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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 235 records · Page 13
All sourcesNASA NTRSDOE OSTI
DOE OSTI2012

Stationary Fuel Cell Evaluation (Presentation)

This powerpoint presentation discusses its objectives: real world operation data from the field and state-of-the-art lab; collection; analysis for independent technology validation; collaboration with industry and end users operating stationary fuel cell systems and reporting on technology status, progress and technical challenges. The approach and accomplishments are: A quarterly data analysisand publication of first technical stationary fuel cell composite data products (data through June 2012).

08 HYDROGEN↗
DOE OSTI2016

Test Data Monitor

The National Security Campus (NSC) collects a large amount of test data used The National Security Campus (NSC) collects a large amount of test data used to accept high value and high rigor product. The data has been used historically to support root cause analysis when anomalies are detected in down-stream processes. The opportunity to use the data for predictive failure analysis however, had never been exploited. The primary goal of the Test Data Monitor (TDM) software is to provide automated capabilities to analyze data in near-real-time and report trends that foreshadow actual product failures. To date, the aerospace industry as a whole is challenged at utilizing collected data to the degree that modern technology allows. As a result of the innovation behind TDM, Honeywell is able to monitor millions of data points through a multitude of SPC algorithms continuously and autonomously so that our personnel resources can more efficiently and accurately direct their attention to suspect processes or features. TDM’s capabilities have been recognized by our U.S. Department of Energy National Nuclear Security Administration (NNSA) sponsor for potential use at other sites within the NNSA. This activity supports multiple initiatives including expectations of the NNSA and broader corporate goals that center around data-based quality controls on production.

Bosas, Joseph↗
DOE OSTI2018

Advanced Data Structures for Improved Cyber Resilience and Awareness in Untrusted Environments: LDRD Report

This report summarizes the work performed under the project "Advanced Data Structures for Improved Cyber Resilience and Awareness in Untrusted Environments." The goal of the project was to design, analyze, and test new data structures for cybersecurity applications. We had two major thrusts: 1) using new/improved write-optimized data structures and/or algorithms to better man- age and analyze high-speed massive-volume cyberstreams, and 2) adding security features to data structures at minimum cost. Write optimization allows data structures to better use secondary memory to store and search a larger amount of useful information. Secondary memory is large compared to main memory, but data movement to and from secondary memory must be carefully managed to run quickly enough to keep up with fast streams. The first thrust included managing cyberstreams in parallel, both multi-threaded and distributed, and improving the benchmarking infrastructure for testing new streaming data structures. We considered both (near) real-time discovery of particular patterns, and improved logging for improved forensics. We considered two kinds of security-feature problem. The first was high-performance history- independent external-memory data structures. These provide certain protections to data if a disk is stolen. We also prove some trade-offs between speed and security in this setting. The second data-security problem is more secure data look-up in secret-shared data bases. This report summarizes the project's major accomplishments, with the background to under- stand these accomplishments. It gathers the abstracts and references for the six refereed publications that have appeared as part of this work. We summarize several accomplishments that will be submitted for publication. We then archive one piece of partial work that is not likely to be published in the near future: validation of history-independent data structure implementations.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Tactical Analysis for Calculating Contextual Risk at Boundaries: Summary of Laboratory Directed Research & Development Effort

The Tactical Analysis for Calculating Contextual Risk at Boundaries (TACCRAB) tool is an innovative digital twin (DT) platform and automated risk algorithm designed to transform operational decision-making in structured screening environments, with an initial focus on Southern Border Land Ports of Entry (POEs). The invention provides integration points for advanced artificial intelligence, predictive modeling, and real-time data analysis to produce a comprehensive risk management tool that enables proactive, data-informed security strategies. The core inventive features of TACCRAB center on its unique risk algorithm, which dynamically calculates contextual risk by synthesizing historical data, near real-time streaming data from the checkpoints themselves, and AI-generated predictions. Unlike traditional risk assessment methods, TACCRAB utilizes a DT to provide comprehensive operational insights, allowing stakeholders to visualize, simulate, and optimize checkpoint configurations with unprecedented speed and contextual awareness. TACCRAB's key innovation lies in its ability to combine multiple complex inputs - including technology detection probabilities, resource availability, screening pathway characteristics, and threat actor behavioral patterns - into a unified risk calculation and update these inputs based on changing operational and environmental conditions. By leveraging a DT that continuously updates and learns from linked data, TACCRAB can suggest adaptive mitigation strategies that minimize risk while maintaining operational efficiency. Particularly novel is the platform's approach to decision support, which goes beyond static risk assessment. The DT provides dynamic metrics such as wait times, resource allocation effectiveness, and potential emerging threat scenarios, enabling users to view sophisticated, relevant what-if simulations and optimize checkpoint operations in near real-time. The system's architecture allows for generalized application across different screening environments, such as secure facilities, ports of entry, and soft targets, making it a versatile tool for security and operational management. The invention distinguishes itself through its comprehensive integration of predictive modeling, AI-driven pattern discovery, and user-friendly interface design. By combining these elements, TACCRAB transforms complex risk data into actionable insights, supporting decision-makers at various organizational levels - from booth agents making split-second screening decisions to checkpoint managers optimizing the day's resource allocation to strategic planners managing long-term investments.

45 MILITARY TECHNOLOGY, WEAPONRY, AND NATIONAL DEF↗
DOE OSTI2017

Standard Energy Efficiency Data Platform® [SWR-17-25]

The Standard Energy Efficiency Data Platform® (SEED Platform) provides public agencies and other organizations with a standardized but flexible, cost-effective, secure, enterprise data platform to manage portfolio scale building performance data from a variety of sources. The SEED Platform streamlines the administrative effort required by public agencies implementing building performance and transparency programs.

Source record↗
DOE OSTI2015

Developing a 3-D Seismic-Attribute Framework Model of Yucca Flat, Nevada National Security Site

Using data collected during underground nuclear testing in Yucca Flat at the Nevada National Security Site, an existing 3-D hydrostratigraphic framework model (HFM) that was developed for groundwater modeling is being converted to a 3-D seismic-attribute framework model (SFM). The resulting SFM can be used to better understand seismic wave propagation from underground chemical explosive tests planned for Yucca Flat as part of the Source Physics Experiment. The Yucca Flat HFM covers 1,250 square kilometers and depicts the distribution of subsurface geologic units, called hydrostratigraphic units, according to their ability to transmit groundwater. Many of the rock properties and geologic structures that control groundwater flow also influence seismic wave transmission, so the team reasoned that the HFM could be modified to create an SFM. Existing geologic and geophysical data, including interval velocity measurements from down-hole seismic surveys conducted in 188 holes across Yucca Flat, were used to evaluate the HFM and develop a 3-D velocity property model. The velocity model was integrated with the HFM so that unit volumes within the HFM could be evaluated according to their seismic character. Results to date show, as anticipated, that most of the 3-D hydrostratigraphic unit volumes in the HFM can function as seismic-stratigraphic unit volumes. Thus, with relatively minor modifications, the HFM can be converted to an SFM.

seismic, Yucca Flat, model↗
DOE OSTI2022

Data Science and Machine Learning for Genome Security

This report describes research conducted to use data science and machine learning methods to distinguish targeted genome editing versus natural mutation and sequencer machine noise. Genome editing capabilities have been around for more than 20 years, and the efficiencies of these techniques has improved dramatically in the last 5+ years, notably with the rise of CRISPR-Cas technology. Whether or not a specific genome has been the target of an edit is concern for U.S. national security. The research detailed in this report provides first steps to address this concern. A large amount of data is necessary in our research, thus we invested considerable time collecting and processing it. We use an ensemble of decision tree and deep neural network machine learning methods as well as anomaly detection to detect genome edits given either whole exome or genome DNA reads. The edit detection results we obtained with our algorithms tested against samples held out during training of our methods are significantly better than random guessing, achieving high F1 and recall scores as well as with precision overall.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2012

Analysis Results for ARRA Projects: Enabling Fuel Cell Market Transformation (Presentation)

This presentation discusses analysis results for American Recovery and Reinvestment Act early market fuel cell deployments and describes the objective of the project and its relevance to the Department of Energy Hydrogen and Fuel Cells Program; NREL's analysis approach; technical accomplishments including publication of a fourth set of composite data products; and collaborations and future work.

08 HYDROGEN↗
DOE OSTI2022

Human Performance Analysis Depending on Operator Expertise (Student vs. Operator) and Simulator Complexity (Rancor Microworld vs. Compact Nuclear Simulator)

Human reliability analysis (HRA) evaluates human errors and provides human error probabilities (HEPs) for application in probabilistic safety assessment (PSA), which is a comprehensive safety assessment method for nuclear power plants (NPPs). Generally, HRA methods estimate HEPs based on human reliability data collected from actual historical measurement, simulator experiments, or expert judgement. Most recent HRA data collection studies focus on collecting data via full-scope main control room (MCR) simulators with actual licensed reactor operators. Contrary to this, Idaho National Laboratory (INL) has adopted a different approach, which attempts to collect HRA data based on experiment using simplified simulators and student participants by following the Simplified Human Error Experimental Program (SHEEP). This approach has a couple of advantages compared to full-scope data collection. Representatively, it has relatively low entry point for collecting HRA data, and secures large sample sizes with reasonable cost and labor. In the previous studies, we developed the SHEEP framework, then verified whether the data collected through the framework could support a representative full-scope data collection study, i.e., the Human Reliability Data Extraction (HuREX) study. Also, we analyzed human performance measurements depending on participant type (i.e., student vs. operator). In this paper, we analyze human performance data collected from an experiment comparing operator expertise and simulator complexity when using the more simplified simulator developed by INL, i.e., Rancor Microworld and the less simplified simulator, i.e., Compact Nuclear Simulator (CNS) developed by Korea Atomic Energy Research Institute (KAERI). Analysis of variance (ANOVA) tests and correlation analysis are used for analyzing the experimental data.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2023

Electrical substation grid testbed for DLT applications of electrical fault detection, power quality monitoring, DERs use cases and cyber-events

Electrical utilities continue to deploy more intelligent electronic devices (IEDs) inside and outside electrical substation, and are associated with customer-owned distributed energy resources (DERs). The integrity and confidentiality of data from these IEDs, like power meters and protective relays, is crucial. Blockchain technology could improve the resilience of microgrids by improving the security of data sharing. The penetration of customer-owned DERs (renewable energy sources) and the increasing deployment of IEDs can lead to integrate power system applications with Distributed Ledger Technology (DLT). In this study, we implemented the electrical faulted phase detection and power quality monitoring algorithms with a Cyber Grid Guard (CGG) system using DLT. In addition, the DERs (wind turbine farms) use case and protective relay cyber-event tests were assessed, by using the CGG system with DLT. In the experimental model, the testbed was created by using a real-time simulator and CGG system with power meters/ protective relays in-the-loop. The data collected from the CGG system and IEDs were compared with the same time stamp source. These results had shown the successful assessment of protection, control and monitoring applications using a CGG system with DLT. In the future, the ESGT with DERs and the CGG system will be used in other power system applications, based on implementing smart contracts between electrical utilities with customer-owned DERs.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2012

Controlled Hydrogen Fleet and Infrastructure Analysis (Presentation)

This is a presentation about the Fuel Cell Electric Vehicle Learning Demo, a 7-year project and the largest single FCEV & infrastructure demonstration in the world to date. Information such as its approach, technical accomplishments and progress; collaborations and future work are discussed.

08 HYDROGEN↗
DOE OSTI2016

Practical challenges in quantum key distribution

Here, quantum key distribution (QKD) promises unconditional security in data communication and is currently being deployed in commercial applications. Nonetheless, before QKD can be widely adopted, it faces a number of important challenges such as secret key rate, distance, size, cost and practical security. Here, we survey those key challenges and the approaches that are currently being taken to address them.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

WRS Capabilities Booklet [Slides]

WRS is the digital backbone of the Weapons Program—delivering trusted data assets, cyber-assured software and systems, and AI-enabling software—that transform insights into decisive action. We empower physicists, engineers, researchers, and scientists to think faster, act strategically, and stay ahead in an ever-evolving threat landscape. Our efforts ensure critical nuclear weapons data remains secure, accessible, and usable—supporting mission-critical work, informed decision making, and scientific advancement at LANL and across the Nuclear Security Enterprise (NSE).

45 MILITARY TECHNOLOGY, WEAPONRY, AND NATIONAL DEF↗
DOE OSTI2015

Transportation Deployment; NREL (National Renewable Energy Laboratory)

Automakers, commercial fleet operators, component manufacturers, and government agencies all turn to the U.S. Department of Energy’s National Renewable Energy Laboratory (NREL) to help put more green vehicles on the road. The lab’s independent analysis and evaluation pinpoint fuel-efficient and low-emission strategies to support economic and operational goals, while breaking down barriers to widespread adoption. Customized assessment of existing equipment and practices, energy-saving alternatives, operational considerations, and marketplace realities factor in the multitude of variables needed to ensure meaningful performance, financial, and environmental benefits. NREL provides integrated, unbiased, 360-degree sustainable transportation deployment expertise encompassing alternative fuels, advanced vehicles, and related infrastructure. Hands-on support comes from technical experts experienced in advanced vehicle technologies, fleet operations, and field data collection coupled with extensive modeling and analysis capabilities. The lab’s research team works closely with automakers and vehicle equipment manufacturers to test, analyze, develop, and evaluate high-performance fuel-efficient technologies that meet marketplace needs.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2017

The Secret Lives of Cepheids: δ Cep—The Prototype of a New Class of Pulsating X-Ray Variable Stars{sup *}

From our Secret Lives of Cepheids program, the prototype Classical Cepheid, δ Cep, is found to be an X-ray source with periodic pulsation-modulated X-ray variations. This finding complements our earlier reported phase-dependent FUV–UV emissions of the star that increase ∼10–20 times with highest fluxes at ∼0.90--0.95ϕ, just prior to maximum brightness. Previously δ Cep was found as potentially X-ray variable, using XMM-Newton observations. Additional phase-constrained data were secured with Chandra near X-ray emission peak, to determine if the emission and variability were pulsation-phase-specific to δ Cep and not transient or due to a possible coronally active, cool companion. The Chandra data were combined with prior XMM-Newton observations, and were found to very closely match the previously observed X-ray behavior. From the combined data set, a ∼4 increase in X-ray flux is measured, reaching a peak L{sub X} = 1.7 × 10{sup 29} erg s{sup −1} near 0.45ϕ. The precise X-ray flux phasing with the star’s pulsation indicates that the emissions arise from the Cepheid and not from a companion. However, it is puzzling that the maximum X-ray flux occurs ∼0.5ϕ (∼3 days) later than the FUV–UV maximum. There are several other potential Cepheid X-ray detections with properties similar to δ Cep, and comparable X-ray variability is indicated for two other Cepheids: β Dor and V473 Lyr. X-ray generating mechanisms in δ Cep and other Cepheids are discussed. If additional Cepheids are confirmed to show phased X-ray variations, then δ Cep will be the prototype of a new class of pulsation-induced X-ray variables.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2014

Multi-Tenant Isolation via Reconfigurable Networks

High performance computing environments are often used for a wide variety of workloads ranging from simulation, data transformation and analysis, and complex workflows to name just a few. These systems may process data at various security levels but in so doing are often enclaved at the highest security posture. This approach places significant restrictions on the users of the system even when processing data at a lower security level and exposes data at higher levels of confidentiality to a much broader population than otherwise necessary. The traditional approach of isolation, while effective in establishing security enclaves poses significant challenges for the use of shared infrastructure in HPC environments. This report details current state-of-the-art in reconfigurable network enclaving through Software Defined Networking (SDN) and Network Function Virtualization (NFV) and their applicability to secure enclaves in HPC environments. SDN and NFV methods are based on a solid foundation of system wide virtualization. The purpose of which is very straight forward, the system administrator can deploy networks that are more amenable to customer needs, and at the same time achieve increased scalability making it easier to increase overall capacity as needed without negatively affecting functionality. The network administration of both the server system and the virtual sub-systems is simplified allowing control of the infrastructure through well-defined APIs (Application Programming Interface). While SDN and NFV technologies offer significant promise in meeting these goals, they also provide the ability to address a significant component of the multi-tenant challenge in HPC environments, namely resource isolation. Traditional HPC systems are built upon scalable high-performance networking technologies designed to meet specific application requirements. Dynamic isolation of resources within these environments has remained difficult to achieve. SDN and NFV methodology provide us with relevant concepts and available open standards based APIs that isolate compute and storage resources within an otherwise common networking infrastructure. Additionally, the integration of the networking APIs within larger system frameworks such as OpenStack provide the tools necessary to establish isolated enclaves dynamically allowing the benefits of HPC while providing a controlled security structure surrounding these systems.

97 MATHEMATICS AND COMPUTING↗