ideas to incorporate radiation concerns. Also, as NASA moves towards Simulation Based Acquisition (SBA), these tools will facilitate the appropriate spending of government money. Most of the tools needed to serve these levels do not exist or exist in pieces and need to be integrated to create the tool.

Singleterry, Robert C., Jr.↗
NASA NTRS2021

Virtual Reality Training Lab

This video highlights the Virtual Reality Laboratory. It shows off the different systems the lab supports for crew training including Charlotte Mass Handling, Robotics and hardware checkout and SAFER. It focuses on the SAFER (Simplified Aid for EVA Rescue) system and shows briefly how to perform a SAFER rescue in Virtual Reality.

Simulation↗
NASA NTRS1992

Progress through precedent: Going where no helicopter simulator has gone before

Helicopter simulators have been approved by means of special exemption; there are no FAA standards for simulators used in training or airmen Certification checking. The fixed-wing industry provides a precedent which can be used for expediting implementation of helicopter simulators. The analysis in this paper is founded on the experience with that precedent and is driven by a clear definition of helicopter user needs for (1) improved training at lower cost, (2) more comprehensive emergency training at lower risk, (3) increased fidelity of transition and instrument training compared with low-cost aircraft alternatives, and (4) certification credit for improved simulator training.

Adams, Richard J.↗
NASA NTRS1990

An ICAI architecture for troubleshooting in complex, dynamic systems

Ahab, an intelligent computer-aided instruction (ICAI) program, illustrates an architecture for simulator-based ICAI programs to teach troubleshooting in complex, dynamic environments. The architecture posits three elements of a computerized instructor: the task model, the student model, and the instructional module. The task model is a prescriptive model of expert performance that uses symptomatic and topographic search strategies to provide students with directed problem-solving aids. The student model is a descriptive model of student performance in the context of the task model. This student model compares the student and task models, critiques student performance, and provides interactive performance feedback. The instructional module coordinates information presented by the instructional media, the task model, and the student model so that each student receives individualized instruction. Concept and metaconcept knowledge that supports these elements is contained in frames and production rules, respectively. The results of an experimental evaluation are discussed. They support the hypothesis that training with an adaptive online system built using the Ahab architecture produces better performance than training using simulator practice alone, at least with unfamiliar problems. It is not sufficient to develop an expert strategy and present it to students using offline materials. The training is most effective if it adapts to individual student needs.

Fath, Janet L.↗
NASA NTRS1995

Hitchhiker mission operations: Past, present, and future

What is mission operations? Mission operations is an iterative process aimed at achieving the greatest possible mission success with the resources available. The process involves understanding of the science objectives, investigation of which system capabilities can best meet these objectives, integration of the objectives and resources into a cohesive mission operations plan, evaluation of the plan through simulations, and implementation of the plan in real-time. In this paper, the authors present a comprehensive description of what the Hitchhiker mission operations approach is and why it is crucial to mission success. The authors describe the significance of operational considerations from the beginning and throughout the experiment ground and flight systems development. The authors also address the necessity of training and simulations. Finally, the authors cite several examples illustrating the benefits of understanding and utilizing the mission operations process.

Anderson, Kathryn↗
NASA NTRS1994

Stereoscopic, Force-Feedback Trainer For Telerobot Operators

Computer-controlled simulator for training technicians to operate remote robots provides both visual and kinesthetic virtual reality. Used during initial stage of training; saves time and expense, increases operational safety, and prevents damage to robots by inexperienced operators. Computes virtual contact forces and torques of compliant robot in real time, providing operator with feel of forces experienced by manipulator as well as view in any of three modes: single view, two split views, or stereoscopic view. From keyboard, user specifies force-reflection gain and stiffness of manipulator hand for three translational and three rotational axes. System offers two simulated telerobotic tasks: insertion of peg in hole in three dimensions, and removal and insertion of drawer.

Kim, Won S.↗