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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 73 records · Page 4
All sourcesNASA NTRSDOE OSTI
NASA NTRSDate not supplied

Goddard Mission Services Evolution Center “GMSEC” Overview

In today's changing satellite mission operations landscape, the need for mission automation and interoperability while containing costs is more important than ever. Enter GMSEC – a flexible and extendible set of software automation tools designed to automate repetitive processes and off-hours alerting, among others. GMSEC is also an API that is open source and supports the OMG C2MS message specification, allowing dissimilar components to communicate with each other. Attendees will learn about the possible use cases for automation that the GMSEC architecture and suite of components enable. The presentation will provide an overview of specific GMSEC software products that provide these automation features such as the GMSEC Generic Extendible Message Utility (GEMU) that enables automation, and GMSEC Services Suite OpenMCT (GSS) that offers enhanced telemetry visualization. This presentation will outline how GMSEC can enable and enhance mission operations automation, enabling efficiencies that can help bring down costs.

James C. Hoffman↗
NASA NTRS1990

Computer vision techniques for rotorcraft low altitude flight

Rotorcraft operating in high-threat environments fly close to the earth's surface to utilize surrounding terrain, vegetation, or manmade objects to minimize the risk of being detected by an enemy. Increasing levels of concealment are achieved by adopting different tactics during low-altitude flight. Rotorcraft employ three tactics during low-altitude flight: low-level, contour, and nap-of-the-earth (NOE). The key feature distinguishing the NOE mode from the other two modes is that the whole rotorcraft, including the main rotor, is below tree-top whenever possible. This leads to the use of lateral maneuvers for avoiding obstacles, which in fact constitutes the means for concealment. The piloting of the rotorcraft is at best a very demanding task and the pilot will need help from onboard automation tools in order to devote more time to mission-related activities. The development of an automation tool which has the potential to detect obstacles in the rotorcraft flight path, warn the crew, and interact with the guidance system to avoid detected obstacles, presents challenging problems. Research is described which applies techniques from computer vision to automation of rotorcraft navigtion. The effort emphasizes the development of a methodology for detecting the ranges to obstacles in the region of interest based on the maximum utilization of passive sensors. The range map derived from the obstacle-detection approach can be used as obstacle data for the obstacle avoidance in an automatic guidance system and as advisory display to the pilot. The lack of suitable flight imagery data presents a problem in the verification of concepts for obstacle detection. This problem is being addressed by the development of an adequate flight database and by preprocessing of currently available flight imagery. The presentation concludes with some comments on future work and how research in this area relates to the guidance of other autonomous vehicles.

Sridhar, Banavar↗
NASA NTRS1994

Robotic Tool Changer For Automated Welding

Prototype robotic tool changer for automated welding system eliminates need for most manual tool setups and attendant problems: operates rapidly, always chooses designated tool, maneuvers tip of welding torch or other tool in correct position, and reliably connects water, gas, welding wire, high-voltage electrical signals, and ground. Also loads tools other than those for welding. Intended for use in robotic work cell producing all good parts, no rejects. In production, robot welds part, tests for flaws, and reworks as necessary before releasing it.

Gilbert, Jeffrey L.↗
NASA NTRSDate not supplied

New Tools for Automating Arcjet Sample Recession Tracking and Analysis

Arcjet Computer Vision (arcjetCV) has been significantly upgraded to enhance accuracy and performance in tracking material recession and shock-material standoff in test videos. These improvements include integrating new machine learning models, developing a specialized edge detection class, and incorporating a more comprehensive training dataset. These upgrades have refined the software’s ability to automate time-resolved recession tracking, making it more precise and reliable for analyzing complex physical processes. In parallel, a new tool called STARscan (Spatial Targeting and Alignment Rig for Scanning) is being developed to capture detailed 3D surface data before and after testing. By comparing these pre- and post-test scans with arcjetCV’s automated video analysis results, users can achieve a more comprehensive assessment of material recession. This method enables cross-validation of results, improving confidence in the analysis of tested materials. The expanded capabilities of arcjetCV have been successfully demonstrated on videos from various facilities, including the NASA Ames arcjets, UIUC’s PlasmatronX, and the VKI Plasmatron. It has been adopted as a new standard for in-situ recession tracking by the Mars Sample Return Project and Orion. ArcjetCV’s improved efficiency and accuracy are critical for reducing testing uncertainties and validating heatshield material performance under extreme conditions. The software’s user-friendly graphical interface ensures ease of use, enabling seamless processing and precise analysis of arcjet videos, providing deeper insights into material behavior in hypersonic environments. ArcjetCV is now available on both PyPI and Conda, allowing easy installation via "pip install arcjetCV" or through the Conda package manager, ensuring broad accessibility and streamlined deployment for users across various platforms.

Ablation↗
DOE OSTI2018

VALIDATING the Accuracy of Sighten's Automated Shading Tool

Solar companies - including installers, financiers, and distributors - leverage Sighten software to deliver accurate shading calculations and solar proposals. Sighten recently partnered with Google Project Sunroof to provide automated remote shading analysis directly within the Sighten platform. The National Renewable Energy Laboratory (NREL), in partnership with Sighten, independently verified the accuracy of Sighten's remote-shading solar access values (SAVs) on an annual basis for locations in Los Angeles, California, and Denver, Colorado.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2025

BAMCensus (The Behavior and Advanced Mobility Census Dataset Aggregator) [SWR-25-120]

This software is a high-performance tool developed in Rust for downloading and processing large-scale geospatial datasets, specifically focusing on US Census data. It is designed to address scaling limitations found in existing tools, such as R's [tidycensus](https://walker-data.com/tidycensus/), by providing performant streaming dataset JOIN operations between various US Census datasets (like ACS and LEHD) and their corresponding geometries stored on the TIGER/Lines web server. The tool automates the process of joining these data sources, returning aggregated data to the user based on a specified census GEOID type. The tool automates the process of joining these data sources, returning aggregated data to the user based on a specified census GEOID type. Its primary motivation stems from the need for a high-performance solution to combine spatial datasets with graph traversals within the context of mobility analysis tooling being developed at NREL's Behavior and Advanced Mobility (BAM) group.

Fitzgerald, Robert [National Renewable Energy Labo↗
NASA NTRS2005

Remotely Operated Aircraft (ROA) Impact on the National Airspace System (NAS) Work Package: Automation Impacts of ROA's in the NAS

The purpose of this document is to analyze the impact of Remotely Operated Aircraft (ROA) operations on current and planned Air Traffic Control (ATC) automation systems in the En Route, Terminal, and Traffic Flow Management domains. The operational aspects of ROA flight, while similar, are not entirely identical to their manned counterparts and may not have been considered within the time-horizons of the automation tools. This analysis was performed to determine if flight characteristics of ROAs would be compatible with current and future NAS automation tools. Improvements to existing systems / processes are recommended that would give Air Traffic Controllers an indication that a particular aircraft is an ROA and modifications to IFR flight plan processing algorithms and / or designation of airspace where an ROA will be operating for long periods of time.

Source record↗
DOE OSTI2012

QUALITATIVE INTERPRETATION OF GALAXY SPECTRA

We describe a simple step-by-step guide to qualitative interpretation of galaxy spectra. Rather than an alternative to existing automated tools, it is put forward as an instrument for quick-look analysis and for gaining physical insight when interpreting the outputs provided by automated tools. Though the recipe is for general application, it was developed for understanding the nature of the Automatic Spectroscopic K-means-based (ASK) template spectra. They resulted from the classification of all the galaxy spectra in the Sloan Digital Sky Survey data release 7, thus being a comprehensive representation of the galaxy spectra in the local universe. Using the recipe, we give a description of the properties of the gas and the stars that characterize the ASK classes, from those corresponding to passively evolving galaxies, to H II galaxies undergoing a galaxy-wide starburst. The qualitative analysis is found to be in excellent agreement with quantitative analyses of the same spectra. We compare the mean ages of the stellar populations with those inferred using the code STARLIGHT. We also examine the estimated gas-phase metallicity with the metallicities obtained using electron-temperature-based methods. A number of byproducts follow from the analysis. There is a tight correlation between the age of the stellar population and the metallicity of the gas, which is stronger than the correlations between galaxy mass and stellar age, and galaxy mass and gas metallicity. The galaxy spectra are known to follow a one-dimensional sequence, and we identify the luminosity-weighted mean stellar age as the affine parameter that describes the sequence. All ASK classes happen to have a significant fraction of old stars, although spectrum-wise they are outshined by the youngest populations. Old stars are metal-rich or metal-poor depending on whether they reside in passive galaxies or in star-forming galaxies.

79 ASTRONOMY AND ASTROPHYSICS↗
NASA NTRS2020

Great Lakes Water Resources II: A Google Earth Engine Tool to Automate Wetland Mapping Using Optical and Radar Satellite Sensors in the Great Lakes Basin for Wetland Management and Monitoring

The Great Lakes Basin is one of the world’s largest freshwater ecosystems. The Basin harbors over 200,000 acres of wetlands. These wetlands provide a variety of environmental, ecological, and recreational functions to over 30 million people in the region. Some of these functions include improving water quality, mitigating flood impacts, providing wildlife habitat, and housing recreational activities. However, due to anthropogenic activities, habitat conversion and degradation threaten to disrupt or destroy remaining wetland ecosystems. Maps of wetland distribution based on ground surveys are costly and labor-intensive, prohibiting timely evaluations of wetland loss and gain. The Great Lakes Water Resources II team at the NASA Jet Propulsion Laboratory developed the Wetlands Extent Tool 2.0 (WET 2.0) in Google Earth Engine to automate mapping of wetland distribution in the Great Lakes Basin. The team partnered with the US Fish and Wildlife Service (USFWS), Environmental Protection Agency (EPA), Minnesota Department of Natural Resources (MDNR), the National Oceanic and Atmospheric Administration (NOAA), and Ducks Unlimited (DU). WET 2.0 incorporates Landsat 8 Operational Land Imager (OLI), Sentinel-1 C-band Synthetic Aperture Radar (C-SAR), and Sentinel-2 Multispectral Instrument (MSI) satellite data. WET 2.0 is trained to classify anywhere in the Great Lakes Basin. Utilizing a Random Forest classifier, WET 2.0 is capable of automatically mapping wetland extent in the entire Great Lakes Basin, achieving a mean overall accuracy of 80.12% when tested in Michigan. Findings and maps produced in WET 2.0 will enable our partners to identify areas of ecosystem degradation and wetland destruction in order to enact environmental practices and policy initiatives to maintain environmental and economic health in the area.

Water Resources↗
NASA NTRS2020

Great Lakes Water Resources II: A Google Earth Engine Tool to Automate Wetland Extent Mapping Using Optical and Radar Satellite Sensors in the Great Lakes Basin for Wetland Management and Monitoring

The Great Lakes Basin is one of the world’s largest freshwater ecosystems. The Basin harbors over 200,000 acres of wetlands that provide a variety of environmental, ecological, and recreational functions to over 30 million people in the region. Some of these functions include improving water quality, mitigating flood impacts, providing wildlife habitat, and housing recreational activities. However, due to anthropogenic activities, habitat conversion and degradation threaten to disrupt or destroy remaining wetland ecosystems. Maps of wetland distribution based on ground surveys are costly and labor-intensive, prohibiting timely evaluations of wetland loss and gain. The Great Lakes Water Resources II team at the NASA Jet Propulsion Laboratory developed the Wetlands Extent Tool 2.0 (WET 2.0) in Google Earth Engine to automate mapping of wetland distribution in the Great Lakes Basin. The team partnered with the US Fish and Wildlife Service, Environmental Protection Agency, Minnesota Department of Natural Resources, the National Oceanic and Atmospheric Administration, and Ducks Unlimited. WET 2.0 incorporates Landsat 8 Operational Land Imager (OLI), Sentinel-1 C-band Synthetic Aperture Radar (C-SAR), and Sentinel-2 Multispectral Instrument (MSI) data. Utilizing a Random Forest classifier, WET 2.0 is capable of automatically mapping wetland extent in the entire Great Lakes Basin, achieving a mean overall accuracy of 80.12% when tested in Michigan.Findings and maps produced by WET 2.0 will enable our partners to identify areas of ecosystem degradation and wetland destruction in order to enact environmental practices and policy initiatives to maintain environmental and economic health in the area.

Water Resources↗
NASA NTRS2000

Perl Tools for Automating Satellite Ground Systems

The freeware scripting language Pert offers many opportunities for automating satellite ground systems for new satellites as well as older, in situ systems. This paper describes a toolkit that has evolved from of the experiences gained by using Pert to automate the ground system for the Compton Gamma Ray Observatory (CGRO) and for automating some of the elements in the Earth Observing System Data and Operations System (EDOS) ground system at Goddard Space Flight Center (GSFC). CGRO is an older ground system that was forced to automate because of fund cuts. Three 8 hour shifts were cut back to one 8 hour shift, 7 days per week. EDOS supports a new mission called Terra, launched December 1999 that requires distribution and tracking of mission-critical reports throughout the world. Both of these ground systems use Pert scripts to process data and display it on the Internet as well as scripts to coordinate many of the other systems that make these ground systems work as a coherent whole. Another task called Automated Multimodal Trend Analysis System (AMTAS) is looking at technology for isolation and recovery of spacecraft problems. This effort has led to prototypes that seek to evaluate various tools and technology that meet at least some of the AMTAS goals. The tools, experiences, and lessons learned by implementing these systems are described here.

McLean, David↗
DOE OSTI2016

SU-G-206-01: A Fully Automated CT Tool to Facilitate Phantom Image QA for Quantitative Imaging in Clinical Trials

Purpose: The use of Quantitative Imaging (QI) methods in Clinical Trials requires both verification of adherence to a specified protocol and an assessment of scanner performance under that protocol, which are currently accomplished manually. This work introduces automated phantom identification and image QA measure extraction towards a fully-automated CT phantom QA system to perform these functions and facilitate the use of Quantitative Imaging methods in clinical trials. Methods: This study used a retrospective cohort of CT phantom scans from existing clinical trial protocols - totaling 84 phantoms, across 3 phantom types using various scanners and protocols. The QA system identifies the input phantom scan through an ensemble of threshold-based classifiers. Each classifier - corresponding to a phantom type - contains a template slice, which is compared to the input scan on a slice-by-slice basis, resulting in slice-wise similarity metric values for each slice compared. Pre-trained thresholds (established from a training set of phantom images matching the template type) are used to filter the similarity distribution, and the slice with the most optimal local mean similarity, with local neighboring slices meeting the threshold requirement, is chosen as the classifier’s matched slice (if it existed). The classifier with the matched slice possessing the most optimal local mean similarity is then chosen as the ensemble’s best matching slice. If the best matching slice exists, image QA algorithm and ROIs corresponding to the matching classifier extracted the image QA measures. Results: Automated phantom identification performed with 84.5% accuracy and 88.8% sensitivity on 84 phantoms. Automated image quality measurements (following standard protocol) on identified water phantoms (n=35) matched user QA decisions with 100% accuracy. Conclusion: We provide a fullyautomated CT phantom QA system consistent with manual QA performance. Further work will include parallel component to automatically verify image acquisition parameters and automated adherence to specifications. Institutional research agreement, Siemens Healthcare; Past recipient, research grant support, Siemens Healthcare; Consultant, Toshiba America Medical Systems; Consultant, Samsung Electronics; NIH Grant support from: U01 CA181156.

60 APPLIED LIFE SCIENCES↗
NASA NTRSDate not supplied

A Dynamic PCA and Machine Learning Tool for Automated Identification of Solar Wind Disturbances Impacting Earth’s Magnetosphere

Earth’s magnetosphere is continuously impacted by solar wind and interplanetary magnetic field (IMF) disturbances, such as shocks, discontinuities, magnetic clouds and more. Understanding how such disturbances propagate from the Sun and what is their impact on the different magnetospheric domains is key to understanding and forecasting energy transfer from the solar wind to Earth. The large number of overlapping solar wind and magnetospheric missions carrying magnetometers and the recent advances in communications and data storage technologies have enabled an unprecedented quantity of high-fidelity magnetic field data captured by in-situ spacecraft to be available at the click of a button. However, this massive quantity of available data can prove unwieldy for researchers, limiting the identification of interesting phenomena and disturbances to a relatively small percentage of the total dataset. Several techniques have been previously developed for automated identification of specific types of magnetic anomalies, but these methods are typically mission-specific and can be difficult to generalize. We present initial results for a generic method of automated anomaly detection in magnetic field measurements based on dimensionality reduction and unsupervised clustering via machine learning. The benefit of our technique is its high degree of generalizability and flexibility which make it a most useful data survey tool for a wide range of magnetic field datasets. This method can also be applied simultaneously to other observed time-series properties like plasma density, pressure, and velocity for more accurate event identification. Additionally, the application of this method to data captured by multiple spacecraft enables the simultaneous identification of disturbances and the determination of their propagation characteristics. Initial evaluation of this technique has been performed using data from Magnetospheric MultiScale (MMS) and THEMIS-ARTEMIS missions, providing a testbed scenario for the future Heliophysics Environmental and Radiation Measurement Experiment Suite (HERMES) platform instruments that will measure solar wind and IMF properties from lunar orbit onboard the Gateway station.

Miguel Martinez-Ledesma↗
NASA NTRSDate not supplied

A Dynamic PCA and Machine Learning Tool for Automated Identification of Solar Wind Disturbances Impacting Earth’s Magnetosphere

Earth’s magnetosphere is continuously impacted by solar wind and interplanetary magnetic field (IMF) disturbances, such as shocks, discontinuities, magnetic clouds and more. Understanding how such disturbances propagate from the Sun and what is their impact on the different magnetospheric domains is key to understanding and forecasting energy transfer from the solar wind to Earth. The large number of overlapping solar wind and magnetospheric missions carrying magnetometers and the recent advances in communications and data storage technologies have enabled an unprecedented quantity of high-fidelity magnetic field data captured by in-situ spacecraft to be available at the click of a button. However, this massive quantity of available data can prove unwieldy for researchers, limiting the identification of interesting phenomena and disturbances to a relatively small percentage of the total dataset. Several techniques have been previously developed for automated identification of specific types of magnetic anomalies, but these methods are typically mission-specific and can be difficult to generalize. We present initial results for a generic method of automated anomaly detection in magnetic field measurements based on dimensionality reduction and unsupervised clustering via machine learning. The benefit of our technique is its high degree of generalizability and flexibility which make it a most useful data survey tool for a wide range of magnetic field datasets. This method can also be applied simultaneously to other observed time-series properties like plasma density, pressure, and velocity for more accurate event identification. Additionally, the application of this method to data captured by multiple spacecraft enables the simultaneous identification of disturbances and the determination of their propagation characteristics. Initial evaluation of this technique has been performed using data from Magnetospheric MultiScale (MMS) and THEMIS-ARTEMIS missions, providing a testbed scenario for the future Heliophysics Environmental and Radiation Measurement Experiment Suite (HERMES) platform instruments that will measure solar wind and IMF properties from lunar orbit onboard the Gateway station.

Miguel Martinez-Ledesma↗
NASA NTRS1995

WISE: Automated support for software project management and measurement

One important aspect of software development and IV&V is measurement. Unless a software development effort is measured in some way, it is difficult to judge the effectiveness of current efforts and predict future performances. Collection of metrics and adherence to a process are difficult tasks in a software project. Change activity is a powerful indicator of project status. Automated systems that can handle change requests, issues, and other process documents provide an excellent platform for tracking the status of the project. A World Wide Web based architecture is developed for (a) making metrics collection an implicit part of the software process, (b) providing metric analysis dynamically, (c) supporting automated tools that can complement current practices of in-process improvement, and (d) overcoming geographical barrier. An operational system (WISE) instantiates this architecture allowing for the improvement of software process in a realistic environment. The tool tracks issues in software development process, provides informal communication between the users with different roles, supports to-do lists (TDL), and helps in software process improvement. WISE minimizes the time devoted to metrics collection, analysis, and captures software change data. Automated tools like WISE focus on understanding and managing the software process. The goal is improvement through measurement.

Ramakrishnan, Sudhakar↗
DOE OSTI2018

An Open-Source Tool for Automated Power Grid Stress Level Prediction at Balancing Authorities

The behavior of modern power systems becomes more stochastic and dynamic, due to the increased penetration of variable generation, demand response, new power market structure, extreme weather conditions, contingencies, and unexpected events. It is critically important to predict potential system operational issues so that grid planners and operators can take preventive actions to mitigate the impact, e.g., lack of operational reserves. In this paper, an innovative software tool is presented to assist power grid operators in a balancing authority (BA) to predict the grid stress level over the next operating day. It periodically collects necessary information from public domain such as weather forecasts, electricity demand, and automatically estimates the stress levels on a daily basis. Advanced Neural Network and regression tree algorithms are developed as the prediction engines to achieve this goal. The tool has been tested on a few key balancing authorities that successfully predicates the growing system peak load and increased stress levels under extreme heat waves in the United States of America.

Berscheid, Alan P.↗
NASA NTRS1988

Automated database design technology and tools

The Automated Database Design Technology and Tools research project results are summarized in this final report. Comments on the state of the art in various aspects of database design are provided, and recommendations made for further research for SNAP and NAVMASSO future database applications.

Shen, Stewart N. T.↗