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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 469 records · Page 26
All sourcesNASA NTRSDOE OSTI
NASA NTRS2008

Facilitating Analysis of Multiple Partial Data Streams

Robotic Operations Automation: Mechanisms, Imaging, Navigation report Generation (ROAMING) is a set of computer programs that facilitates and accelerates both tactical and strategic analysis of time-sampled data especially the disparate and often incomplete streams of Mars Explorer Rover (MER) telemetry data described in the immediately preceding article. As used here, tactical refers to the activities over a relatively short time (one Martian day in the original MER application) and strategic refers to a longer time (the entire multi-year MER missions in the original application). Prior to installation, ROAMING must be configured with the types of data of interest, and parsers must be modified to understand the format of the input data (many example parsers are provided, including for general CSV files). Thereafter, new data from multiple disparate sources are automatically resampled into a single common annotated spreadsheet stored in a readable space-separated format, and these data can be processed or plotted at any time scale. Such processing or plotting makes it possible to study not only the details of a particular activity spanning only a few seconds, but also longer-term trends. ROAMING makes it possible to generate mission-wide plots of multiple engineering quantities [e.g., vehicle tilt as in Figure 1(a), motor current, numbers of images] that, heretofore could be found only in thousands of separate files. ROAMING also supports automatic annotation of both images and graphs. In the MER application, labels given to terrain features by rover scientists and engineers are automatically plotted in all received images based on their associated camera models (see Figure 2), times measured in seconds are mapped to Mars local time, and command names or arbitrary time-labeled events can be used to label engineering plots, as in Figure 1(b).

Maimone, Mark W.↗
NASA NTRS2004

Viewing ISS Data in Real Time via the Internet

EZStream is a computer program that enables authorized users at diverse terrestrial locations to view, in real time, data generated by scientific payloads aboard the International Space Station (ISS). The only computation/communication resource needed for use of EZStream is a computer equipped with standard Web-browser software and a connection to the Internet. EZStream runs in conjunction with the TReK software, described in a prior NASA Tech Briefs article, that coordinates multiple streams of data for the ground communication system of the ISS. EZStream includes server components that interact with TReK within the ISS ground communication system and client components that reside in the users' remote computers. Once an authorized client has logged in, a server component of EZStream pulls the requested data from a TReK application-program interface and sends the data to the client. Future EZStream enhancements will include (1) extensions that enable the server to receive and process arbitrary data streams on its own and (2) a Web-based graphical-user-interface-building subprogram that enables a client who lacks programming expertise to create customized display Web pages.

Myers, Gerry↗
NASA NTRS1985

Fuel containment and damage tolerance in large composite primary aircraft structures. Phase 2: Testing

Technical issues associated with fuel containment and damage tolerance of composite wing structures for transport aircraft were investigated. Material evaluation tests were conducted on two toughened resin composites: Celion/HX1504 and Celion/5245. These consisted of impact, tension, compression, edge delamination, and double cantilever beam tests. Another test series was conducted on graphite/epoxy box beams simulating a wing cover to spar cap joint configuration of a pressurized fuel tank. These tests evaluated the effectiveness of sealing methods with various fastener types and spacings under fatigue loading and with pressurized fuel. Another test series evaluated the ability of the selected coatings, film, and materials to prevent fuel leakage through 32-ply AS4/2220-1 laminates at various impact energy levels. To verify the structural integrity of the technology demonstration article structural details, tests were conducted on blade stiffened panels and sections. Compression tests were performed on undamaged and impacted stiffened AS4/2220-1 panels and smaller element tests to evaluate stiffener pull-off, side load and failsafe properties. Compression tests were also performed on panels subjected to Zone 2 lightning strikes. All of these data were integrated into a demonstration article representing a moderately loaded area of a transport wing. This test combined lightning strike, pressurized fuel, impact, impact repair, fatigue and residual strength.

Sandifer, J. P.↗
NASA NTRS2017

Improved Reference Sampling and Subtraction: A Technique for Reducing the Read Noise of Near-Infrared Detector Systems

Near-infrared array detectors, like the James Webb Space Telescope (JWST) NIRSpec's Teledyne's H2RGs, often provide reference pixels and a reference output. These are used to remove correlated noise. Improved reference sampling and subtraction (IRS(exp 2)) is a statistical technique for using this reference information optimally in a leastsquares sense. Compared with the traditional H2RG readout, IRS(exp 2) uses a different clocking pattern to interleave many more reference pixels into the data than is otherwise possible. Compared with standard reference correction techniques, IRS(exp 2) subtracts the reference pixels and reference output using a statistically optimized set of frequency dependent weights. The benefits include somewhat lower noise variance and much less obvious correlated noise. NIRSpec's IRS(exp 2) images are cosmetically clean, with less 1/f banding than in traditional data from the same system. This article describes the IRS(exp 2) clocking pattern and presents the equations needed to use IRS(exp 2) in systems other than NIRSpec. For NIRSpec, applying these equations is already an option in the calibration pipeline. As an aid to instrument builders, we provide our prototype IRS(exp 2) calibration software and sample JWST NIRSpec data. The same techniques are applicable to other detector systems, including those based on Teledyne's H4RG arrays. The H4RG's interleaved reference pixel readout mode is effectively one IRS(exp 2) pattern.

Methods-statistical↗
DOE OSTI2017

The AME2016 atomic mass evaluation (I). Evaluation of input data; and adjustment procedures

This paper is the first of two articles (Part I and Part II) that presents the results of the new atomic mass evaluation, Ame2016. It includes complete information on the experimental input data (also including unused and rejected ones), as well as details on the evaluation procedures used to derive the tables of recommended values given in the second part. This article describes the evaluation philosophy and procedures that were implemented in the selection of specific nuclear reaction, decay and mass-spectrometric results. These input values were entered in the least-squares adjustment for determining the best values for the atomic masses and their uncertainties. Details of the calculation and particularities of the Ame are then described. All accepted and rejected data, including outweighted ones, are presented in a tabular format and compared with the adjusted values obtained using the least-squares fit analysis. Differences with the previous Ame2012 evaluation are discussed and specific information is presented for several cases that may be of interest to Ame users. The second Ame2016 article gives a table with the recommended values of atomic masses, as well as tables and graphs of derived quantities, along with the list of references used in both the Ame2016 and the Nubase2016 evaluations (the first paper in this issue). Amdc: http://amdc.impcas.ac.cn/

Ame↗
DOE OSTI2014

Performance Evaluation of Advanced Retrofit Roof Technologies Using Field-Test Data Phase Three Final Report, Volume 1

This article presents various metal roof configurations that were tested at Oak Ridge National Laboratory in Tennessee, U.S.A. between 2009 and 2013, and describes their potential for reducing the attic-generated space conditioning loads. These roofs contained different combinations of phase change material, rigid insulation, low emittance surface and above-sheathing ventilation, with standing-seam metal panels on top. These roofs were designed to be installed on existing roofs decks, or on top of asphalt shingles for retrofit construction. All the tested roofs showed the potential for substantial energy savings compared to an asphalt shingle roof, which was used as a control for comparison. The roofs were constructed on a series of adjacent attics separated at the gables using thick foam insulation. The attics were built on top of a conditioned room. All attics were vented at the soffit and ridge. The test roofs and attics were instrumented with an array of thermocouples. Heat flux transducers were installed in the roof deck and attic floor (ceiling) to measure the heat flows through the roof and between the attic and conditioned space below. Temperature and heat flux data were collected during the heating, cooling and swing seasons over a 3 year period. Data from previous years of testing have been published. Here, data from the latest roof configurations being tested in year 3 of the project are presented. All test roofs were highly effective in reducing the heat flows through the roof and ceiling, and in reducing the diurnal attic temperature fluctuations.

above sheating ventilation↗
DOE OSTI2025

Large language model-driven database for thermoelectric materials

Thermoelectric materials have the ability to convert waste heat into electricity, offering a valuable solution for energy harvesting. However, their widespread use is hindered by low conversion efficiency, the reliance on expensive rare earth elements, and the environmental and regulatory concerns associated with lead-based materials. A fast and cost-effective way to identify highly efficient thermoelectric materials is through data-driven methods. These approaches rely on robust and comprehensive datasets to train models. Although there are several databases on thermoelectric materials, there is still a need to collect and integrate experimental data from peer-reviewed research articles to capture diverse compositions and properties of materials. Here, in this work, we developed a comprehensive database of 7,123 thermoelectric compounds, containing key information such as chemical composition, structural detail, seebeck coefficient, electrical and thermal conductivity, power factor, and figure of merit (ZT). We used the GPTArticleExtractor workflow, powered by large language models (LLM), to extract and curate data automatically from the scientific literature published in Elsevier journals. This process enabled the creation of a structured database that addresses the challenges of manual data collection. The open access database could stimulate data-driven research and advance thermoelectric material analysis and discovery.

Database↗
DOE OSTI2025

Observations of surface energy fluxes and meteorology in the seasonally snow-covered high-elevation East River watershed during SPLASH, 2021–2023

Abstract. From autumn 2021 through summer 2023, scientists from the National Oceanic and Atmospheric Administration (NOAA) and partners conducted the Study of Precipitation, the Lower Atmosphere, and Surface for Hydrometeorology (SPLASH) campaign in the East River watershed of Colorado. One objective of SPLASH was to observe the transfer of energy between the atmosphere and the surface, which was done at several locations. Two remote sites were chosen that did not have access to power utilities. These were along the valley floor near the East River in the vicinity of the unincorporated town of Gothic, Colorado. Energy balance measurements were made at these locations using autonomous, single-level flux towers referred to as atmospheric surface flux stations (ASFSs). The ASFSs were deployed on 28 September 2021 at the Kettle Ponds Annex site and on 12 October 2021 at the Avery Picnic site and operated until 19 July and 21 June 2023, respectively. Measurements included basic meteorology; upward and downward longwave and shortwave radiative fluxes and subsurface conductive flux, each at 1 min resolution; 3-D winds from a sonic anemometer and H2O/CO2 from an open-path gas analyzer, both at 20 Hz from which sensible, latent heat, and CO2 fluxes were derived; and profiles of soil properties in the upper 0.5 m (both sites) and temperature profiles through the snow (at Avery Picnic), each reported between 10 min and 6 h. The system uptime was 97 % (Kettle Ponds) and 90 % (Avery Picnic), and collectively 1184 d of data was obtained between the stations. The purpose of this article is to document the ASFS deployment at SPLASH, to document the data acquisition and post-processing of measurements, and to serve as a guide for interested users of the data sets, which are archived at Zenodo (https://doi.org/10.5281/zenodo.10313363, Cox et al., 2023b; https://doi.org/10.5281/zenodo.10327409, Cox et al., 2023c; https://doi.org/10.5281/zenodo.10313894, Cox et al., 2023d; https://doi.org/10.5281/zenodo.10307825, Cox et al., 2023e; https://doi.org/10.5281/zenodo.10310520, Cox et al., 2023f) with the Creative Commons Attribution 4.0 International license.

Cox, Christopher J. (ORCID:0000000322037173)↗
DOE OSTI2021

Challenges and Advances in Information Extraction from Scientific Literature: a Review

Scientific articles have long been the primary means of disseminating scientific discoveries. Over the centuries, valuable data and potentially groundbreaking insights have been collected and buried deep in the mountain of publications. In materials engineering, such data are spread across technical handbooks specification sheets, journal articles, and laboratory notebooks in myriad formats. Extracting information from papers on a large scale has been a tedious and time-consuming job to which few researchers have wanted to devote their limited time and effort, yet is an activity that is essential for modern data-driven design practices. However, in recent years, significant progress has been made by the computer science community on techniques for automated information extraction from free text. Yet, transformative application of these techniques to scientific literature remains elusive-due not to a lack of interest or effort but to technical and logistical challenges. Using the challenges in the materials science literature as a driving motivation, we review the gaps between state-of-the-art information extraction methods and the practical application of such methods to scientific texts, and offer a comprehensive overview of work that can be undertaken to close these gaps.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗
DOE OSTI2024

A Compound Data Poisoning Technique with Significant Adversarial Effects on Transformer-based Sentiment Classification Tasks

Transformer-based models have demonstrated much success in various natural language processing tasks. However, they are often vulnerable to adversarial attacks, such as data poisoning, which can intentionally fool the model into generating incorrect results. In this article, we present a novel, compound variant of a data poisoning attack on a transformer-based model that maximizes the poisoning effect while minimizing the scope of poisoning. Here we do so by combining the established data poisoning technique (label flipping) with a novel adversarial artifact selection and insertion technique aimed at minimizing detectability and the scope of the poisoning footprint. We find that by using a combination of these two techniques, we achieve a state-of-the-art attack success rate of approximately 90% while poisoning only 0.5% of the original training set, thus minimizing the scope and detectability of the poisoning action. These findings have the potential to advance the development of better data poisoning detection methods.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS2005

CRYSTAL-FACE Polarization Lidar Research

The University of Alaska Fairbanks (UAF) Polarization Diversity Lidar (PDL, Sassen 1994) participated in the July 2002 CRYSTAL-FACE field campaign, shortly after the PI moved from the University of Utah to UAF. The truck-mounted PDL is an advanced dual wavelength (1.06 and 0.532 micron), high resolution (0.1-s by 1.5-m), scanning lidar system designed as a testbed for evaluating laser backscatter depolarization techniques for the study of clouds and aerosols in the atmosphere. The main goals identified in our proposal for the CRYSTAL-FACE experiment were, i) the characterization of Florida thunderstorm anvil macrophysical and microphysical properties from lidar backscattering and depolarization, ii) the study of thin to subvisual tropopause-topped subtropical cirrus, iii) the search for indirect cloud effects of trans- Atlantic advected Saharan dust storm aerosols on clouds, and iv) the investigation of melting layer effects on lidar and multi-wavelength Doppler radar measurements in precipitation. Although we experienced adversity in the field during the campaign, sufficient data was collected to begin addressing these topics, and several conference presentations, three journal articles, and one book chapter have resulted from the data analysis effort supported by this grant. (PDL operations were delayed by FAA concerns over the initial sighting at the Kendall-Tamiami Airport, and a brief but major laser breakdown was experienced during the re- setup at the remote Ochopee Everglades site that also supported the N-POL radar.) All lidar data collected by the PDL system were processed and quality checked, and submitted to the CRYSTAL-FACE data archive in a timely manner.

Sassen, Kenneth↗
DOE OSTI2025

ILLICIT TRANSIT INTERDICTION GLOBAL ANALYSIS

This study aims to enhance the security of radioactive materials during transport by analyzing commonalities in cargo thefts conducted by non-state groups such as thieves and terrorists. This research focuses on identifying patterns and trends in the methods used to steal high-value cargo, with the goal of applying these insights to improve transport security of radioactive materials. Key questions addressed include the frequency of specific tools, techniques, and insider involvement in thefts, as well as the use of weapons, electronic jamming equipment, and specialized tools. Findings will inform security design improvements and industry practices to mitigate vulnerabilities. The study involves a comprehensive review of literature and case studies, utilizing data sources from 2018 to 2023. Articles will be selected based on their relevance to thefts of valuable cargo in transit, with a focus on incidents involving non-state actors. The methodology will include statistical and inferential analysis to identify trends, with results visualized through pie charts, frequency analyses, and terrain maps. The discussion will highlight the implications of findings and provide actionable recommendations for strengthening security measures. Limitations such as data availability and reporting inconsistencies will be acknowledged. Suggestions for future improvements will be constructed using existing case studies and expert feedback. The study’s outcomes aim to raise awareness within the industry, inform policy decisions, and enhance security protocols for radioactive material transport. Metrics for impact include the potential publication of findings, presentations at conferences to raise awareness, and the subsequent actions taken by stakeholders based on the research. By identifying trends and vulnerabilities and suggesting improvements, this research contributes to preventing the illicit use of nuclear and radiological materials.

Zineddin, Dr. Z. [ORNL] (ORCID:0009000848740725)↗
NASA NTRS2024

Preface to the Special Issue on Modeling and Data Analysis Methods for the SMILE mission

The SMILE (Solar wind Magnetosphere Ionosphere Link Explorer) project (http://www.nssc.cas.cn/smile/, https://www.cosmos.esa.int/web/smile/mission) is a joint spacecraft mission of the European Space Agency (ESA) and the Chinese Academy of Sciences (CAS) with an expected launch in 2025. SMILE aims to study the global interactions of solar wind–magnetosphere–ionosphere innovatively by imaging the Earth’s magnetosheath and cusps in soft X-rays and the northern auroral region in ultraviolet (UV) while simultaneously measuring plasma and magnetic field parameters in the solar wind and magnetosheath along a highly-elliptical and highly-inclined orbit. This special issue is composed of 22 articles, presenting recent progress in modeling and data analysis techniques developed for the SMILE mission. In this preface, we categorize the articles into the following seven topics and provide brief summaries: (1) instrument descriptions of the Soft X-ray Imager (SXI), (2) numerical modeling of the X-ray signals, (3) data processing of the X-ray images, (4) boundary tracing methods from the simulated images, (5) physical phenomena and a mission concept related to the scientific goals of SMILE-SXI, (6) studies of the aurora, and (7) ground-based support for SMILE.

SMILE↗
DOE OSTI2019

Efficient data extraction from neutron time-of-flight spin-echo raw data

Neutron spin-echo spectrometers with a position-sensitive detector and operating with extended time-of-flight-tagged wavelength frames are able to collect a comprehensive set of data covering a large range of wavevector and Fourier time space with only a few instrumental settings in a quasi-continuous way. Extracting all the information contained in the raw data and mapping them to a suitable physical space in the most efficient way is a challenge. This article reports algorithms employed in dedicated software, DrSpine (data reduction for spin echo), that achieves this goal and yields reliable representations of the intermediate scattering function S(Q, t) independent of the selected `binning'.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Model-based data center cooling controls comparative co-design

This article presents a comparative simulation-based control logic design process. It uses the Control Description Language (CDL) and the ASHRAE Guideline 36 high-performing building control sequences with the Modelica Buildings Library (MBL) to demonstrate a comparative analysis of two control designs for a data center chilled water plant. Details include a description of the closed-loop plant and control design methodology, including sizing and parameterization, base and alternative (Guideline 36) control logic with software implementation structure, and outline of the simulation experimentation process. The selected control designs are paired with comparable chilled water plant configurations. The models include a chiller, a water-side economizer, and an evaporative cooling tower. The plant provides cooling at 27ºC zone supply air temperature to a data center in Sacramento, CA. The comparative simulation results examined the impacts of a selected control logic detail, and present an example model-based design application. Overall, the simulation results showed a 25% annual and a 18% summer energy use reduction for alternative controls. This shows that simulation-based control logic design performance evaluation can improve energy efficiency and resilience aspects of system controls at large.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2025

Electrical Load Forecasting Over Multihop Smart Metering Networks With Federated Learning

Electric load forecasting is essential for power management and stability in smart grids. This is mainly achieved via advanced metering infrastructure, where smart meters (SMs) record household energy data. Traditional machine learning (ML) methods are often employed for load forecasting, but require data sharing, which raises data privacy concerns. Federated learning (FL) can address this issue by running distributed ML models at local SMs without data exchange. However, current FL-based approaches struggle to achieve efficient load forecasting due to imbalanced data distribution across heterogeneous SMs. Here, this article presents a novel personalized FL (PFL) method for high-quality load forecasting in metering networks. A meta-learning-based strategy is developed to address data heterogeneity at local SMs in the collaborative training of local load forecasting models. Moreover, to minimize the load forecasting delays in our PFL model, we study a new latency optimization problem based on optimal resource allocation at SMs. A theoretical convergence analysis is also conducted to provide insights into FL design for federated load forecasting. Extensive simulations from real-world datasets show that our method outperforms existing approaches regarding better load forecasting and reduced operational latency costs.

Rahman, Ratun [Univ. of Alabama, Huntsville, AL (U↗
DOE OSTI2022

Data-Driven Dynamic Modeling in Power Systems: A Fresh Look on Inverter-Based Resource Modeling

Here, this article introduces ways to identify dynamic system models using measurement data. In power system analysis, a static model represents the time-invariant input-output relationship of a system, while a dynamic model describes the behavior of the system over time. For example, how will a system transit from one steady-state operation point to another?

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2015

Framework for Identifying Cybersecurity Risks in Manufacturing

Increasing connectivity, use of digital computation, and off-site data storage provide potential for dramatic improvements in manufacturing productivity, quality, and cost. However, there are also risks associated with the increased volume and pervasiveness of data that are generated and potentially accessible to competitors or adversaries. Enterprises have experienced cyber attacks that exfiltrate confidential and/or proprietary data, alter information to cause an unexpected or unwanted effect, and destroy capital assets. Manufacturers need tools to incorporate these risks into their existing risk management processes. This article establishes a framework that considers the data flows within a manufacturing enterprise and throughout its supply chain. The framework provides several mechanisms for identifying generic and manufacturing-specific vulnerabilities and is illustrated with details pertinent to an automotive manufacturer. Finally, in addition to providing manufacturers with insights into their potential data risks, this framework addresses an outcome identified by the NIST Cybersecurity Framework.

97 MATHEMATICS AND COMPUTING↗