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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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All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

DeepLynx Ecosystem 2025

Poor data integration and governance continue to plague complex engineering projects, resulting in missed cost, schedule, and performance targets. Departments operate in isolated systems with manual data exchange, creating fragmented information that compounds errors and leads to significant delays and cost overruns. The DeepLynx ecosystem addresses these challenges through an open-source, modular data management platform that transforms fragmented project data into an integrated digital thread. Built on a federated microservice architecture, the ecosystem comprises seven specialized tools centered around DeepLynx Nexus, a unified data catalog with hierarchical organization and graph-based navigation capabilities. The ecosystem includes: DeepLynx Stream for real-time timeseries data ingestion from industrial sources; DeepLynx Ingest for governed data uploads with formal review workflows; DeepLynx Lattice for ontology-based entity and relationship extraction; DeepLynx Run for workflow orchestration and secure AI/ML compute; DeepLynx Visualize for 3D digital twin visualization; and DeepLynx Insight for AI-assisted document analysis with traceable, grounded responses. Deployable in cloud, on-premise, or hybrid environments using containerized Docker applications and Helm charts, the DeepLynx ecosystem provides flexible infrastructure that adapts to organizational requirements. By consolidating project data into a unified data lake with role-based access controls and OAuth2 authentication, DeepLynx enables digital thread and digital twin capabilities that improve decision-making, reduce risk, and support complex engineering workflows throughout the project lifecycle.

42 - ENGINEERING↗
DOE OSTI2023

Energy Community Atlas

The Energy Community Atlas provides efficient access to authoritative, curated, and relevant data that is vital to supporting energy planning, development, and economic growth across the U.S. In this effort, researchers at the National Energy Technology Laboratory (NETL) are utilizing advanced data visualization and transformation capabilities to develop an integrated, data atlas and resource focused on supporting energy community transitions to new manufacturing opportunities. Specifically, this project is working to find, acquire, integrate, and virtually host in a user-friendly, public and private solution from available resources, relevant to understanding and characterizing fossil energy communities themselves and inform energy planning, development, and economic growth opportunities, including opportunities for co-development to support manufacturing, critical materials, and more. This Atlas when complete is to offer a one-stop-shop for stakeholders to derive new insights to accelerate energy investments and strategic decision support needs. These are following datasets that are available as part of this ongoing project • Energy Community Atlas Map Package - This is ArcPro Map package and it contains all of the symbolized layers along with ArcPro map and geodatabase • Energy Community Atlas ArcGIS REST service - https://www.arcgis.com/apps/mapviewer/index.html?panel=gallery&suggestField=true&layers=537ced69bd88440380a62c2ec8aca30c • README Energy Community Atlas - Read me word document that has details about feature classes in Map package, ArcPro map and ArcGIS Rest Service

Bipartisan Infrastructure Law↗
DOE OSTI2025

Navigating Integration: Key Challenges for Data Centers, Nuclear Stakeholders, and Utility Operators

The rapid expansion of data centers, driven by the exponential growth in data-processing and storage needs, presents significant challenges and opportunities for various stakeholders, including data center developers, nuclear energy providers, and utility companies. Data centers are projected to consume 6.7–12% of United States (U.S.) electricity by 2028, driven by artificial intelligence (AI) and cloud-computing demands. Nuclear energy offers reliability and dispatchable baseload power, but data centers need power now while nuclear still needs time to address siting, fast power ramping, and regulatory hurdles. Utilities must keep pace with the unprecedented acceleration of large load interconnection requests and urgently adapt to high-density loads while maintaining grid stability, reliability, and accelerating interconnection timelines. This report dives into these challenges and proposes key collaboration strategies to streamline data center integration that aligns with recent federal initiatives like America’s AI Action Plan and related executive orders that emphasize the importance of data center growth, nuclear energy expansion, and maintaining a competitive edge in the global AI race.

21 - SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLAN↗
NASA NTRS1981

Operational alternatives for LANDSAT in California

Data integration is defined and examined as the means of promoting data sharing among the various governmental and private geobased information systems in California. Elements of vertical integration considered included technical factors (such as resolution and classification) and institutional factors (such as organizational control, and legal and political barriers). Attempts are made to fit the theoretical elements of vertical integration into a meaningful structure for looking at the problem from a statewide focus. Both manual (mapped) and machine readable data systems are included. Special attention is given to LANDSAT imagery because of its strong potential for integrated use and its primary in the California Integrated Remote Sensing System program.

Wilson, P.↗
NASA NTRS1991

Integration of SAR and DEM data: Geometrical considerations

General principles for integrating data from different sources are derived from the experience of registration of SAR images with digital elevation models (DEM) data. The integration consists of establishing geometrical relations between the data sets that allow us to accumulate information from both data sets for any given object point (e.g., elevation, slope, backscatter of ground cover, etc.). Since the geometries of the two data are completely different they cannot be compared on a pixel by pixel basis. The presented approach detects instances of higher level features in both data sets independently and performs the matching at the high level. Besides the efficiency of this general strategy it further allows the integration of additional knowledge sources: world knowledge and sensor characteristics are also useful sources of information. The SAR features layover and shadow can be detected easily in SAR images. An analytical method to find such regions also in a DEM needs in addition the parameters of the flight path of the SAR sensor and the range projection model. The generation of the SAR layover and shadow maps is summarized and new extensions to this method are proposed.

Kropatsch, Walter G.↗
DOE OSTI2025

Mass Spectrometry Sample Submission Portal

Each step in the scientific process generates contextual information about the data that is important to consider when performing data integration, developing models of biological process, or training AI models. We will develop a flexible, template-driven tool that will log biological samples, capture metadata about those samples, and track the type(s) of analysis being performed by researchers providing samples for analysis by mass spectrometry.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Machine Learning to Select Experiments Driven by Fundamental Science and Applications for Targeted Nuclear Data Improvement

This work describes a blueprint for a process that accelerates progress in science by quantitatively answering the following question: What is the optimal combination of fundamental-science and application-driven experiments to maximally reduce pertinent data uncertainties? Answering this question entails solving a high-dimensional and complex optimization problem that is best solved with advanced statistic techniques often classified as machine learning. We apply this process within the framework of nuclear data with the aim to select an experiment combination that will reduce uncertainties in 239 Pu nuclear data for neutron energies between 1 and 600 keV. In this field, fundamental-physics driven data, called differential, look at one nuclear physics observable at a time. They are contrasted to application-driven, integral, data where one or few resulting values inform a broad set of nuclear data across several nuclides and energies. The candidates for integral experiments are criticality measurements that were refined by a genetic algorithm to be maximally sensitive to 239 Pu fission cross sections in the desired energy range. Twenty-three candidate differential experiments were investigated and span multiple nuclear physics observables (e.g., total, capture cross sections) for isotopes appearing in the integral experiments. The optimal combination among these candidate experiments was investigated via generalized least squares fitting, augmented with Gaussian processes to ameliorate statistical irregularities in data, and the D-optimality criterion. The latter evaluates for each pair of candidates the joint reduction in uncertainties of all 12200 nuclear data appearing in the integral experiments compared to the knowledge we have from 168 past experiments, theory, and nuclear data. We chose as differential measurements those that investigate 63 Cu and 239 Pu total cross sections, based on D-optimality rank and feasibility constraints. Two integral (criticality) experiments were selected: An experiment with Al 2 ⁢O 3 and graphite interleaved with Pu and a thick Cu reflector explores 1–30 keV, while we target the 30–600 keV range with an experiment that swaps boron in place of graphite with a different geometry.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
NASA NTRS1993

Telemetry Monitoring and Display Using LabVIEW

The Measurement Technology Center of the Instrumentation Section configures automated data acquisition systems to meet the diverse needs of JPL's experimental research community. These systems are based on personal computers or workstations (Apple, IBM/Compatible, Hewlett-Packard, and Sun Microsystems) and often include integrated data analysis, visualization and experiment control functions in addition to data acquisition capabilities. These integrated systems may include sensors, signal conditioning, data acquisition interface cards, software, and a user interface. Graphical programming is used to simplify configuration of such systems. Employment of a graphical programming language is the most important factor in enabling the implementation of data acquisition, analysis, display and visualization systems at low cost. Other important factors are the use of commercial software packages and off-the-shelf data acquisition hardware where possible. Understanding the experimenter's needs is also critical. An interactive approach to user interface construction and training of operators is also important. One application was created as a result of a competative effort between a graphical programming language team and a text-based C language programming team to verify the advantages of using a graphical programming language approach. With approximately eight weeks of funding over a period of three months, the text-based programming team accomplished about 10% of the basic requirements, while the Macintosh/LabVIEW team accomplished about 150%, having gone beyond the original requirements to simulate a telemetry stream and provide utility programs. This application verified that using graphical programming can significantly reduce software development time. As a result of this initial effort, additional follow-on work was awarded to the graphical programming team.

graphical user interface LabVIEW graphical program↗
DOE OSTI2024

Investigation of Benchmark $k$ eff Sensitivity and Uncertainty for 239 Pu fission in Specific Energy Ranges

Nuclear data at intermediate energies (from 1 to 100s of keV) are evaluated based on scarce differential data and theory unable to capture physics’ expected structure. There is also a lack of integral data. This is a known deficiency and is challenging to address. Calculated effective multiplication factor, k eff , values for intermediate energy experiments are ~25× further from experiment than for fast energies and are often well outside the experimental uncertainties. The goal of the PARADIGM (PARallel Approach of Differential and InteGral Measurements) project is to significantly re duce the uncertainties of intermediate energy nuclear data for 239 Pu. To this end, PARADIGM simultaneously optimizes experiments at both the Los Alamos Neutron Science Center (LANSCE) and National Criticality Experiments Research Center (NCERC). The combined set of data will inform new intermediate-energy nuclear data. By execution of differential and integral experiments, establishment of new theory, and undertaking nuclear data evaluation in parallel, the timeline to deliver improved nuclear data to users will be reduced significantly that is to three years. For the PARADIGM project, it was decided to optimize an integral experiment for two neutron energy ranges, within the full intermediate energy range. The low energy range goes from 1 to 30 keV, while the higher energy range goes from 30 to 600 keV. This work focuses on nuclear data sensitivities and uncertainties for 239 Pu fission for existing experiments in the International Criticality Safety Benchmark Evaluation Project (ICSBEP). When designing new experiments, it is important to understand what benchmarks currently exist. For a more traditional experiment design (in which a specific application model(s) exists), comparisons would be made between the application model(s) and existing benchmarks. For PARADIGM, there is no specific application model, but instead the specific nuclear data reaction and energy ranges of interest can be explored for existing benchmarks.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2017

As-Loaded Criticality Margin Assessment of Dual-Purpose Canisters Using UNF-ST&DARDS

A novel assessment has been completed in this paper to determine the previously unquantified and uncredited criticality margin available in as-loaded commercial spent nuclear fuel (SNF) canisters. This assessment was performed as part of a broader effort to assess issues and uncertainties with storage, subsequent transportation, and final disposal of SNF canister systems. Detailed analyses crediting the burnup, initial enrichment, and postirradiation cooling time of actual SNF inventory were performed for 554 SNF canisters stored at 23 commercial reactor sites to determine realistic criticality safety margins. These detailed analyses were automated by the Used Nuclear Fuel-Storage, Transportation & Disposal Analysis Resource and Data System (UNF-ST&DARDS), a comprehensive, integrated data and analysis tool. Calculated, uncredited criticality margins (Δk eff ) with respect to the safety analysis results range from 0 to almost 0.30 Δk eff for normal storage and transportation cases. Calculated eigenvalues (k eff ) range from 0.72 to 1.11 assuming a degraded neutron absorber disposal condition, and they range from 0.94 to 1.20 assuming a degraded basket disposal condition. Calculations with NaCl present in the moderator (which is possible for certain disposal geologies) were used to demonstrate the possibility for subcriticality for degraded cases with a k eff above 0.98 with freshwater. The methods used to calculate k eff for the canisters analyzed in this work are discussed in detail. The results demonstrate that, for the majority of canisters analyzed here, significant uncredited safety margin is available that could be used to compensate for uncertainties in the SNF assembly and canister internal components. These uncertainties are associated with long-term storage and subsequent transportation and disposal. Results also suggest that the inherent margins associated with how canisters are loaded could support future changes in licensing SNF storage and transportation systems to directly or indirectly credit the margins associated with actual SNF characteristics. Finally, ongoing research continues to gather additional data to quantify uncredited safety margins for SNF canisters loaded at other nuclear reactor sites and to explore potential methods for applying this uncredited margin.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2017

Interannual variability of ecosystem carbon exchange: From observation to prediction

Here, terrestrial ecosystems have sequestered, on average, the equivalent of 30% of anthropogenic carbon (C) emissions during the past decades, but annual sequestration varies from year to year. For effective C management, it is imperative to develop a predictive understanding of the interannual variability (IAV) of terrestrial net ecosystem C exchange (NEE). Location: Global terrestrial ecosystems. Furthermore, we conducted a comprehensive review to examine the IAV of NEE at global, regional and ecosystem scales. Then we outlined a conceptual framework for understanding how anomalies in climate factors impact ecological processes of C cycling and thus influence the IAV of NEE through biogeochemical regulation. As a result, the phenomenon of IAV in land NEE has been ubiquitously observed at global, regional and ecosystem scales. Global IAV is often attributable to either tropical or semi-arid regions, or to some combination thereof, which is still under debate. Previous studies focus on identifying climate factors as driving forces of IAV, whereas biological mechanisms underlying the IAV of ecosystem NEE are less clear. We found that climate anomalies affect the IAV of NEE primarily through their differential impacts on ecosystem C uptake and respiration. Moreover, recent studies suggest that the carbon uptake period makes less contribution than the carbon uptake amplitude to IAV in NEE. Although land models incorporate most processes underlying IAV, their efficacy to predict the IAV in NEE remains low. In conclusion, to improve our ability to predict future IAV of the terrestrial C cycle, we have to understand biological mechanisms through which anomalies in climate factors cause the IAV of NEE. Future research needs to pay more attention not only to the differential effects of climate anomalies on photosynthesis and respiration but also to the relative importance of the C uptake period and amplitude in causing the IAV of NEE. Ultimately, we need multiple independent approaches, such as benchmark analysis, data assimilation and time-series statistics, to integrate data, modelling frameworks and theory to improve our ability to predict future IAV in the terrestrial C cycle.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Tools for Visualization and Analysis of Small-Angle Neutron Scattering Data: Descriptions and Examples

A great deal of progress has been made in improving the data reduction experience for the SANS instruments at the SNS and HFIR at ORNL. The existing data reduction toolset, drtsans, makes it possible to integrate data analysis and visualization tools into the data reduction scripts, thereby providing new opportunities for more automated data processing for users of the SNS and HFIR. Here, the first set of tools developed is described with usage examples.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
NASA NTRS1984

Ionospheric electron density irregularities observed by satellite-to-satellite, dual-frequency, low-low Doppler tracking link

A low-low, satellite-to-satellite, dual-frequency, Doppler tracking experiment was performed. The data are analyzed here for irregularities in electron density at the altitude of 212 km. The differential Doppler data with the relative motion term removed are integrated to obtain a representation of the electron density variation along the satellite path. Well-known large-scale features such as the equatorial geomagnetic anomaly and day/night ionization level differences are clearly observed in the integrated data. The larger crest of the morning geomagnetic anomaly is seen to occur in the southern (winter) hemisphere in agreement with previous observations. In addition, a sharp peak in the electron density at the day-to-night transition point is observed in two consecutive revolutions. This effect may be due to the previously postulated atmospheric shock wave generated by supersonic motion of the terminator.

Estes, R. D.↗
NASA NTRS2016

Datalist: A Value Added Service to Enable Easy Data Selection

Imagine a user wanting to study hurricane events. This could involve searching and downloading multiple data variables from multiple data sets. The currently available services from the Goddard Earth Sciences Data and Information Services Center (GES DISC) only allow the user to select one data set at a time. The GES DISC started a Data List initiative, in order to enable users to easily select multiple data variables. A Data List is a collection of predefined or user-defined data variables from one or more archived data sets. Target users of Data Lists include science teams, individual science researchers, application users, and educational users. Data Lists are more than just data. Data Lists effectively provide users with a sophisticated integrated data and services package, including metadata, citation, documentation, visualization, and data-specific services, all available from one-stop shopping. Data Lists are created based on the software architecture of the GES DISC Unified User Interface (UUI). The Data List service is completely data-driven, and a Data List is treated just as any other data set. The predefined Data Lists, created by the experienced GES DISC science support team, should save a significant amount of time that users would otherwise have to spend.

Datalist↗
NASA NTRS2017

GES DISC Datalist Enables Easy Data Selection For Natural Phenomena Studies

In order to investigate and assess natural hazards such as tropical storms, winter storms, volcanic eruptions, floods, and drought in a timely manner, the NASA Goddard Earth Sciences Data and Information Services Center (GES DISC) has been developing an efficient data search and access service. Called "Datalist," this service enables users to acquire their data of interest "all at once," with minimum effort. A Datalist is a virtual collection of predefined or user-defined data variables from one or more archived data sets. Datalists are more than just data. Datalists effectively provide users with a sophisticated integrated data and services package, including metadata, citation, documentation, visualization, and data-specific services (e.g., subset and OPeNDAP), all available from one-stop shopping. The predefined Datalists, created by the experienced GES DISC science support team, should save a significant amount of time that users would otherwise have to spend. The Datalist service is an extension of the new GES DISC website, which is completely data-driven. A Datalist, also known as "data bundle," is treated just as any other data set. Being a virtual collection, a Datalist requires no extra storage space.

natural hazards↗
DOE OSTI2024

Integrating Multi-Source Data for Bi-Level Traffic Simulator Calibration: A Literature Review and Highway Case Study

Traffic simulation serves as a powerful tool for pre-evaluating policies and technologies. In this context, simulation-based Dynamic traffic assignment (DTA) models are capable of capturing traffic dynamics. They are well-known as critical tools in controlling and predicting traffic situations. The reliability of simulation results heavily depends on the calibration process. Most studies in the literature formulate and calibrate simulators based on a single source of collected data or multiple data sets with the same spatiotemporal characteristics. However, in practice, traffic data is collected by various tools with usually different spatial and temporal resolutions. This study introduces a novel approach to taking into account diverse input data from a variety of sources. An iterative bi-level solution is proposed. to equally treat traffic flow and speed data. The upper level solves flow calibration with the exact solution method, and the lower level calibrates the speed with the simultaneous perturbation stochastic approximation (SPSA) algorithm. Subsequently, the effectiveness of the proposed model is investigated using data from a six-mile section of Nashville's I-24 highway in Tennessee. The results demonstrate that our proposed model creates an effective feedback loop between the optimizer and the simulator for calibrating flow and speed to reduce the error between simulated and real data.

42 ENGINEERING↗
DOE OSTI2015

Integration of a Self-Coherence Algorithm into DISAT for Forced Oscillation Detection

With the increasing number of phasor measurement units on the power system, behaviors typically not observable on the power system are becoming more apparent. Oscillatory behavior on the power system, notably forced oscillations, are one such behavior. However, the large amounts of data coming from the PMUs makes manually detecting and locating these oscillations difficult. To automate portions of the process, an oscillation detection routine was coded into the Data Integrity and Situational Awareness Tool (DISAT) framework. Integration into the DISAT framework allows forced oscillations to be detected and information about the event provided to operational engineers. The oscillation detection algorithm integrates with the data handling and atypical data detecting capabilities of DISAT, building off of a standard library of functions. This report details that integration with information on the algorithm, some implementation issues, and some sample results from the western United States’ power grid.

Forced oscillations↗
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↗