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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 1,387 records · Page 77
All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

Web-based Preprocessing and Visualization of 3D FIB Tomography Data for Nuclear Fuel Characterization

Three-dimensional (3D) focused ion beam (FIB) tomography enables reconstruction of internal nuclear fuel features that can't be fully evaluated through surface imaging alone. This capability supports characterization of fuel constituents and defects under thermal and irradiation conditions relevant to microreactor development. However, large tomography datasets can create data-handling, loading, and visualization challenges, especially when image-stack preparation and file conversion must be completed with separate tools. The Computational Ultraspatial Tomography Toolkit for High-Resolution Object Analysis Tools (CUTTRHOAT) is an open-source web application being developed to display FIB tomography datasets available through the Nuclear Research Data System (NRDS). The current alpha version requires prepared HDF5 datasets and has limited integrated data-preparation capabilities. This project improves CUTTHROAT by adding dataset-folder selection, automatic input detection, dataset scanning, missing-slice identification, blank-slice insertion, and image-stack-to-HDF5 conversion. Two applications will be compared: the baseline CUTTHROAT alpha workflow and the updated application containing the integrated data-handling and preprocessing functions. Evaluation will consider dataset detection accuracy, conversion success, loading time, rendering responsiveness, application stability, and user interaction. Preliminary results demonstrate successful loading of existing HDF5 files and converted image stacks, while testing also identified performance reductions caused by excessive blank-slice generation. The updated workflow reduces reliance on external preparation tools and supports more direct movement from image stacks to color-code 3D visualization. Future work includes refining missing-slice handling, integrating additional preprocessing functions, like a denoising feature, parsing TIFF metadata for automatic voxel scaling, and adding manual X, Y, and Z voxel-spacing inputs for PNG and JPEG.

36 - MATERIALS SCIENCE↗
DOE OSTI2025

Data for "Discovery, Characterization, and Application of Chromosomal Integration Sites in the Hyperthermophilic Archaeon Sulfolobus islandicus"

Sulfolobus islandicus , an emerging archaeal model organism, offers unique advantages for metabolic engineering and synthetic biology applications owing to its ability to thrive in extreme environments. Although several genetic tools have been established for this organism, the lack of well-characterized chromosomal integration sites has limited its potential as a cellular factory. Here, we systematically identified and characterized 13 artificial CRISPR RNAs targeting eight integration sites in S. islandicus using the CRISPR-COPIES pipeline and a multi-omics-informed computational workflow. We leveraged the endogenous CRISPR-Cas system to integrate the reporter gene lacS and validated heterologous expression through a β-galactosidase assay, revealing significant positional effects. As a proof of concept, we utilized these sites to genetically manipulate lipid ether composition by overexpressing glycerol dibiphytanyl glycerol tetraether (GDGT) ring synthase B (GrsB). This study expands the genetic toolbox for S. islandicus and advances its potential as a robust platform for archaeal synthetic biology and industrial biotechnology.

AI/ML↗
DOE OSTI2015

Integration of microseismic monitoring data into coupled flow and geomechanical models with ensemble Kalman filter

Abstract Hydraulic stimulation of low‐permeability rocks in enhanced geothermal systems, shale resources, and CO 2 storage aquifers can trigger microseismic events, also known as microearthquakes (MEQs). The distribution of microseismic source locations in the reservoir may reveal important information about the distribution of hydraulic and geomechanical rock properties. In this paper, we present a framework for conditioning heterogeneous rock permeability and geomechanical property distributions on microseismic data. To simulate the multiphysics processes in these systems, we combine a fully coupled flow and geomechanical model with the Mohr‐Coulomb type rock failure criterion. The resulting multiphysics simulation constitutes the forecast model that relates microseismic source locations to reservoir rock properties. We adopt this forward model in an ensemble Kalman filter (EnKF) data assimilation framework to jointly estimate reservoir permeability and geomechanical property distributions from injection‐induced microseismic response measurements. We show that integration of a large number of spatially correlated microseismic data with practical ensemble sizes can lead to severe underestimation of ensemble spread, and eventually ensemble collapse. To mitigate the variance underestimation issue, two low‐rank data representation schemes are presented and discussed. In the first approach, microseismic data are projected onto a low‐dimensional subspace defined by the left singular vectors of the perturbed observation matrix. The second method uses a coarser grid for representing the microseismic data. A series of numerical experiments is presented to evaluate the performance of the proposed methods and to illustrate their applicability for assimilating microseismic data into coupled flow and geomechanical forward models to estimate multiphysics rock properties.

Tarrahi, Mohammadali↗
DOE OSTI2018

Design of machine learning models with domain experts for automated sensor selection for energy fault detection

Data-driven techniques that extract insights from sensor data reduce the cost of improving system energy performance through fault detection and system health monitoring. To lower cost barriers to widespread deployment, a methodology is proposed that takes advantage of existing sensor data, encodes expert knowledge about the application system to create ‘virtual sensors’ and applies statistical and mathematical methods to reduce the time required for manual configurations. The approach combines sensor data points with encoded expert knowledge that is generic to the application system but independent of a particular deployment, thereby reducing the need to tailor to individual deployments. Additionally, this paper not only presents a method that detects faults from measured energy data, but also (1) describes an engagement method with experts in the energy system domain to identify data, (2) integrates domain knowledge with the data, (3) automatically selects from among the large pool of potential input data, and (4) uses machine learning to automatically build a data-driven fault detection model. Demonstration on a commercial building chiller plant shows that only a small number of virtual sensors is necessary for fault detection with high accuracy rates. This corresponds to the use of only five out of 52 original sensor data points features. With as few as four features, classification F1 scores exceed 90% on the training set and 80% on the testing set. The results are implementable and realizable using off-the-shelf tools. The goal is to design with domain experts an energy monitoring system that can be configured once and then widely deployed with little additional cost or effort.

47 OTHER INSTRUMENTATION↗
DOE OSTI2022

Computational prediction of plant metabolic pathways

Uncovering genes encoding enzymes responsible for the biosynthesis of diverse plant metabolites is essential for metabolic engineering and production of plant metabolite-derived medicine. With the availability of multi-omics data for an ever-increasing number of plant species and the development of computational approaches, the metabolic pathways of many important plant compounds can be predicted, complementing a more traditional genetic and/or biochemical approach. In this work, we summarize recent progress in predicting plant metabolic pathways using genome, transcriptome, proteome, interactome, and/or metabolome data, and the utility of integrating these data with machine learning to further improve metabolic pathway predictions.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Deep Learning Advances Arctic River Water Temperature Predictions

The accelerated warming in the Arctic poses serious risks to freshwater ecosystems by altering streamflow and river thermal regimes. However, limited research on Arctic River water temperatures exists due to data scarcity and the absence of robust methodologies, which often focus on large, major river basins. To address this, we leveraged the newly released, extensive AKTEMP data set and advanced machine learning techniques to develop a Long Short-Term Memory (LSTM) model. By incorporating ERA5-Land reanalysis data and integrating physical understanding into data-driven processes, our model advanced river water temperature predictions in ungauged, snow- and permafrost-affected basins in Alaska. Our model outperformed existing approaches in high-latitude regions, achieving a median Nash-Sutcliffe Efficiency of 0.95 and root mean squared error of 1.0°C. The LSTM model learned air temperature, soil temperature, solar radiation, and thermal radiation—factors associated with energy balance—were the most important drivers of river temperature dynamics. Soil moisture and snow water equivalent were highlighted as critical factors representing key processes such as thawing, melting, and groundwater contributions. Glaciers and permafrost were also identified as important covariates, particularly in seasonal river water temperature predictions. Our LSTM model successfully captured the complex relationships between hydrometeorological factors and river water temperatures across varying timescales and hydrological conditions. This scalable and transferable approach can be potentially applied across the Arctic, offering valuable insights for future conservation and management efforts.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Analyzing the Multiscale Impacts of Implementing Energy-Efficient HVAC Improvements Through Energy Audits and Economic Input–Output Analysis

Abstract Heating, ventilation, and air-conditioning (HVAC) systems are usually an industry’s highest consumer of energy, most of which goes toward space cooling in buildings. Industrial energy-efficiency audits not only benefit manufacturers but also generate significant economic and environmental benefits to localities, states, and the nation. This article analyzes the micro- and macro scale impacts of implementing energy-efficient HVAC systems by integrating the industrial building energy data with the macroeconomic regional economic flow model. Micro-scale data include 10 years of historical energy, cost, and carbon dioxide savings achieved from energy-efficient HVAC implementation offered to manufacturers through industrial energy audits. The data were integrated into the macroeconomic modeling framework to illuminate the cascading regional economic impacts of implementing energy-efficient HVAC recommendations in manufacturing facilities. Results show that if recommendations had been implemented throughout all manufacturers in the region, $656 M energy costs would have been directly saved, 7.8 million metric tons of carbon dioxide emissions would have been avoided, and 4387 jobs could have been created, resulting in a total annual economic impact of $899 M stemming from direct, indirect, and induced impacts. The results offer insight into how industrial energy systems can be designed and provide models for how communities can accomplish a net-zero society.

Energy & Fuels↗
DOE OSTI2016

Frontier Observatory for Research in Geothermal Energy: Phase 1 Topical Report Fallon, NV

The Department of Energy (DOE) Frontier Observatory for Research in Geothermal Energy (FORGE) is to be a dedicated site where the subsurface scientific and engineering community can develop, test, and improve technologies and techniques for the creation of cost-effective and sustainable enhanced geothermal systems (EGS) in a controlled, ideal environment. The establishment of FORGE will facilitate development of an understanding of the key mechanisms controlling a successful EGS. Execution of FORGE is occurring in three phases with five distinct sub-phases (1, 2A, 2B, 2C, and 3). This report focuses on Phase 1 activities. During Phase 1, critical technical and logistical tasks necessary to demonstrate the viability of the Fallon FORGE Project site were completed and the commitment and capability of the Fallon FORGE team to execute FORGE was demonstrated. As part of Phase 1, the Fallon FORGE Team provided an assessment of available relevant data and integrated these geologic and geophysical data to develop a conceptual 3-D geologic model of the proposed test location. Additionally, the team prepared relevant operational plans for full FORGE implementation, provided relevant site data to the science and engineering community, engaged in outreach and communications with interested stakeholders, and performed a review of the environmental and permitting activities needed to allow FORGE to progress through Phase 3. The results of these activities are provided as Appendices to this report. The Fallon FORGE Team is diverse, with deep roots in geothermal science and engineering. The institutions and key personnel that comprise the Fallon FORGE Team provide a breadth of geoscience and geoengineering capabilities, a strong and productive history in geothermal research and applications, and the capability and experience to manage projects with the complexity anticipated for FORGE. Fallon FORGE Team members include the U.S. Navy, Ormat Nevada Inc., Sandia National Laboratories (SNL), Lawrence Berkeley National Laboratory (LBNL), the United States Geological Survey (USGS), the University of Nevada, Reno (UNR), GeothermEx/Schlumberger (GeothelinEx), and Itasca Consulting Group (Itasca). The site owners (through direct land ownership or via applicable permits)—the U.S. Navy and Ormat Nevada Inc.—are deeply committed to expanding the development of geothermal resources and are fully supportive of FORGE operations taking place on their lands.

15 GEOTHERMAL ENERGY↗
DOE OSTI2018

Cooperative Research and Development Agreement between University of Genoa and National Energy Technology Laboratory [Abstract]

The production, transportation, storage, and consumption of oil and gas resources rely on a global network of infrastructure, which is maintained and regulated by numerous commercial, non-profit, academia, and local to national government entities. Thus, information about this infrastructure is often highly variable in the quality, quantity and accessibility of the data. However, integrating and standardizing these disparate data sets into a global, open-access assessment and inventory of oil and gas infrastructure can provide a framework to support advanced analyses, including predictions and assessments of global methane infrastructure and emissions risk, identify information gaps, evaluate economic costs, and support a range of decision making needs. At present, there is no single point of access to authoritative, publicly available oil and gas infrastructure data. In addition, conventional online search approaches are increasingly inefficient as the volume and variety of open-data online continues to evolve and increase. Consequently, there exists a great deal of uncertainty surrounding the extent, gaps, and quality of infrastructure data across the world. This six-month effort used a combination of expert driven and machine learning techniques to build a virtual taxonomy of global oil and gas infrastructure to provide an open data foundation for addressing a range of stakeholder questions and needs.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2021

Geophysical Retrievals in an Artificial Intelligence (AI) Framework for Illuminating Processes Controlling Water Cycle

Focal Area(s): This white paper responds to Focal Area #3: Insight gleaned from complex data (both observed and simulated) using AI, big data analytics, and other advanced methods, including explainable AI and physics- or knowledge-guided AI. Science Challenge: This white paper addresses the water-cycle and data-model integration grand challenge. It leverages data from the Atmospheric Radiation Measurement (ARM) Climate Research Facility, and Next-Generation Ecosystem Experiment (NGEE), and Science Focus Area (SFA). The white paper focuses on controlling cloud, precipitation, and radiative properties as observed and simulated by the Earth System Models (ESM). The described framework can be readily applied to any other ensemble of instruments, including satellites and other ground-based networks.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Data Fusion to Enhance Quality Control and Analysis with Instruments at the Marine and Coastal Research Laboratory

Deploying environmental monitoring instruments in the marine environment can be challenging, facing challenges around device survivability, biofouling and corrosion, and consistent data collection. This project explores the use of data fusion – the process of integrating multiple data sources to produce more consistent, accurate, and useful information – to build a consistent long-term monitoring system at the Marine and Coastal Research Laboratory (MCRL) in Sequim, Washington. Unused instruments that had been acquired from past projects were inventoried and deployments planned on the MCRL pier and floating dock. A total of 8 instruments were deployed including a tide gauge, hydrophone, acoustic Doppler current profiler (ADCP), photosynthetically active radiation (PAR) sensors, meteorological station, and three water quality sensors. Deployments were planned to be well-protected around the pier structure and a maintenance schedule was created for cleaning and recalibration. An automated data pipeline was created to aggregate data on edge computers that push data to Amazon Web Services (AWS) cloud storage every 15 minutes, performing automated quality control and data transformations using the Time Series Data Analytical Toolkit (TSDAT). Continued efforts are underway to maintain this system into the future, take a data-driven approach to maintenance scheduling, improve the reliability of the system, and share the data with a variety of end-users.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS1978

Optical and holographic storage properties of F3, Cu, and Mg-doped lithium niobate

Several samples of iron, copper, and magnesium doped lithium niobate were tested to determine their storage properties which would be applicable to an optical data storage system and an integrated optics data preprocessor which makes use of holographic storage techniques. The parameters of interest were the diffraction efficiency, write power, write time, erase time, erase energy, and write sensitivity. Results of these parameters are presented. It was found that iron doped lithium niobate samples yielded the best results in all parameters except for a few percent higher diffraction efficiency in copper doped samples. The magnesium doped samples were extremely insensitive and are not recommended for use in holographic optical data storage and processing systems.

Beatty, M. E., III↗
NASA NTRS1985

Optical detection of large meteoroids in space

CCD sensors placed across the focal plane of large Schmidt telescopes have great potential for detecting and measuring the very low flux in space of meteoroids with diameters larger than 1 meter. With the Palomar 'Big Schmidt', a detection rate of 1.4 per hour is obtained for meteoroids between 0.6 and 200 meters in diameter. For the Baker-Nunn 'Satellite Tracking Camera', the corresponding rate is about 0.8 per hour. The key to obtaining such high detection rates derives from approximately setting the sensor integration time equal to the time it takes a meteoroid to cross a pixel field of view. This minimizes signal to noise problems and is accomplished, in practice, by multiple summing of short integration time data records to obtain data records of longer effective integration times.

Zook, H. A.↗
NASA NTRS1988

Research on enhancing the utilization of digital multispectral data and geographic information systems in global habitability studies

During 1986 to 1987, the Kansas Applied Remote Sensing (KARS) Program continued to build upon long-term research efforts oriented towards enhancement and development of technologies for using remote sensing in the inventory and evaluation of land use and renewable resources (both natural and agricultural). These research efforts directly addressed needs and objectives of NASA's Land-Related Global Habitability Program as well as needs of and interests of public agencies and private firms. The KARS Program placed particular emphasis on two major areas: development of intelligent algorithms to improve automated classification of digital multispectral data; and integrating and merging digital multispectral data with ancillary data in spatial modes.

Martinko, Edward A.↗
NASA NTRS1991

Effect of a simulated ice accretion on the aerodynamics of a swept wing

The effect of a simulated glaze ice accretion on the aerodynamic performance of a three-dimensional swept wing is studied experimentally. A semispan wing of effective aspect ratio four was mounted from the sidewall of the UIUC subsonic wind tunnel. The model uses a NACA 0012 airfoil section on a rectangular planform with interchangeable tip and root sections to allow for 0- and 30-degree sweep. A sidewall suction system is used to minmize the tunnel boundary-layer interaction with the model. Surface pressure data from five spanwise stations are compared to earlier data from a similar tunnel. A three-component sidewall balance has been designed, built and used to measure lift, drag and pitching moment on the clean and iced model. The data compare well to the integrated pressure data and to theory on the clean model. In addition, helium-bubble flow visualization has been performed on the iced model and reveals extensive spanwise flow in the separation bubble aft of the upper surface horn. This compares well to the computational results of other researchers. Sidewall suction was found to have no effect on the aerodynamics of the swept wing.

Bragg, M. B.↗
NASA NTRS1993

Recommendations resulting from the SPDS Community-Wide Workshop

The Data Systems Panel identified three critical functionalities of a Space Physics Data System (SPDS): the delivery of self-documenting data, the existence of a matrix of translators between various standard formats (IDFS, CDF, netCDF, HDF, TENNIS, UCLA flat file, and FITS), and a network-based capability for browsing and examining inventory records for the system's data holdings. The recommendations resulting from the workshop include the philosophy, funding, and objectives of a SPDS. Access to quality data is seen as the most important objective by the Policy Panel, with curation and information about the data being integral parts of any accessible data set. The Data Issues Panel concluded that the SPDS can supply encouragement, guidelines, and ultimately provide a mechanism for financial support for data archiving, restoration, and curation. The Software Panel of the SPDS focused on defining the requirements and priorities for SPDS to support common data analysis and data visualization tools and packages.

Source record↗
NASA NTRS2003

Rapid Technology Assessment via Unified Deployment of Global Optical and Virtual Diagnostics

This paper discusses recent developments in rapid technology assessment resulting from an active collaboration between researchers at the Air Force Research Laboratory (AFRL) at Wright Patterson Air Force Base (WPAFB) and the NASA Langley Research Center (LaRC). This program targets the unified development and deployment of global measurement technologies coupled with a virtual diagnostic interface to enable the comparative evaluation of experimental and computational results. Continuing efforts focus on the development of seamless data translation methods to enable integration of data sets of disparate file format in a common platform. Results from a successful low-speed wind tunnel test at WPAFB in which global surface pressure distributions were acquired simultaneously with model deformation and geometry measurements are discussed and comparatively evaluated with numerical simulations. Intensity- and lifetime-based pressure-sensitive paint (PSP) and projection moire interferometry (PMI) results are presented within the context of rapid technology assessment to enable simulation-based R&D.

Jordan, Jeffrey D.↗
NASA NTRS2006

Ground Processing of Data From the Mars Exploration Rovers

A computer program implements the Earth side of the protocol that governs the transfer of data files generated by the Mars Exploration Rovers. It also provides tools for viewing data in these files and integrating data-product files into automated and manual processes. It reconstitutes files from telemetry data packets. Even if only one packet is received, metadata provide enough information to enable this program to identify and use partial data products. This software can generate commands to acknowledge received files and retransmit missed parts of files, or it can feed a manual process to make decisions about retransmission. The software uses an Extensible Markup Language (XML) data dictionary to provide a generic capability for displaying files of basic types, and uses external "plug-in" application programs to provide more sophisticated displays. This program makes data products available with very low latency, and can trigger automated actions when complete or partial products are received. The software is easy to install and use. The only system requirement for installing the software is a Java J2SE 1.4 platform. Several instances of the software can be executed simultaneously on the same machine.

Wright, Jesse↗