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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 199 records · Page 11
All sourcesNASA NTRSDOE OSTI
NASA NTRS2014

Maintaining Aura's Orbit Requirements While Performing Orbit Maintenance Maneuvers Containing an Orbit Normal Delta-V Component

The Earth Observing System (EOS) Afternoon Constellation consists of five member missions (GCOM-W1, Aqua, CALIPSO, CloudSat, and Aura), each of which maintain a frozen, sun-synchronous orbit with a 16-day repeating ground track that follows the Worldwide Reference System-2 (WRS-2). Under nominal science operations for Aura, the propulsion system is oriented such that the resultant thrust vector is aligned 13.493 degrees away from the velocity vector along the yaw axis. When performing orbit maintenance maneuvers, the spacecraft performs a yaw slew to align the thrust vector in the appropriate direction. A new Drag Make Up (DMU) maneuver operations scheme has been implemented for Aura alleviating the need for the 13.493 degree yaw slew. The focus of this investigation is to assess the impact that no-slew DMU maneuver operations will have on Aura's Mean Local Time (MLT) which drives the required along track separation between Aura and the constellation members, as well as Aura's frozen orbit properties, eccentricity and argument of perigee. Seven maneuver strategies were analyzed to determine the best operational approach. A mirror pole strategy, with maneuvers alternating at the North and South poles, was implemented operationally to minimize impact to the MLT. Additional analysis determined that the mirror pole strategy could be further modified to include frozen orbit maneuvers and thus maintain both MLT and the frozen orbit properties under noslew operations.

Aqua↗
NASA NTRS2015

Florida Atlantic Coast Telemetry (FACT) Array: A Working Partnership

The Florida Atlantic Coast Telemetry (FACT) Array is a collaborative partnership of researchers from 24 different organizations using passive acoustic telemetry to document site fidelity, habitat preferences, seasonal migration patterns, and reproductive strategies of valuable sportfish, sharks, and marine turtles. FACT partners have found that by bundling resources, they can leverage a smaller investment to track highly mobile animals beyond a study area typically restrained in scale by funds and manpower. FACT is guided by several simple rules: use of the same type of equipment, locate receivers in areas that are beneficial to all researchers when feasible, maintain strong scientific ethics by recognizing that detection data on any receiver belongs to the tag owner, do not use other members detection data without permission and acknowledge FACT in publications. Partners have access to a network of 480 receivers deployed along a continuum of habitats from freshwater rivers to offshore reefs and covers 1100 km of coastline from the Dry Tortugas, Florida to South Carolina and extends to the Bahamas. Presently, 49 species, (25 covered by Fisheries Management Plans and five covered by the Endangered Species Act) have been tagged with 2736 tags in which 1767 tags are still active.

Scheidt, Douglas↗
NASA NTRSDate not supplied

Supporting Global Air Quality Management Needs With A Flexible Data Fusion Tool for Estimation and Forecasting in Google Earth Engine

High spatial and temporal resolution air quality estimation and forecasting can be enhanced by combining global data sources, like chemical transport models and satellite remote sensing, with local information from regulatory and low-cost air quality monitors. Successful integration of data from these diverse sources is complicated by many factors, however, including differences in spatial and temporal resolution, data availability and latency issues, varying data quality, and large computational and data storage requirements. This presentation will provide an overview of a NASA-funded effort to develop the foundation for future operationalization of air quality forecasting for world-wide end-users and integration into their air quality management decision processes, which will be achieved in future phases of this multi-year project. We will summarize our progress in developing a data fusion system using the Google Earth Engine platform which can integrate model, satellite, and surface-level monitoring datasets to enhance estimation and forecasting of air-quality-relevant pollutants at sub-daily and sub-city scales. The tool is being developed in close cooperation with several city- and regional-level air quality managers in the USA and around the world. Our end-goal is to provide these air quality managers with the information they need to assess and anticipate the impacts of poor air quality, track changes in air quality due to ongoing mitigation efforts and land use changes, and identify ways to improve their air quality monitoring strategies. This presentation will focus on recent advances achieved through the project, including integration of multiple air quality datasets in a prototype data fusion system in Google Earth Engine, the quantification of uncertainties associated with our data fusion approach, and the development of user interfaces and visualization tools to convey air quality information in a way which best meets end-user needs.

Carl Malings↗
NASA NTRSDate not supplied

Supporting Global Air Quality Management Needs With A Flexible Data Fusion Tool for Estimation and Forecasting in Google Earth Engine

High spatial and temporal resolution air quality estimation and forecasting can be enhanced by combining global data sources, like chemical transport models and satellite remote sensing, with local information from regulatory and low-cost air quality monitors. Successful integration of data from these diverse sources is complicated by many factors, however, including differences in spatial and temporal resolution, data availability and latency issues, varying data quality, and large computational and data storage requirements. This presentation will provide an overview of a NASA-funded effort to develop the foundation for future operationalization of air quality forecasting for world-wide end-users and integration into their air quality management decision processes, which will be achieved in future phases of this multi-year project. We will summarize our progress in developing a data fusion system using the Google Earth Engine platform which can integrate model, satellite, and surface-level monitoring datasets to enhance estimation and forecasting of air-quality-relevant pollutants at sub-daily and sub-city scales. The tool is being developed in close cooperation with several city- and regional-level air quality managers in the USA and around the world. Our end-goal is to provide these air quality managers with the information they need to assess and anticipate the impacts of poor air quality, track changes in air quality due to ongoing mitigation efforts and land use changes, and identify ways to improve their air quality monitoring strategies. This presentation will focus on recent advances achieved through the project, including integration of multiple air quality datasets in a prototype data fusion system in Google Earth Engine, the quantification of uncertainties associated with our data fusion approach, and the development of user interfaces and visualization tools to convey air quality information in a way which best meets end-user needs.

Carl Malings↗
NASA NTRS2026

The Habitable Worlds Observatory Technology Development Plan

The Habitable Worlds Observatory (HWO) is NASA’s next large space telescope, selected by the 2020 Decadal Survey in Astronomy and Astrophysics to search for and characterize habitable exoplanets while enabling a broad range of transformative astrophysics. In August 2024, the HWO Technology Maturation Project Office (TMPO) was formed to begin exploring the HWO science, technology, and mission architectures toward a Mission Concept Review (MCR) at the end of the decade. A primary deliverable of this effort is this technology development plan that identifies critical technologies that enable the mission, defines a process for assessing the readiness of those technologies, and outlines a strategy for developing those technologies to a Technology Readiness Level (TRL) of 5 before the MCR. This document covers technologies organized along three “tracks”: Coronagraph System technologies, Ultra-stable Telescope System technologies, and High-sensitivity Ultraviolet and Visible Instrumentation technologies. Additional emerging and enhancing technologies are also discussed.

technology development↗
NASA NTRS1991

Power optimal single-axis articulating strategies

Power optimal single axis articulating PV array motion for Space Station Freedom is investigated. The motivation is to eliminate one of the articular joints to reduce Station costs. Optimal (maximum power) Beta tracking is addressed for local vertical local horizontal (LVLH) and non-LVLH attitudes. Effects of intra-array shadowing are also presented. Maximum power availability while Beta tracking is compared to full sun tracking and optimal alpha tracking. The results are quantified in orbital and yearly minimum, maximum, and average values of power availability.

Kumar, Renjith R.↗
NASA NTRS2023

Flexible Data Fusion for Air Quality Estimation and Forecasting in Google Earth Engine to support Global Health Management Needs

The assessment and forecasting of air quality around the world at high spatial and temporal resolution can be enhanced by integrating data from multiple sources including models, satellites, regulatory monitors, and low-cost sensors. Such integration is subject to numerous technical challenges, however, including heterogeneous data resolution and formatting, different levels of data availability and reliability, and computational and capacity challenges to developing data fusion tools and platforms. This presentation will provide an overview of a NASA-funded effort to develop a data fusion system within the Google Earth Engine platform which integrates these air quality data sources to produce comprehensive assessments and forecasts of key air pollutants at sub-daily and sub-city scales. The system is being developed in collaboration with city- and regional-level air quality managers, and will provide them with information to the assess and anticipate the health impacts of poor air quality, track local changes in air quality due to ongoing transportation and land use changes, and identify potential gaps in their current air quality monitoring strategies. The presentation will report advances achieved through the project, including bringing local air quality monitoring data into Google Earth Engine, quantifying uncertainties in air quality estimates and forecasts, and tailored communications tools providing integration into end-user processes to meet their needs.

Carl Malings↗
NASA NTRSDate not supplied

Flexible Data Fusion for Air Quality Estimation and Forecasting in Google Earth Engine to support Global Health Management Needs

The assessment and forecasting of air quality around the world at high spatial and temporal resolution can be enhanced by integrating data from multiple sources including models, satellites, regulatory monitors, and low-cost sensors. Such integration is subject to numerous technical challenges, however, including heterogeneous data resolution and formatting, different levels of data availability and reliability, and computational and capacity challenges to developing data fusion tools and platforms. This presentation will provide an overview of a NASA-funded effort to develop a data fusion system within the Google Earth Engine platform which integrates these air quality data sources to produce comprehensive assessments and forecasts of key air pollutants at sub-daily and sub-city scales. The system is being developed in collaboration with city- and regional-level air quality managers and will provide them with information to the assess and anticipate the health impacts of poor air quality, track local changes in air quality due to ongoing transportation and land use changes, and identify potential gaps in their current air quality monitoring strategies. The presentation will report advances achieved through the project, including bringing local air quality monitoring data into Google Earth Engine, quantifying uncertainties in air quality estimates and forecasts, and tailored communications tools providing integration into end-user processes to meet their needs.

Nathan R. Pavlovic↗
NASA NTRSDate not supplied

Automated Tracking of Shallow Maritime Clouds on Geostationary Imagery to Extract Lifecycle Characteristics

Shallow moist convection is ubiquitous throughout the tropics and represents a key player in boundary layer processes. Satellites have provided valuable insight on shallow clouds, such as size, structure, and geographical coverage, from static views of recurring cloud fields. But determining why certain cloud features appear and persist for different periods requires a time-evolving view of their behaviors. Geostationary satellites provide a unique opportunity to follow the time evolution of individual convective features, given their enhanced spatial and temporal sampling. A cloud-tracking tool was developed to identify properties of cloud lifecycle from the NASA Cloud, Aerosol, and Monsoon Processes Philippines Experiment (CAMP2EX) field campaign of 2019. The mission conducted intensive sampling of shallow cumulus in the West Pacific Ocean, in tandem with Rapid Scan imagery from the Advanced Himawari Imager (AHI) on the Japan Meteorological Agency’s (JMA) Himawari-8 satellite. Shallow cumulus were segmented according to thresholds in 0.5-km visible reflectance and with blurring techniques. Despite being limited to daytime hours, the segmentations yielded the best resolution available for capturing cloud initiation, growth, and decay. The tracking procedure is based on a computer vision package that includes Kalman filters for motion prediction, object overlap search, and the Hungarian (or Kuhn-Munkres) matching algorithm for track designation. AHI radiances available within the tracked cloud boundaries are assembled to form individual spectral histories. The resulting catalog provides thousands of cloud histories for domains measuring only a few degrees in latitude and longitude. We present an overview of the cloud-tracking tool and its results for cloud fields sampled throughout CAMP2EX by the airborne P-3. Cloud tracks were selected from about 10 flights to form ensembles, specific groups of tracks occurring in a region with airborne sampling. Cloud lifecycle properties, including duration and maximum area, are calculated for all ensemble members, and analyzed for cloud behavior and P-3 coincidences. By following this strategy, we quantitatively assess the degree of airborne sampling for specific cloud classes defined by the lifecycle calculations. We can summarize which cloud classes had more sampling, the stage of development during sampling, and general differences in character (e.g., isolated congestus vs. cold-pool producer).

Cloud Tracking↗
NASA NTRS1984

Model estimation and identification of manual controller objectives in complex tracking tasks

A methodology is presented for estimating the parameters in an optimal control structural model of the manual controller from experimental data on complex, multiinput/multioutput tracking tasks. Special attention is devoted to estimating the appropriate objective function for the task, as this is considered key in understanding the objectives and strategy of the manual controller. The technique is applied to data from single input/single output as well as multi input/multi outpuut experiments, and results discussed.

Schmidt, D. K.↗
NASA NTRS1992

An experimental evaluation and comparison of explicit force control strategies for robotic manipulator

The authors present an experimental evaluation and comparison of basic strategies that have been proposed for force control of robot manipulators. This experimental review of force control methodologies is unique in its breadth. The commonality amongst the experiments has permitted the ability to objectively compare and contrast these strategies, and draw conclusions about the efficacy of each. The results support previous analysis and show the superiority of integral force control for force trajectory tracking.

Volpe, Richard↗
NASA NTRS2002

Recent Results from CHAMP Tracking and Accelerometer Data Analysis

The CHAMP mission's unique combination of sensors and orbit configuration will enable unprecedented improvements in modeling and understanding the Earth's static gravity field and its temporal variations. CHAMP is the first of two missions (GRACE to be launched in the early part of 02') that combine a new generation of Global Positioning System (GPS) receivers, a high precision three-axis accelerometer, and star cameras for the precision attitude determination. In order to isolate the gravity signal for science investigations, it is necessary to perform a detailed reduction and analysis of the GPS and Satellite Laser Ranging (SLR) tracking data in conjunction with the accelerometer and attitude data. Precision orbit determination based on the GPS and SLR tracking data will isolate the orbit perturbations, while the accelerometer data will be used to distinguish the non-gravitational forces from those due to the geopotential (static, and time varying). In preparation for the CHAMP and GRACE missions, extensive modifications have been made to NASA/GSFC's GEODYN orbit determination software to enable the simultaneous reduction of spacecraft tracking (e.g. GPS and SLR), three-axis accelerometer and precise attitude data. Several weeks of CHAMP tracking and accelerometer data have been analyzed and the results will be presented. Precision orbit determination analysis based on tracking data alone in addition to results based on the simultaneous reduction of tracking and accelerometer data will be discussed. Results from a calibration of the accelerometer will be presented along with the results from various orbit determination strategies.

Luthcke, S. B.↗
NASA NTRS2002

CHAMP Tracking and Accelerometer Data Analysis Results

The CHAMP (Challenging Minisatellite Payload) mission's unique combination of sensors and orbit configuration will enable unprecedented improvements in modeling and understanding the Earth's static gravity field and its temporal variations. CHAMP is the first of two missions (GRACE (Gravity Recovery and Climate Experiment) to be launched in the later part of '01) that combine a new generation of GPS (Global Positioning System) receivers, a high precision three axis accelerometer, and star cameras for the precision attitude determination. In order to isolate the gravity signal for science investigations, it is necessary to perform a detailed reduction and analysis of the GPS and SLR tracking data in conjunction with the accelerometer and attitude data. Precision orbit determination based on the GPS and SLR (Satellite Laser Ranging) tracking data will isolate the orbit perturbations, while the accelerometer data will be used to distinguish the surface forces from those due to the geopotential (static, and time varying). In preparation for the CHAMP and GRACE missions, extensive modifications have been made to NASA/GSFC's GEODYN orbit determination software to enable the simultaneous reduction of spacecraft tracking (e.g. GPS and SLR), three axis accelerometer and precise attitude data. Several weeks of CHAMP tracking and accelerometer data have been analyzed and the results will be presented. Precision orbit determination analysis based on tracking data alone in addition to results based on the simultaneous reduction of tracking and accelerometer data will be discussed. Results from a calibration of the accelerometer will be presented along with the results from various orbit determination strategies. Gravity field modeling status and plans will be discussed.

Lemoine, Frank G.↗
NASA NTRS2011

Mars Sample Return - Launch and Detection Strategies for Orbital Rendezvous

This study sets forth conceptual mission design strategies for the ascent and rendezvous phase of the proposed NASA/ESA joint Mars Sample Return Campaign. The current notional mission architecture calls for the launch of an acquisition/cache rover in 2018, an orbiter with an Earth return vehicle in 2022, and a fetch rover and ascent vehicle in 2024. Strategies are presented to launch the sample into a coplanar orbit with the Orbiter which facilitate robust optical detection, orbit determination, and rendezvous. Repeating ground track orbits exist at 457 and 572 km which provide multiple launch opportunities with similar geometries for detection and rendezvous.

Orbiter's trans-Earth injection (TEI):↗
NASA NTRS2022

Near-Earth Object Surveyor Overview

The Near-Earth Object (NEO) Surveyor is designed to detect, categorize and characterize Near-Earth Objects (NEOs) using infrared imaging. The project was approved to enter the preliminary design phase (Phase B) in FY21 after an extended Concept Development Phase (Phase A). The NEO Surveyor project responds to US Public Law 109-155[1], National Research Council’s report “Defending Planet Earth: Near-Earth Object Surveys & Hazard Mitigation Strategies (2010)”[2], the U. S. National Near-Earth Object Preparedness Strategy and Action Plan (June 2018)[3], and the objectives of NASA’s Planetary Defense Coordination Office (PDCO). The goals of the NEO Surveyor project are to: (1) identify impact hazards to the Earth posed by NEOs (defined as asteroids and comets that come within 1.3 AU of the Sun) by performing a comprehensive survey of the NEO population; (2) obtain detailed physical characterization data for individual objects that are likely to pose an impact hazard; (3) characterize the entire population of potentially hazardous NEOs to inform potential mitigation strategies. The mission will make significant progress toward the George E. Brown, Jr. NEO Survey Program objective defined by the U. S. Congress of detecting, tracking, cataloging, and characterizing at least 90% of NEOs equal to or larger than 140 m in diameter. The project is a collaboration between NASA-JPL, the University of Arizona (UA) and industry, with Ball Aerospace notably providing the spacecraft and key instrument elements. This paper will describe the overall NEO Surveyor Project objectives, initial spacecraft and instrument design and development plans and mission concept.

Veto, Michael↗
NASA NTRS2012

On-orbit Performance of MODIS Solar Diffuser Stability Monitor

MODIS has 20 reflective solar bands (RSBs) in the visible (VIS), near-infrared (NIR) , and shortwave infrared (SWIR) spectral regions. MODIS RSBs are calibrated on-orbit by a solar diffuser (SD) with its on-orbit degradation tracked by a solar diffuser stability monitor (SDSM). The SDSM is an instrument by itself, consisting of a solar integration sphere (SIS) with 9 individual filtered detectors covering wavelengths from 0.41 to 0.94 micrometers. It functions as a ratioing radiometer with alternate observations made of the direct sunlight (Sun view) and that diffusely reflected from the SD panel (SD view) during each scheduled SD/SDSM calibration event. Since launch, Terra and Aqua MODIS and their SDSM have successfully operated for more than 12 and 10 years, respectively. The VIIRS, on-board the newly launched Suomi National Polar orbiting Partnership (NPP) spacecraft, has been designed and built with strong MODIS heritage and carries a similar and improved SD and SDSM calibration system. This paper provides an overview of SDSM design functions, its on-orbit calibration strategies and performance for both Terra and Aqua MODIS missions. Discussions of its on-orbit performance are focused on changes in SDSM detector responses over time and their impact on tracking SD on-orbit degradation. Also presented in this paper are lessons learned from MODIS SDSM and improvements made for the VIIRS SDSM, and preliminary comparisons of their on-orbit performance.

Xiong, Xiaoxiong↗
NASA NTRS1991

Operator strategies under varying conditions of workload

An attempt was made to operationally define and measure strategic behavior in a complex multiple task environment. The Multi-Attribute Task battery was developed to simulate various aspects of flight and consisted of an auditory communication task, monitoring tasks, a tracking tasks, a resource management task which allowed a wide range of responding patterns, and a scheduling window which allowed operators to predict changes in workload. This battery was validated for its sensitivity to strategic behavior, and baseline measures for each individual task were collected. Twenty-four undergraduate and graduate students then performed the battery for four 64 minute sessions which took place over a period of 2 days. Each subject performed the task battery under four levels of workload, which were presented for equal lengths of time during all four sessions. Results indicated that in general, performance improves as a function of experience with the battery, but that performance decreased as workload level increased. The data also showed that subjects developed strategies for responding to the resource management task which allowed them to manage the high workload levels more efficiently. This particular strategy developed over time but was also associated with errors of complacency. These results are presented along with implications for the aviation field and areas of future research.

Arnegard, Ruth J.↗
NASA NTRS1973

Study to design and develop remote manipulator system

Human performance measurement techniques for remote manipulation tasks and remote sensing techniques for manipulators are described for common manipulation tasks, performance is monitored by means of an on-line computer capable of measuring the joint angles of both master and slave arms as a function of time. The computer programs allow measurements of the operator's strategy and physical quantities such as task time and power consumed. The results are printed out after a test run to compare different experimental conditions. For tracking tasks, we describe a method of displaying errors in three dimensions and measuring the end-effector position in three dimensions.

Hill, J. W.↗