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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 OSTI2022

Quantitative Metrics for Grid Resilience Evaluation and Optimization

Power system resilience has become a critical topic in recent years because of the increasing trend of extreme events and the growing integration of intermittent renewable energy sources. To enhance grid resilience against high-impact, low-frequency events, two questions should be answered: how to quantify the resilience of a given grid and how to incorporate the quantification into power system planning, operation, and restoration. Here this paper develops a new set of quantitative metrics with clear physical interpretation to comprehensively evaluate power system resilience. Using microgrids as an example, an event-based corrective scheduling (ECS) model and an online model predictive control (OMPC) model are developed to integrate the proposed quantitative resilience metrics into power system optimization models for resilience enhancement. The ECS model employs extreme event data to investigate the optimal restoration solution and to help microgrid operators prepare to respond to similar events. The OMPC model provides online decision-making support for operators to handle ongoing outages in the most resilient fashion. The effectiveness and superiority of the proposed quantitative resilience metrics and the resilience enhancement models are demonstrated through simulations and comparative studies on an IEEE test feeder and a real distribution feeder in Southern California.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2025

Hazard Detection Detector Cards

This report presents a comprehensive summary of five advanced anomaly detection tools developed and deployed by Oak Ridge National Laboratory in support of the VA’s Health Information Technology modernization. These detectors—Order Path Tracker, Trend Watcher, Pain Pointer, Performance Monitor, and Patient Record Flag Detector—leverage statistical and machine learning methods to monitor workflow disruptions, detect anomalies in care sequences and volumes, identify bottlenecks, and track system-level performance metrics across VistA and Millennium systems. All detectors have been integrated into the Health Data Analytics Platform (HDAP), with most having completed deployment and testing using live data from targeted stations in cardiology and oncology domains. This work enhances VA’s capacity for proactive system surveillance, promotes patient safety, and informs data-driven operational improvements across the EHR ecosystem.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS2018

MODIS and VIIRS On-Orbit Calibration and Characterization Using Observations from Spacecraft Pitch Maneuvers

Two MODIS instruments (Terra and Aqua) and two VIIRS instruments (S-NPP and JPSS-1) are currently operated inspace, continuously making global earth observations in the spectral range from visible (VIS) to long-wave infrared(LWIR). These observations have enabled a broad range of environmental data records to be generated and distributed insupport of both operational and scientific community. Despite extensive pre-launch calibration and characterizationperformed for both MODIS and VIIRS instruments and routine on-orbit calibration activities carried out using their onboardcalibrators (OBC), various spacecraft maneuvers have also been designed and implemented to further enhance thesensor on-orbit calibration and data quality. This paper focuses on the use of observations made during spacecraft pitchmaneuvers of MODIS and VIIRS in support of their on-orbit characterization of thermal emissive bands (TEB) responseversus scan-angle (RVS). In the case of Terra MODIS, lunar observations made from instrument nadir view duringspacecraft pitch maneuvers are used to compare with that made regularly through instrument space view (SV) port toevaluate on-orbit changes in RVS and band-to-band registration (BBR) for the reflective solar bands (RSB). In additionto results derived from spacecraft pitch maneuvers performed for MODIS and VIIRS, discussion is provided on theadvantages, challenges, and lessons for future considerations and improvements.

Xiong, Xiaoxiong↗
DOE OSTI2025

FL‐ADS: Federated learning anomaly detection system for distributed energy resource networks

Abstract With the ongoing development of Distributed Energy Resources (DER) communication networks, the imperative for strong cybersecurity and data privacy safeguards is increasingly evident. DER networks, which rely on protocols such as Distributed Network Protocol 3 and Modbus, are susceptible to cyberattacks such as data integrity breaches and denial of service due to their inherent security vulnerabilities. This paper introduces an innovative Federated Learning (FL)‐based anomaly detection system designed to enhance the security of DER networks while preserving data privacy. Our models leverage Vertical and Horizontal Federated Learning to enable collaborative learning while preserving data privacy, exchanging only non‐sensitive information, such as model parameters, and maintaining the privacy of DER clients' raw data. The effectiveness of the models is demonstrated through its evaluation on datasets representative of real‐world DER scenarios, showcasing significant improvements in accuracy and F1‐score across all clients compared to the traditional baseline model. Additionally, this work demonstrates a consistent reduction in loss function over multiple FL rounds, further validating its efficacy and offering a robust solution that balances effective anomaly detection with stringent data privacy needs.

Purohit, Shaurya [Iowa State University Ames Iowa ↗
DOE OSTI2025

VA EDH Advanced Software Pipeline Framework Report: Enhancing Automation and Scalability

The VA Environmental Determinants of Health (EDH) Advanced Software Pipeline Framework is designed to enhance the efficiency, scalability, and security of geospatial data processing workflows. This framework integrates modern data orchestration and containerization technologies, including Prefect for workflow automation, Docker for containerization, and PostgreSQL/PostGIS for geospatial data storage and analysis. It ensures standardized, reproducible, and automated data processing, supporting VA objectives related to substance use risk assessment and recovery research. The pipeline addresses key scalability and performance challenges through horizontal and vertical scaling, high-performance computing (HPC) integration, parallel processing, task caching, and dynamic resource allocation. These optimizations improve throughput and reduce latency, allowing the system to efficiently manage large and complex datasets. Additionally, security and compliance measures—such as data encryption (SSL), Role-Based Access Control (RBAC), and adherence to GDPR and HIPAA standards—safeguard sensitive information throughout data transmission and storage. A key implementation of this framework includes the automation of shelter list geolocation workflows, ensuring that up-to-date data is readily available for VA decision-making. Lessons learned from this project include the transition from in-memory processing to incremental storage writes, improving resource management and reliability. Future enhancements aim to expand automation, integrate AI-driven anomaly detection, and incorporate high-performance computing resources. This framework provides a scalable, secure, and adaptable solution for managing geospatial datasets, reinforcing the VA’s ability to support clinical and strategic initiatives through data-driven decision-making.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

S AP F LOWER : an automated tool for sap flow data preprocessing, gap-filling, and analysis using deep learning

Sap flow, a critical process in plant water use and ecosystem water cycles, is often measured using thermal dissipation probes (TDP) due to their ease of installation and continuous data collection. However, sap flow data frequently include noise, outliers, and gaps, creating challenges for analysis and requiring substantial manual processing. We developed S AP F LOWER , a tool that automates data preprocessing, model training, gap-filling, sapwood area scaling and modeling, and water use analysis. It integrates autocleaning, machine learning and deep learning models (e.g. random forest, Gaussian process regression, long short-term memory (LSTM), bidirectional LSTM (BiLSTM)), and efficient workflows to process sap flow data. S AP F LOWER can remove over 90% of noisy data while preserving legitimate variations and achieve high accuracy in gap-filling based on user-determined parameters. Random forest, LSTM, and BiLSTM models reduced root mean square error to 10% or less for long-term gaps. Model training and prediction can be performed efficiently within seconds. S AP F LOWER significantly enhances the efficiency and accessibility of TDP data analysis by automating complex tasks, enabling researchers without programming expertise to employ advanced techniques. Future improvements will focus on species-specific corrections for TDP and support for additional measurement methods. S AP F LOWER is openly available on GitHub (https://github.com/JiaxinWang123/SapFlower) and Zenodo (doi: 10.5281/zenodo.13665919).

ecosystem water balance↗
NASA NTRS1988

Influence of the Yukon River on the Bering Sea

Physical and biological oceanography of the northern Bering Sea including the influence of the Yukon River were studied. Satellite data acquired by the Advanced Very High Resolution Radiometer (AVHRR), the LANDSAT Multispectral Scanner (MSS) and the Thematic Mapper (TM) sensor were used to detect sea surface temperatures and suspended sediments. Shipboard measurements of temperature, salinity and nutrients were acquired through the Inner Shelf Transfer and Recycling (ISHTAR) project and were compared to digitally enhanced and historical satellite images. The satellite data reveal north-flowing, warm water along the Alaskan coast that is highly turbid with complex patterns of surface circulation near the Yukon River delta. To the west near the Soviet Union, cold water, derived from an upwelling, mixes with shelf water and also flows north. The cold and warm water coincide with the Anadyr, Bering Shelf and Alaskan coastal water masses. Generally, warm Alaskan coastal water forms near the coast and extends offshore as the summer progresses. Turbid water discharged by the Yukon River progresses in the same fashion but extends northward across the entrance to Norton Sound, attaining its maximum surface extent in October. The Anadyr water flows northward and around St. Lawrence Island, but its extent is highly variable and depends upon mesoscale pressure fields in the Arctic Ocean and the Bering Sea.

Dean, Kenneson G.↗
NASA NTRS1972

Comparative evaluation of ERTS-A imagery for resource inventory in land-use planning

There are no author-identified significant results in this report. The overall objectives of this program are: (1) use of multidiscipline team approach to determine features that can be successfully monitored by ERTS-1 imagery for resource inventory, planning, land use zoning, and resource development; and (2) using carefully selected sample areas, develop a comprehensive resource inventory mapping system for use in planning, zoning, and resource development. Progress has included compilation and organization of ground truth data and observations in the primary study area of Crook County; resource inventory legend development; assembly and testing of color enhancement equipment; development and adaption of programs for digital data processing; and quick-look evaluations of initial ERTS-1 imagery for Oregon.

Simonson, G. H.↗
NASA NTRS2005

Managed Development Environment Successes for MSFC's VIPA Team

This paper outlines the best practices of the Vehicle Design Team for VIPA. The functions of the VIPA Vehicle Design (VVD) discipline team are to maintain the controlled reference geometry and provide linked, simplified geometry for each of the other discipline analyses. The core of the VVD work, and the approach for VVD s first task of controlling the reference geometry, involves systems engineering, top-down, layout-based CAD modeling within a Product Data Manager (PDM) development environment. The top- down approach allows for simple control of very large, integrated assemblies and greatly enhances the ability to generate trade configurations and reuse data. The second VVD task, model simplification for analysis, is handled within the managed environment through application of the master model concept. In this approach, there is a single controlling, or master, product definition dataset. Connected to this master model are reference datasets with live geometric and expression links. The referenced models can be for drawings, manufacturing, visualization, embedded analysis, or analysis simplification. A discussion of web based interaction, including visualization, between the design and other disciplines is included. Demonstrated examples are cited, including the Space Launch Initiative development cycle, the Saturn V systems integration and verification cycle, an Orbital Space Plane study, and NASA Exploration Office studies of Shuttle derived and clean sheet launch vehicles. The VIPA Team has brought an immense amount of detailed data to bear on program issues. A central piece of that success has been the Managed Development Environment and the VVD Team approach to modeling.

Finckenor, Jeff↗
NASA NTRS1990

Cyclotron lines - The next 100 years

The relationship between cyclotron lines and other spectral features of stars requires a numerical construction of theoretical spectra and numerical comparison of simulated and actual data. These techniques - combined with supercomputer technology - can enhance the study of X-ray and gamma-ray sources. The present understanding of the behavior of the rays is set forth, and the application of data from cyclotron line formation is suggested to examine accreting X-ray sources and gamma-ray bursts in neutron stars.

Wasserman, Ira↗
NASA NTRS1995

Computerized design of controllers using data models

The major contributions of the grant effort have been the enhancement of the Compensator Improvement Program (CIP), which resulted in the Ohio University CIP (OUCIP) package, and the development of the Model and Data-Oriented Computer Aided Design System (MADCADS). Incorporation of direct z-domain designs into CIP was tested and determined to be numerically ill-conditioned for the type of lightly damped problems for which the development was intended. Therefore, it was decided to pursue the development of z-plane designs in the w-plane, and to make this conversion transparent to the user. The analytical development needed for this feature, as well as that needed for including compensator damping ratios and DC gain specifications, closed loop stability requirements, and closed loop disturbance rejection specifications into OUCIP are all contained in Section 3. OUCIP was successfully tested with several example systems to verify proper operation of existing and new features. The extension of the CIP philosophy and algorithmic approach to handle modern multivariable controller design criteria was implemented and tested. Several new algorithms for implementing the search approach to modern multivariable control system design were developed and tested. This analytical development, most of which was incorporated into the MADCADS software package, is described in Section 4, which also includes results of the application of MADCADS to the MSFC ACES facility and the Hubble Space Telescope.

Irwin, Dennis↗
DOE OSTI2022

Enhancing Worker Safety During Off-Site Construction of Energy-Efficient, Low-Carbon Exterior Envelope

The construction industry is not doing enough to embrace off-site modular construction. It is a missed opportunity for optimized energy efficiency because prefabricated exterior wall envelopes are lower in cost and faster and easier to install. Factory off-site construction offers improved safety for construction workers because work is done in a controlled environment, but lack of intent, understanding, and data is also a missed opportunity for enhancing worker safety. This research looks specifically at the prefabricated wall exterior envelope in the context of safety. The research question is "off-site factories build with ease of energy efficiency and lowered carbon emissions, but is the process safer?" This research was funded by NREL Student Training in Applied Research (STAR) program, University Partnerships Program, and Wells Fargo Bank. Key finding is that modular and prefabricated construction are perceived to be safer, but the accident rates are much higher than in the general construction and manufacturing industries. Proposed solutions to decrease accidents in off-site prefabricated construction includes safety training for workers, provide movable kiosks with PPE at workstations, ensure tools are well-maintained and place safety posters in visible locations. Future work includes improving worker safety to increase worker productivity, perform pilot demonstration, work with factory designers for new modular layout for worker safety, i.e., easy access to PPE kiosks, and work closely with OSHA so that they take notice of this safety niche.

drywall↗
DOE OSTI2021

Chemically Enabled CO 2 -Enhanced Oil Recovery in Multi-Porosity, Hydrothermally Altered Carbonates in the Southern Michigan Basin - Task 2 Topical Report

This attachment A is a detailed Topical Report for Task 2 (Advanced Field Characterization and Machine Learning Based Data Integration) under the project "Chemically Enabled CO 2 -Enhanced Oil Recovery in Multi-Porosity, Hydrothermally Altered Carbonates in the Southern Michigan Basin." The overall project activity and finding, including the field pilot testing of CO 2 injection are summarized in the companion Final Technical Report The Advanced Field Characterization and Machine Learning Based Data Integration task (Task 2) involved a systematic geologic characterization of the Trenton-Black River (TBR) play in the SMB which included the development of comprehensive datasets, advanced data analytics, risk assessment, and piggyback field characterization. These activities aimed to address the complex carbonate systems by evaluating the extent of fractures, dolomitization, facies, and reservoir properties with the primary objective of informing the static and dynamic modeling, field injection test, and providing input into the development strategy plan. The task was divided into four subtasks: • Subtask 2.1 – Data compilation, review, and analysis • Subtask 2.2 – Risk Assessment • Subtask 2.3 – Advanced Field Characterization • Subtask 2.4 – Integrated Physics-Based Machine Learning and Advanced Data Analytics

02 PETROLEUM↗
NASA NTRS2007

Toward an Objective Enhanced-V Detection Algorithm

The area of coldest cloud tops above thunderstorms sometimes has a distinct V or U shape. This pattern, often referred to as an "enhanced-V' signature, has been observed to occur during and preceding severe weather in previous studies. This study describes an algorithmic approach to objectively detect enhanced-V features with observations from the Geostationary Operational Environmental Satellite and Low Earth Orbit data. The methodology consists of cross correlation statistics of pixels and thresholds of enhanced-V quantitative parameters. The effectiveness of the enhanced-V detection method will be examined using Geostationary Operational Environmental Satellite, MODerate-resolution Imaging Spectroradiometer, and Advanced Very High Resolution Radiometer image data from case studies in the 2003-2006 seasons. The main goal of this study is to develop an objective enhanced-V detection algorithm for future implementation into operations with future sensors, such as GOES-R.

Brunner, Jason↗
DOE OSTI2024

Transformer-powered surrogates close the ICF simulation-experiment gap with extremely limited data

Abstract Recent advances in machine learning, specifically transformer architecture, have led to significant advancements in commercial domains. These powerful models have demonstrated superior capability to learn complex relationships and often generalize better to new data and problems. This paper presents a novel transformer-powered approach for enhancing prediction accuracy in multi-modal output scenarios, where sparse experimental data is supplemented with simulation data. The proposed approach integrates transformer-based architecture with a novel graph-based hyper-parameter optimization technique. The resulting system not only effectively reduces simulation bias, but also achieves superior prediction accuracy compared to the prior method. We demonstrate the efficacy of our approach on inertial confinement fusion experiments, where only 10 shots of real-world data are available, as well as synthetic versions of these experiments.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS1984

Rocket observations of the ionosphere during the eclipse of 26 February 1979

Electron density profiles and energetic particle fluxes were determined from two rockets launched, respectively, at the beginning and end of totality during the solar eclipse of 26 February 1979. These, and one other rocket at the same time of day on 24 February 1979, were launched from near Red Lake, Ontario. The electron density profile from 24 February shows the electron density to be normal above 110 km, to rocket apogee. Below 110 km, the electron density is enhanced, by an order of magnitude in the D region, compared with data from Wallops Island at the same solar zenith angle (63 deg). The enhancement is qualitatively explained by the large flux of field aligned energetic particles observed on the same rocket. During totality (on 26 February) the electron density above 110 km to rocket apogee is reduced by a factor of about three. Below 110 km, the electron density is much greater than observed during previous eclipses. The particle flux measured on the 26 February was an order of magnitude less than that on the 24 February but showed greater variability, particularly at the higher energies (100 keV). A feature of the particle flux is that, for the two rockets that were separated horizontally by 38 km while above the absorbing region, the variations are uncorrelated.

Mcinerney, M. K.↗
NASA NTRSDate not supplied

Application of Lidar-Radar Combined Data Products during the IMPACTS 2020 Field Campaign

The Investigation of Microphysics and Precipitation for Atlantic Coast-Threatening Snowstorms (IMPACTS) is a three-year field campaign investigating the formation and lifecycle of banded, precipitation structures within intense wintertime cyclones impacting the highly-populated northeast United States. Unlike previous campaigns in the region, IMPACTS mission scientists have access to and coordinate the NASA ER-2 and P-3 aircraft to collect observations from both a remote sensing and in-situ perspective, respectively. For IMPACTS, the ER-2 aircraft was equipped with lidar (CPL), radar (CRS, HIWRAP, EXRAD), microwave radiometer (CoSMIR, AMPR) instruments, each with their own unique capabilities and limitations. Our on-going work effort aims to develop IMPACTS lidar and radar merged data products, which can be used to supplement the raw data sources, tease out additional aspects of the mechanisms underpinning wintertime cyclones, and enhance our understanding of microphysical properties in sensor overlap regions. Preliminary merged data products combine CPL lidar with any one of the NASA High Altitude Radar (HAR) radar products (CRS, HIWRAP, EXRAD) and represent these data as both normalized values and translated to reflectivity signal (dB). Continuing work will further refine our merged data product algorithm to provide additional information on hydrometeor species identification, ice crystal habit, and ice-water content, which will help IMPACTS achieve its mission goal of improving microphysical properties retrievals from both airborne and spaceborne platforms.

Stephen D. Nicholls↗

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