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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 325 records · Page 18

All sources NASA NTRS DOE OSTI

NASA NTRS1994

Attempt of automated space network operations at ETS-VI experimental data relay system

National Space Development Agency of Japan (NASDA) is to perform experimental operations to acquire necessary technology for the future inter-satellite communications configured with a data relay satellite. This paper intends to overview functions of the experimental ground system which NASDA has developed for the Engineering Test Satellite VI (ETS-VI) Data Relay and Tracking Experiment, and to introduce Space Network System Operations Procedure (SNSOP) method with an example of Ka-band Single Access (KSA) acquisition sequence. To reduce operational load, SNSOP is developed with the concept of automated control and monitor of both ground terminal and data relay satellite. To perform acquisition and tracking operations fluently, the information exchange with user spacecraft controllers is automated by SNSOP functions.

Ishihara, Kiyoomi↗

DOE OSTI2022

A cosine-based correlation information entropy approach for building automatic fault detection baseline construction

Building automatic fault detection and diagnosis (AFDD) technologies have shown great potential for energy savings. To enable AFDD, a baseline depicting the normal operation mode is needed to detect whether the building operation deviates from normality. Existing research using physics-based knowledge and models for AFDD has mainly taken a trial-and-error approach to determine if a given baseline is sufficient via empirical experiments. A mechanism to support decisions such as how many samples and what samples should be included in the baseline is currently lacking. In this study, a data-driven method for AFDD baseline construction based on information entropy is developed. The entropy is derived based on cosine similarity among typical building automation system measurements in conjunction with outdoor weather information. The performance of the proposed method is evaluated using real building data. Evaluation results indicate that the fault detection strategy adopting the proposed method has similar or better accuracy in detecting faults compared to the same fault detection strategy using the baseline construction method from the literature. Additionally, the use of entropy enables the proposed method to automatically construct and assess the baseline consisting of information-rich samples.

42 ENGINEERING↗

NASA NTRSDate not supplied

Metadata Entry Optimization for NASA's Biological Institutional Scientific Collection (NBISC)

The NASA Biological Institutional Sample Collection (NBISC) at NASA’s Ames Research Center is a critical resource housing non-human samples collected from spaceflight missions and ground analog studies, primarily consisting of specimens from rats, mice, and select microbes. The primary objective of NBISC is to systematically receive, document, preserve, and facilitate access to these samples for the global scientific community. NBISC promotes international collaboration and maximizes the return on investment for precious tissues from spaceflight and analog experiments. Researchers can request physical samples through an online request form and subsequent written proposal review process. This study addresses two core research objectives: streamlining the NBISC sample lifecycle processes and strategizing for managing an influx of 50,000 tissue samples from a series of cosmic radiation analog experiments carried out at the NASA Space Radiation Laboratory (NSRL) by Drs. Eleanor Chang (Lawrence Berkeley Laboratory) and Polly Blakely (SRI). The Chang/Blakely studies investigated Harderian gland (HG) tumorigenesis in mice exposed to low dose and LET radiation comprising 8 different exposure protocols in over 4000 mice. NBISC sample metadata is stored in a Laboratory Information Management System (SLIMS). To streamline sample data entry, we customize python scripts using information extracted from the individual experimental protocols. The scripts automate entry into multiple SLIMS data fields including protocol name, unique sample barcode, tissue and sub-tissue information, freezer location, sample preservation method, etc. The semi-automated procedure significantly decreases the time spent on data entry by several orders of magnitude. Automation and data organization are essential, as they free up time for curation and promotion of the collection which, in turn, increase the accessibility of samples to the broader research community. NBISC benefits from streamlined data ingestion, and the methodologies developed here are applicable to other projects which use SLIMS including the NASA Biospecimen Sharing Program and GeneLab. As of Fall 2023, plans include transferring sample data from SLIMS to public facing repositories (OSDR and NLSP), expanding the reach of the Chang/Blakely sample collection. The Human Research Program Space Radiation Element plans to transfer non-human tissues from many more investigations to NBISC in the coming year.

Sample Repository↗

NASA NTRSDate not supplied

Metadata Entry Optimization For NASA's Biological Institutional Scientific Collection (NBISC)

Biospecimen↗

NASA NTRS1984

Space station automation study. Volume 1: Executive summary. Autonomous systems and assembly

The space station automation study (SSAS) was to develop informed technical guidance for NASA personnel in the use of autonomy and autonomous systems to implement space station functions. The initial step taken by NASA in organizing the SSAS was to form and convene a panel of recognized expert technologists in automation, space sciences and aerospace engineering to produce a space station automation plan.

Source record↗

NASA NTRS2019

TCL3 UTM (UAS Traffic Management) Flight Tests, Airspace Operations Laboratory (AOL) Report

The Technology Capability Level-3 (TCL3) flight tests were conducted at six different test sites located across the USA from March to May of 2018. The campaign resulted in over 830 data collection flights using 28 different aircraft and involving 20 flight crews. Flights not only varied in duration, but also in the environments and terrains over which they flew. The TCL3 tests highlighted four different types of tests: three tests focused on Communication, Navigation and Surveillance (CNS); six tests focused on Sense and Avoid (SAA) technologies; six tests focused on USS Data and Information Exchange (DAT); and five tests focused on exploring fundamental Concepts of the project (CON). This document presents data collected during the TCL3 tests that informed the operator’s experiences—the quality of the unmanned aerial system (UAS) Service Supplier (USS) information that the operator was provided with, the usefulness of this information, and the usability of the automation, both while airborne and on the ground. It is intended to complement the reports written by the test sites and the quantitative reports and presentations of the UAS Traffic Management (UTM) project. With the goal of instructing what the minimum information requirements and/or best practices might be in TCL3 operations, the driving enquiry was: How do you get the information you need, when you need it, to successfully fly a UAS in UTM airspace? This enquiry touches on two requirements for displays, which are to provide adequate situation awareness (SA) and to share information through a USS. The six test sites participating in the TCL3 tests flew a subset of the 20 tests (outlined above), with most sites working on a subset of each of the four types: Communications, Navigation and Surveillance (CNS); DAT; CON; and Sense and Avoid (SAA). The, mainly qualitative, data addressed in this report was collected by the AOL (Airspace Operations Laboratory) both on-site and remotely for each test. The data consists of the contents of end-of-day debriefs, end-of-day surveys, observer notes, and flight test information, all submitted as part of the Data Management Plan (DMP).

Martin, Lynne↗

NASA NTRSDate not supplied

Automation of the ICME Workflow Incorporating Material Digital Twins at Different Length Scales Within a Robust Information Management System

Recent successes in Integrated Computational Materials Engineering (ICME) have demonstrated the potential in designing fit-for-purpose materials for a given application in a cost and time efficient manner. However, the material design process must contain a level of automation in the material decision process, implementing some optimization algorithms, to truly enable the full benefits of ICME, particularly when considering materials at multiple length/time scales. In this work, we will demonstrate how the GRC ICME schema and Python framework automates a workflow that captures, analyzes, maintains, and disseminates the digital footprint in the context of tailoring resin material at the nanoscale of a woven composite Y-joint at the macroscale for an Aurora D8 double bubble fuselage. This digital footprint incorporates the interaction of both structural digital twins and material twins at various length scales.

Brandon L. Hearley↗

NASA NTRS1984

Automated RTOP Management System

The structure of NASA's Office of Aeronautics and Space Technology electronic information system network from 1983 to 1985 is illustrated. The RTOP automated system takes advantage of existing hardware, software, and expertise, and provides: (1) computerized cover sheet and resources forms; (2) electronic signature and transmission; (3) a data-based information system; (4) graphics; (5) intercenter communications; (6) management information; and (7) text editing. The system is coordinated with Headquarters efforts in codes R,E, and T.

Hayes, P.↗

NASA NTRS1994

Matrix Failure Modes and Effects Analysis as a Knowledge Base for a Real Time Automated Diagnosis Expert System

Failure Modes and Effects Analysis contain a wealth of information that can be used to create the knowledge base required for building automated diagnostic Expert systems. A real time monitoring and diagnosis expert system based on an actual NASA project's matrix failure modes and effects analysis was developed. This Expert system Was developed at NASA Ames Research Center. This system was first used as a case study to monitor the Research Animal Holding Facility (RAHF), a Space Shuttle payload that is used to house and monitor animals in orbit so the effects of space flight and microgravity can be studied. The techniques developed for the RAHF monitoring and diagnosis Expert system are general enough to be used for monitoring and diagnosis of a variety of other systems that undergo a Matrix FMEA. This automated diagnosis system was successfully used on-line and validated on the Space Shuttle flight STS-58, mission SLS-2 in October 1993.

Herrin, Stephanie↗

NASA NTRS1990

Choice of image attribute for automated radargrammetry

This paper discusses SAR imaging with emphasis on radar backscatter properties. In studying how planetary surfaces modulate the radar cross-section, it is determined that both the amount of and variation in backscattered microwave energy provide the necessary cues (i.e., object shading and boundary information) for stereointerpretation. While past research into automated reconstruction of topography from radar stereopairs has concentrated on the use of shading, it is shown that boundary information may also be used successfully. How these attributes can be potentially misleading are discussed. This work suggests a process for generating high-quality topography maps from SAR imagery.

Matarese, Joseph R.↗

NASA NTRS2000

A Comparison of Center/TRACON Automation System and Airline Time of Arrival Predictions

Benefits from information sharing between an air traffic service provider and a major air carrier are evaluated. Aircraft arrival time schedules generated by the NASA/FAA Center/TRACON Automation System (CTAS) were provided to the American Airlines System Operations Control Center in Fort Worth, Texas, during a field trial of a specialized CTAS display. A statistical analysis indicates that the CTAS schedules, based on aircraft trajectories predicted from real-time radar and weather data, are substantially more accurate than the traditional airline arrival time estimates, constructed from flight plans and en route crew updates. The improvement offered by CTAS is especially advantageous during periods of heavy traffic and substantial terminal area delay, allowing the airline to avoid large predictive errors with serious impact on the efficiency and profitability of flight operations.

Heere, Karen R.↗

NASA NTRS1991

Aircrew decision-making behavior in hazardous weather avoidance

A study was conducted to assess the situation awareness and decision-making behavior of aircrews in a line-oriented simulation of the microburst/windshear encounter which occurred on July 11, 1988, at the Denver Stapleton Airport as affected by the manner in which information available at different times and in different forms. Intracrew communications and approach-to-land decisions were evaluated with conventional ATC communications and with automated cockpit alerting and display of weather information. It was found that the avoidance dicision-making performance of aircrews provided only with conventional ATC transmissions of weather information was significantly less efficient than the performance of crews provided with a visual display of microburst events.

Lee, Alfred T.↗

DOE OSTI2020

Decision Support Tool for Planning Neighborhood-Scale Deployment of Low-Speed Shared Automated Shuttles

Increasing interest and investment in connected, automated, and electric vehicles as well as mobility-as-a-service (MaaS) concepts are paving the way for the next major transformation in transportation through automated and shared mobility. The initial excitement toward rapid deployment and adoption of automated vehicles (AVs) has subsided, and low-speed automated shuttles are emerging as a more pragmatic pathway for introducing automated mobility in geofenced districts. Such shuttles hold the promise to provide a viable alternative for serving short trips in urban districts with high travel densities. As interest in low-speed automated shuttle systems (to improve urban mobility) increases, the need for tools that can inform communities in relation to benefits or disadvantages of automated shuttle deployments is imminent. However, most of the existing transportation planning and simulation tools are not capable of handling emerging shared automated mobility options. This paper presents a microscopic simulation toolkit that can be used by cities and communities to plan for the deployment of low-speed automated shuttles systems, as well as other shared mobility options. Labeled as the Automated Mobility District modeling and simulation toolkit, the proposed decision support tool intends to help cities evaluate the mobility and sustainability impacts of deploying shared automated vehicles (SAVs) in geofenced regions. This paper describes the toolkit, as well as a sample scenario analysis for the deployment of low-speed automated shuttles in Greenville, South Carolina, U.S. Results from the scenario study demonstrate the effectiveness of the proposed simulation toolkit in planning for advanced mobility systems.

47 OTHER INSTRUMENTATION↗

NASA NTRS2011

ISS Operations Cost Reductions Through Automation of Real-Time Planning Tasks

In 2007 the Johnson Space Center s Mission Operations Directorate (MOD) management team challenged their organizations to find ways to reduce the cost of operations for supporting the International Space Station (ISS) in the Mission Control Center (MCC). Each MOD organization was asked to define and execute projects that would help them attain cost reductions by 2012. The MOD Operations Division Flight Planning Branch responded to this challenge by launching several software automation projects that would allow them to greatly improve console operations and reduce ISS console staffing and intern reduce operating costs. These tasks ranged from improving the management and integration mission plan changes, to automating the uploading and downloading of information to and from the ISS and the associated ground complex tasks that required multiple decision points. The software solutions leveraged several different technologies including customized web applications and implementation of industry standard web services architecture; as well as engaging a previously TRL 4-5 technology developed by Ames Research Center (ARC) that utilized an intelligent agent-based system to manage and automate file traffic flow, archive data, and generate console logs. These projects to date have allowed the MOD Operations organization to remove one full time (7 x 24 x 365) ISS console position in 2010; with the goal of eliminating a second full time ISS console support position by 2012. The team will also reduce one long range planning console position by 2014. When complete, these Flight Planning Branch projects will account for the elimination of 3 console positions and a reduction in staffing of 11 engineering personnel (EP) for ISS.

Hall, Timothy A.↗

DOE OSTI2020

A Decision Support Tool for Planning Neighborhood-Scale Deployment of Low-Speed Shared Automated Shuttles

Increasing interest and investment in connected, automated, and electric vehicles, and mobility-as-a-service concepts are paving the way for the next major shift in transportation through automated and shared mobility. The initial excitement towards rapid deployment and adoption of automated vehicles has subsided, and low-speed automated shuttles are emerging as a more pragmatic pathway for introducing automated mobility in geo-fenced districts. Such shuttles hold the promise to provide a viable alternative for serving short trips in urban districts with high travel densities. As interest in low-speed automated shuttle systems (to improve urban mobility) increases, the need for tools that can inform communities regarding benefits or dis-benefits of automated shuttle deployments is imminent. However, most of the existing transportation planning and simulation tools are not capable of handling emerging shared automated mobility options. This presentation presents a microscopic simulation toolkit that can be used by cities and communities to plan for the deployment of low-speed automated shuttles systems, as well as other shared mobility options. Labeled as the Automated Mobility District (AMD) modeling and simulation toolkit, the proposed decision support tool can help cities evaluate the mobility and sustainability impacts of deploying shared automated vehicles in geofenced regions. This paper presents a description of the toolkit, as well as a sample scenario analysis for the deployment of low-speed automated shuttles in Greenville, South Carolina. Results from the scenario study demonstrate the effectiveness of the proposed simulation toolkit in planning for advanced mobility systems.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

DOE OSTI2020

Modeling and Simulation of Automated Mobility Districts

Increasing interest and investment in connected, automated, and electric vehicles (CAEVs), and mobility-as-a-service (MaaS) concepts are paving the way for the next major shift in transportation through automated and shared mobility. The initial excitement towards rapid deployment and adoption of automated vehicles has subsided, and low-speed automated shuttles are emerging as a more pragmatic pathway for introducing automated mobility in geo-fenced districts. Such shuttles hold the promise to provide a viable alternative for serving short trips in urban districts with high travel densities. As interest in low-speed automated shuttle systems (to improve urban mobility) increases, the need for tools that can inform communities regarding benefits or dis-benefits of automated shuttle deployments is imminent. However, most of the existing transportation planning and simulation tools are not capable of handling emerging shared automated mobility options. This presentation provide the details of a microscopic simulation toolkit that can be used by cities and communities to plan for the deployment of low-speed automated shuttles systems, as well as other shared mobility options. Labeled as Automated Mobility District (AMD) modeling and simulation toolkit, the proposed decision support tool intends to help cities evaluate the mobility and sustainability impacts of deploying shared automated vehicles in geofenced regions.The AMD modeling and simulation task is funded through DOE's SMART Mobility Consortium. This presentation outlines the progress of the AMD modeling and simulation task in FY19, including case studies conducted in Greenville, SC, and Austin, TX.

47 OTHER INSTRUMENTATION↗

DOE OSTI2022

Automated System-wide Event Detection and Classification Using Machine Learning on Synchrophasor Data

As the number of phasor measurement units (PMUs) deployed in a power system increases, and their data volume streamed to the control canter intensifies, operators are facing challenges related to the analysis of such data, which need to be observed and responded to as the measurements are displayed in the Control Room. Humans are generally unable to process such large amount of data efficiently and rapidly. There is an apparent need for automated ways to analyze the data, extract actionable information about occurrence of specific events, and characterize the events quickly and cost effectively. This paper discusses the use of machine learning (ML) to facilitate such tasks by providing automated, highly computationally efficient, and cost-effective ways of extracting actionable information from synchrophasor big data in real-time. We developed Big Data Smart (BDSmart) ML-based prototype tool for the Control Room use that automatically analyses data properties from synchrophasor system measurements taken across the three grid Interconnections in the USA (Western, Eastern and ERCOT). The data collected from several hundreds of PMUs located across the Interconnections over a period of two years have been made available for our extensive study. As a result, we were able to identify a number of big data properties that influence how ML methodology is applied to select, develop, train and test the data models that can eventually be used for the tool implementation. The resulting set of candidate algorithms spans unsupervised, supervised, semi-supervised and transfer-learning approaches. Many ML techniques, such as decision trees, multinomial logistic regression, feed-forward neural networks, K-nearest neighbor, multiclass support vector machine, and single and multi-channel convolutional neural networks, are implemented, and their performance is examined. We offer the results from testing the data models. The novelty of our study is in the approaches for bad data detection and mitigation, selection of a simplified feature for event detection, and data label improvements. As a result, we came up with a list of recommendations for the utilities on how to improve the PMU recording practices to cater to the future ML applications aimed at automating the analysis of synchrophasor data.

Synchrophasors, Machine Learning, System-wide Even↗

NASA NTRS1979

Automated Solar Module Assembly Line

The gathering of information that led to the design approach of the machine, and a summary of the findings in the areas of study along with a description of each station of the machine are discussed. The machine is a cell stringing and string applique machine which is flexible in design, capable of handling a variety of cells and assembling strings of cells which can then be placed in a matrix up to 4 ft x 2 ft. in series or parallel arrangement. The target machine cycle is to be 5 seconds per cell. This machine is primarily adapted to 100 MM round cells with one or two tabs between cells. It places finished strings of up to twelve cells in a matrix of up to six such strings arranged in series or in parallel.

Bycer, M.↗