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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 649 records · Page 36
All sourcesNASA NTRSDOE OSTI
NASA NTRS2017

Characterizing DebriSat Fragments: So Many Fragments, So Much Data, and So Little Time

To improve prediction accuracy, the DebriSat project was conceived by NASA and DoD to update existing standard break-up models. Updating standard break-up models require detailed fragment characteristics such as physical size, material properties, bulk density, and ballistic coefficient. For the DebriSat project, a representative modern LEO spacecraft was developed and subjected to a laboratory hypervelocity impact test and all generated fragments with at least one dimension greater than 2 mm are collected, characterized and archived. Since the beginning of the characterization phase of the DebriSat project, over 130,000 fragments have been collected and approximately 250,000 fragments are expected to be collected in total, a three-fold increase over the 85,000 fragments predicted by the current break-up model. The challenge throughout the project has been to ensure the integrity and accuracy of the characteristics of each fragment. To this end, the post hypervelocity-impact test activities, which include fragment collection, extraction, and characterization, have been designed to minimize handling of the fragments. The procedures for fragment collection, extraction, and characterization were painstakingly designed and implemented to maintain the post-impact state of the fragments, thus ensuring the integrity and accuracy of the characterization data. Each process is designed to expedite the accumulation of data, however, the need for speed is restrained by the need to protect the fragments. Methods to expedite the process such as parallel processing have been explored and implemented while continuing to maintain the highest integrity and value of the data. To minimize fragment handling, automated systems have been developed and implemented. Errors due to human inputs are also minimized by the use of these automated systems. This paper discusses the processes and challenges involved in the collection, extraction, and characterization of the fragments as well as the time required to complete the processes. The objective is to provide the orbital debris community an understanding of the scale of the effort required to generate and archive high quality data and metadata for each debris fragment 2 mm or larger generated by the DebriSat project.

Shiotani, B.↗
NASA NTRS1986

Workload characterization for the space station data communications system

NASA plans to launch a permanent manned space station in the early 1990's. The station will be used to support a wide variety of activities involving Earth and space observation, satellite maintenance, scientific experimentation, and commercial manufacturing. The control and monitoring of many of these activities will require extensive computer and communications system support. In order to identify an appropriate computer and communication system for supporting the space station, an attempt to characterize the space station's data communications subsystem workload is currently underway. Some of the special aspects of the workload characterization problem are discussed in connection with the space station, and some possible approaches are presented.

Sevcik, K. C.↗
DOE OSTI2020

Rapid Identification of X-ray Diffraction Patterns Based on Very Limited Data by Interpretable Convolutional Neural Networks

Large volumes of data from material characterizations call for rapid and automatic data analysis to accelerate materials discovery. Herein, we report a convolutional neural network (CNN) that was trained based on theoretical data and very limited experimental data for fast identification of experimental X-ray diffraction (XRD) patterns of metal–organic frameworks (MOFs). To augment the data for training the model, noise was extracted from experimental data and shuffled; then it was merged with the main peaks that were extracted from theoretical spectra to synthesize new spectra. For the first time, one-to-one material identification was achieved. Theoretical MOFs patterns (1012) were augmented to a whole data set of 72 864 samples. It was then randomly shuffled and split into training (58 292 samples) and validation (14 572 samples) data sets at a ratio of 4:1. For the task of discriminating, the optimized model showed the highest identification accuracy of 96.7% for the top 5 ranking on a test data set of 30 hold-out samples. Neighborhood component analysis (NCA) on the experimental XRD samples shows that the samples from the same material are clustered in groups in the NCA map. Analysis on the class activation maps of the last CNN layer further discloses the mechanism by which the CNN model successfully identifies individual MOFs from the XRD patterns. Furthermore, this CNN model trained by the data augmentation technique would not only open numerous potential applications for identifying XRD patterns for different materials, but also pave avenues to autonomously analyze data by other characterization tools such as FTIR, Raman, and NMR spectroscopies.

36 MATERIALS SCIENCE↗
DOE OSTI2016

Modeling the Spatio-Temporal Variability in Subsurface Thermal Regimes Across a Low-Relief Polygonal Tundra Landscape: Modeling Archive

Vast carbon stocks stored in permafrost soils of Arctic tundra are under risk of release to atmosphere under warming climate. Ice--wedge polygons in the low-gradient polygonal tundra create a complex mosaic of microtopographic features. The microtopography plays a critical role in regulating the fine scale variability in thermal and hydrological regimes in the polygonal tundra landscape underlain by continuous permafrost. Modeling of thermal regimes of this sensitive ecosystem is essential for understanding the landscape behaviour under current as well as changing climate. We present here an end-to-end effort for high resolution numerical modeling of thermal hydrology at real-world field sites, utilizing the best available data to characterize and parameterize the models. We develop approaches to model the thermal hydrology of polygonal tundra and apply them at four study sites at Barrow, Alaska spanning across low to transitional to high-centered polygon and representative of broad polygonal tundra landscape. A multi--phase subsurface thermal hydrology model (PFLOTRAN) was developed and applied to study the thermal regimes at four sites. Using high resolution LiDAR DEM, microtopographic features of the landscape were characterized and represented in the high resolution model mesh. Best available soil data from field observations and literature was utilized to represent the complex heterogeneous subsurface in the numerical model. This data collection provides the complete set of input files, forcing data sets and computational meshes for simulations using PFLOTRAN for four sites at Barrow Environmental Observatory. It also documents the complete computational workflow for this modeling study to allow verification, reproducibility and follow up studies. This dataset includes two zipped files and one .pdf file.The Next-Generation Ecosystem Experiments: Arctic (NGEE Arctic), was a research effort to reduce uncertainty in Earth System Models by developing a predictive understanding of carbon-rich Arctic ecosystems and feedbacks to climate. NGEE Arctic was supported by the Department of Energy's Office of Biological and Environmental Research.The NGEE Arctic project had two field research sites: 1) located within the Arctic polygonal tundra coastal region on the Barrow Environmental Observatory (BEO) and the North Slope near Utqiagvik (Barrow), Alaska and 2) multiple areas on the discontinuous permafrost region of the Seward Peninsula north of Nome, Alaska.Through observations, experiments, and synthesis with existing datasets, NGEE Arctic provided an enhanced knowledge base for multi-scale modeling and contributed to improved process representation at global pan-Arctic scales within the Department of Energy's Earth system Model (the Energy Exascale Earth System Model, or E3SM), and specifically within the E3SM Land Model component (ELM).

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS1995

Linear and nonlinear trending and prediction for AVHRR time series data

The variability of AVHRR calibration coefficient in time was analyzed using algorithms of linear and non-linear time series analysis. Specifically we have used the spline trend modeling, autoregressive process analysis, incremental neural network learning algorithm and redundancy functional testing. The analysis performed on available AVHRR data sets revealed that (1) the calibration data have nonlinear dependencies, (2) the calibration data depend strongly on the target temperature, (3) both calibration coefficients and the temperature time series can be modeled, in the first approximation, as autonomous dynamical systems, (4) the high frequency residuals of the analyzed data sets can be best modeled as an autoregressive process of the 10th degree. We have dealt with a nonlinear identification problem and the problem of noise filtering (data smoothing). The system identification and filtering are significant problems for AVHRR data sets. The algorithms outlined in this study can be used for the future EOS missions. Prediction and smoothing algorithms for time series of calibration data provide a functional characterization of the data. Those algorithms can be particularly useful when calibration data are incomplete or sparse.

Smid, J.↗
NASA NTRS2004

Using Fuzzy Clustering for Real-time Space Flight Safety

To ensure space flight safety, it is necessary to monitor myriad sensor readings on the ground and in flight. Since a space shuttle has many sensors, monitoring data and drawing conclusions from information contained within the data in real time is challenging. The nature of the information can be critical to the success of the mission and safety of the crew and therefore, must be processed with minimal data-processing time. Data analysis algorithms could be used to synthesize sensor readings and compare data associated with normal operation with the data obtained that contain fault patterns to draw conclusions. Detecting abnormal operation during early stages in the transition from safe to unsafe operation requires a large amount of historical data that can be categorized into different classes (non-risk, risk). Even though the 40 years of shuttle flight program has accumulated volumes of historical data, these data don t comprehensively represent all possible fault patterns since fault patterns are usually unknown before the fault occurs. This paper presents a method that uses a similarity measure between fuzzy clusters to detect possible faults in real time. A clustering technique based on a fuzzy equivalence relation is used to characterize temporal data. Data collected during an initial time period are separated into clusters. These clusters are characterized by their centroids. Clusters formed during subsequent time periods are either merged with an existing cluster or added to the cluster list. The resulting list of cluster centroids, called a cluster group, characterizes the behavior of a particular set of temporal data. The degree to which new clusters formed in a subsequent time period are similar to the cluster group is characterized by a similarity measure, q. This method is applied to downlink data from Columbia flights. The results show that this technique can detect an unexpected fault that has not been present in the training data set.

Lee, Charles↗
NASA NTRS2002

Characterization of MER Landing Sites Using MOC and MOLA

The MOC images for MER are compared with MOLA data to characterize and locate each image. MOLA profiles show that Hematite remains benign, Melas and Isidis are rougher, and Athabasca and Gusev have regions of significant small scale topography. Additional information is contained in the original extended abstract.

Anderson, F. S.↗
NASA NTRS2009

General Purpose Data-Driven Monitoring for Space Operations

As modern space propulsion and exploration systems improve in capability and efficiency, their designs are becoming increasingly sophisticated and complex. Determining the health state of these systems, using traditional parameter limit checking, model-based, or rule-based methods, is becoming more difficult as the number of sensors and component interactions grow. Data-driven monitoring techniques have been developed to address these issues by analyzing system operations data to automatically characterize normal system behavior. System health can be monitored by comparing real-time operating data with these nominal characterizations, providing detection of anomalous data signatures indicative of system faults or failures. The Inductive Monitoring System (IMS) is a data-driven system health monitoring software tool that has been successfully applied to several aerospace applications. IMS uses a data mining technique called clustering to analyze archived system data and characterize normal interactions between parameters. The scope of IMS based data-driven monitoring applications continues to expand with current development activities. Successful IMS deployment in the International Space Station (ISS) flight control room to monitor ISS attitude control systems has led to applications in other ISS flight control disciplines, such as thermal control. It has also generated interest in data-driven monitoring capability for Constellation, NASA's program to replace the Space Shuttle with new launch vehicles and spacecraft capable of returning astronauts to the moon, and then on to Mars. Several projects are currently underway to evaluate and mature the IMS technology and complementary tools for use in the Constellation program. These include an experiment on board the Air Force TacSat-3 satellite, and ground systems monitoring for NASA's Ares I-X and Ares I launch vehicles. The TacSat-3 Vehicle System Management (TVSM) project is a software experiment to integrate fault and anomaly detection algorithms and diagnosis tools with executive and adaptive planning functions contained in the flight software on-board the Air Force Research Laboratory TacSat-3 satellite. The TVSM software package will be uploaded after launch to monitor spacecraft subsystems such as power and guidance, navigation, and control (GN&C). It will analyze data in real-time to demonstrate detection of faults and unusual conditions, diagnose problems, and react to threats to spacecraft health and mission goals. The experiment will demonstrate the feasibility and effectiveness of integrated system health management (ISHM) technologies with both ground and on-board experiments.

Iverson, David L.↗
DOE OSTI2013

Clean Cities 2012 Annual Metrics Report

The U.S. Department of Energy's (DOE) Clean Cities program advances the nation's economic, environmental, and energy security by supporting local actions to cut petroleum use in transportation. A national network of nearly 100 Clean Cities coalitions brings together stakeholders in the public and private sectors to deploy alternative and renewable fuels, idle-reduction measures, fuel economy improvements, and new transportation technologies, as they emerge. Each year DOE asks Clean Cities coordinators to submit annual reports of their activities and accomplishments for the previous calendar year. Data and information are submitted via an online database that is maintained as part of the Alternative Fuels Data Center (AFDC) at the National Renewable Energy Laboratory (NREL). Coordinators submit a range of data that characterizes the membership, funding, projects, and activities of their coalitions. They also submit data about sales of alternative fuels, deployment of alternative fuel vehicles (AFVs) and hybrid electric vehicles (HEVs), idle-reduction initiatives, fuel economy activities, and programs to reduce vehicle miles traveled (VMT). NREL analyzes the data and translates them into petroleum-use reduction impacts, which are summarized in this report.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2014

Clean Cities 2013 Annual Metrics Report

Each year, the U.S. Department of Energy asks its Clean Cities program coordinators to submit annual reports of their activities and accomplishments for the previous calendar year. Data and information are submitted via an online database that is maintained as part of the Alternative Fuels Data Center (AFDC) at the National Renewable Energy Laboratory (NREL). Coordinators submit a range of data that characterize the membership, funding, projects, and activities of their coalitions. They also submit data about sales of alternative fuels, deployment of alternative fuel vehicles (AFVs) and hybrid electric vehicles (HEVs), idle-reduction (IR) initiatives, fuel economy activities, and programs to reduce vehicle miles traveled (VMT). NREL analyzes the data and translates them into petroleum-use reduction impacts, which are summarized in this 2013 Annual Metrics Report.

2013 DATA↗
DOE OSTI2015

Clean Cities 2014 Annual Metrics Report

Each year, the U.S. Department of Energy asks its Clean Cities program coordinators to submit annual reports of their activities and accomplishments for the previous calendar year. Data and information are submitted via an online database that is maintained as part of the Alternative Fuels Data Center (AFDC) at the National Renewable Energy Laboratory (NREL). Coordinators submit a range of data that characterize the membership, funding, projects, and activities of their coalitions. They also submit data about sales of alternative fuels, deployment of alternative fuel vehicles (AFVs) and hybrid electric vehicles (HEVs), idle-reduction (IR) initiatives, fuel economy activities, and programs to reduce vehicle miles traveled (VMT). NREL analyzes the data and translates them into petroleum-use reduction impacts, which are summarized in this 2014 Annual Metrics Report.

2014 data↗
DOE OSTI2018

Clean Cities Coalitions: 2016 Activity Report

The U.S. Department of Energy's (DOE's) national network of Clean Cities Coalitions advance the nation's economic, environmental, and energy security by supporting local actions to promote the use of domestic fuels within transportation. The nearly 100 Clean Cities coalitions, whose territory covers 80% of the U.S. population, bring together stakeholders in the public and private sectors to use alternative and renewable fuels, idle-reduction (IR) measures, fuel economy improvements, and new transportation technologies as they emerge. To ensure success, coalitions leverage a robust set of expert resources and tools provided by national laboratories and DOE. Each year, Clean Cities coordinators submit annual reports of their activities and accomplishments for the previous calendar year. Data and information are submitted via an online tool that is maintained as part of the Alternative Fuels Data Center (AFDC) at the National Renewable Energy Laboratory (NREL). Coordinators submit a range of data that characterize the membership, funding, projects, and activities of their coalitions. They also submit data about sales of alternative fuels; use of alternative fuel vehicles (AFVs), plug-in electric vehicles (PEVs), and hybrid electric vehicles (HEVs); IR initiatives; fuel economy improvement activities; and programs to reduce vehicle miles traveled (VMT). NREL analyzes the submitted data to determine how broadly energy use in the U.S. has shifted due to coalition activities, which are summarized in this report.

2016 metrics↗
NASA NTRS1976

Soft X-ray observation experiment MA-048

The Apollo Soyuz Test Project Soft X-ray Experiment was designed to observe celestial X-ray sources in the energy range from 0.1 to 10 kiloelectronvolts. The instrument that was used in the experiment obtained energy and fast timing data to characterize both the spectrum and the variability of known X-ray sources. Data were obtained on approximately 12 sources. During the mission, the instrument developed an intermittent high voltage discharge problem that resulted in the loss of approximately 75 percent of the anticipated data, including the scans intended for mapping of the low energy diffuse X-ray background.

Shulman, S.↗
NASA NTRS1988

Simplified half-life methods for the analysis of kinetic data

The analysis of reaction rate data has as its goal the determination of the order rate constant which characterize the data. Chemical reactions with one reactant and present simplified methods for accomplishing this goal are considered. The approaches presented involve the use of half lives or other fractional lives. These methods are particularly useful for the more elementary discussions of kinetics found in general and physical chemistry courses.

Eberhart, J. G.↗
NASA NTRS2021

Volatiles Investigating Polar Exploration Rover (VIPER)

VIPER is a lunar volatiles detection and measurement mission that will be launched as a payload on the CLPS (Commercial Lunar Payload Services) provided Astrobotic's Griffin lander to the lunar south polar region. VIPER includes a suite of rover-mounted instruments that will conduct science and map volatiles (especially hydrogen-bearing volatiles). The VIPER rover is also designed to excavate volatiles such as hydrogen, oxygen, and water from the Moon. After landing the VIPER rover will travel to investigate a range of Ice Stability Regions (ISRs) across scales from 100s of meters to kilometers and conduct surface and subsurface assessment of lunar water and other volatiles. The VIPER science mission team will use the instrument data to characterize the nature of the volatiles in the area and to extrapolate these data to create global lunar water resource maps. The expected lunar surface mission duration is up to four lunar days, with active surface operations during the periods when both Sun exposure and direct to Earth (DTE) communication conditions overlap. When comm and Sun are not both available, VIPER will go into ‘Safe Haven operations’ and maintain survival temperatures until Sun and comm return. The rover is controlled in near-real time and science decisions are made both tactically (short-term) and strategically (longer-term) to achieve the mission science success criteria and objectives.

Anthony Colaprete↗
NASA NTRSDate not supplied

On Characterization of Laminar to Turbulent Transitional Flow in the Truncated IHF Semi-Elliptical Nozzle

This paper reports computational analysis and experimental data to characterize the laminar to turbulent flow transition in a high-enthalpy panel test facility at NASA Ames Research Center. The recently developed facility configuration uses a truncated version of an existing semi-elliptical nozzle in the 60-MW Interaction Heating Facility, and it provides testing capability for panel test articles as large as 43.2 cm x 43.2 cm (17 in x 17 in). A water-cooled calibration plate, which is attached to the semi-elliptical nozzle bottom surface and instrumented with twenty Gardon-gage heat flux calorimeters and twelve pressure gages, is used to characterize the convective heating environment over the panel test articles. The present analysis comprises computational fluid dynamics simulations of the nonequilibrium flowfield in the facility nozzle and test box, and the flowfields over panel test articles, and comparisons with the measured test data. Both laminar and turbulent simulations are performed. Constant current studies are conducted: the measured data are grouped at the same arc current levels with varying mass flow rates, and they are compared with corresponding computations. For all currents considered, these studies indicate that the semi-elliptical nozzle flow is transitional at relatively higher mass flow rates within the facility operating envelope. An approximate facility map for the transitional flow characterization is developed, showing laminar and transitional to turbulent flow regions.

Arc-jets↗
DOE OSTI2026

Uinta Basin CarbonSAFE II: Storage Complex Feasibility (Final Report)

The primary objective of this CarbonSAFE Phase II project was to establish the technical and commercial feasibility of a commercial-scale CO 2 geological storage complex for Deseret Power Electric Cooperative Bonanza Power Plant and other CO 2 sources in the northeast Uinta Basin, Utah, with the goal to securely store at least 50 million metric tons of captured CO 2 and accelerate CO 2 capture, utilization, and storage (CCUS) deployment. The project team established high-potential technical and commercial feasibility for a storage site within the east Uinta Basin (Utah), in the Cretaceous sandstones (Frontier, Dakota, and Buckhorn), Entrada Sandstone, Nugget Sandstone, and/or Weber Sandstone southwest of the Bonanza coal-fired power plant. This project collected and analyzed state-of-the-art data to characterize the storage complex consistent with Environmental Protection Agency (EPA) permitting standards. The team conducted extensive analog studies, outcrop mapping, and data sampling, which largely contributed to understanding the subsurface lithology and facies. Existing data were obtained and assessed from Utah Division of Oil, Gas, and Mining (DOGM), Utah Geological Survey (UGS), Colorado Geological Survey (CGS), U.S. Geological Survey (USGS), and EPA. These data were analyzed using state-of-the-art CCUS technologies for Societal Considerations, Site Characterization, Modeling and Simulations, Risk Assessment, Management and Monitoring, potential Underground Injection Control (UIC) Class VI Well Permitting, and Technical/Economic Feasibility. Through these high-resolution data collection and feasibility studies, this project was expected to provide a reference for initiating Underground Injection Control (UIC) and other commercial-scale geological storage permitting processes in the Western United States, ultimately contributing to the nation's decarbonization goals through low-risk, cost-effective commercial-scale carbon capture, utilization, and storage (CCUS) projects.

42 ENGINEERING↗