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At least 1,171 records · Page 65

All sources NASA NTRS DOE OSTI

DOE OSTI2013

THE APPLICATION OF MULTIVIEW METHODS FOR HIGH-PRECISION ASTROMETRIC SPACE VLBI AT LOW FREQUENCIES

High-precision astrometric space very long baseline interferometry (S-VLBI) at the low end of the conventional frequency range, i.e., 20 cm, is a requirement for a number of high-priority science goals. These are headlined by obtaining trigonometric parallax distances to pulsars in pulsar-black hole pairs and OH masers anywhere in the Milky Way and the Magellanic Clouds. We propose a solution for the most difficult technical problems in S-VLBI by the MultiView approach where multiple sources, separated by several degrees on the sky, are observed simultaneously. We simulated a number of challenging S-VLBI configurations, with orbit errors up to 8 m in size and with ionospheric atmospheres consistent with poor conditions. In these simulations we performed MultiView analysis to achieve the required science goals. This approach removes the need for beam switching requiring a Control Moment Gyro, and the space and ground infrastructure required for high-quality orbit reconstruction of a space-based radio telescope. This will dramatically reduce the complexity of S-VLBI missions which implement the phase-referencing technique.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2017

Resilient Grid Operational Strategies

Extreme weather-related disturbances, such as hurricanes, are a leading cause of grid outages historically. Although physical asset hardening is perhaps the most common way to mitigate the impacts of severe weather, operational strategies may be deployed to limit the extent of societal and economic losses associated with weather-related physical damage.1 The purpose of this study is to examine bulk power-system operational strategies that can be deployed to mitigate the impact of severe weather disruptions caused by hurricanes, thereby increasing grid resilience to maintain continuity of critical infrastructure during extreme weather. To estimate the impacts of resilient grid operational strategies, Los Alamos National Laboratory (LANL) developed a framework for hurricane probabilistic risk analysis (PRA). The probabilistic nature of this framework allows us to estimate the probability distribution of likely impacts, as opposed to the worst-case impacts. The project scope does not include strategies that are not operations related, such as transmission system hardening (e.g., undergrounding, transmission tower reinforcement and substation flood protection) and solutions in the distribution network.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2018

Report on the completion of long-lead components procurement of new sodium materials test loop

This report provides an update on the completion of long-lead components procurement of new sodium materials test loop. The loop, designated as SMT-3, is a forced-convection sodium loop to be used for the evaluation of sodium compatibility of advanced Alloy 709 with emphasis on long term exposures of tensile, creep, fatigue, creep fatigue, and fracture toughness ASTM-size specimens in support of ASME Code qualification and NRC licensing. The report is a deliverable (Level 4) in FY18 (M4NT-18AN050503013), under the Work Package NT-18AN05050301, “A709 Sodium Compatibility” performed by Argonne National Laboratory (ANL), as part of the Advanced Materials Program for the Advanced Reactor Technology. This work package enables the development of advanced structural materials by providing corrosion, microstructure, and mechanical property data from the standpoint of sodium compatibility of advanced structural alloys. The first sodium loop (SMT-1) with a single tank was constructed in 2011 at ANL and has been in operation for exposure of subsize sheet specimens of advanced alloys at a single temperature. The second sodium loop with dual tanks (SMT-2) was constructed in 2013 and has been in operation for the exposure of subsize sheet specimens of advanced alloys at two different temperatures. The current loop (SMT-3) has been designed to incorporate sufficient chamber capacity to expose a large number of ASTM-size specimens to evaluate the sodium effects on tensile, creep, fatigue, creep-fatigue, and fracture toughness properties, in support of ASME Code Qualification and USNRC Licensing. Three major long lead items were identified: 1) Selection and approval of location for the new loop and installation of the necessary infrastructure, such as transformer, power supply panels, and wiring, that is adequate for the long term operation of the loop, 2) Perform a detailed piping analysis that meets the ANL QA and Safety guidelines as well as complying with ASME Code requirements, and 3) Design and fabrication of the specimen exposure vessels that are capable of at least 42 tensile, creep, and fatigue specimens in each vessel and is compliant and compatible with ASME Code and the piping design analysis. The SMT-3 loop will be located in Building 206 adjacent to the currently operating SMT-2 loop. In addition, we have removed the aged power supply system in Building 206 and installed a new transformer, wiring, and power panels for the new loop. The preliminary loop design was sent to an outside vendor for a detailed piping analysis, to ensure compliance with the ASME Code guidelines. The piping design was completed in FY2017. The construction of components such as cold trap, economizers, piping arrangement etc. will be performed in the central shops at ANL. About 150 liters of sodium for the loop will be procured in early FY2018. The loop system is designed to circulate sodium through the sample tanks and he associated loop without an operator for an extended period of time. With the three sodium loops (i.e., single-tank, dual-tank and four–tank), materials can be tested at different sodium temperatures, and large tensile, creep, fatigue, creep-fatigue, and fracture toughness specimens can be exposed to sodium for extended periods of time and generate data on mechanical properties in support of ASME Code Qualification and USNRC Licensing of advanced Alloy 709 for use as a structural material in SFRs.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗

DOE OSTI2019

Washington State Highway Seismic Screening Tool (HSST)

The U.S. Department of Homeland Security’s Regional Resiliency Assessment Program (RRAP) undertook the Washington State Transportation Systems RRAP project in 2017 to assess the seismic vulnerabilities of that state’s transportation system to a Cascadia Subduction Zone (CSZ) earthquake. The specific goal of that project was to assess the vulnerability of, and prioritize, transportation infrastructure for moving goods and supplies from Incident Support Bases to Federal Staging Areas within Washington State following a CSZ earthquake. As part of this analysis, Argonne National Laboratory (Argonne), with input and support from the Washington State Department of Transportation (WSDOT) maintenance and pavements offices and state geotechnical engineer, developed the Highway Seismic Screening Tool (HSST) to inform a system-level analysis of highway recovery and reopening times. This report provides a full discussion of the HSST’s development, implementation, and results for the CSZ scenario earthquake. The primary outcome of the HSST is to determine the approximate reopening times of highway segments or bridges damaged by a CSZ magnitude 9.0 seismic event. This is done by calculating the permanent ground deformation (PGD) caused by soil liquefaction along state highways, which can cause significant damage or displacement of highway pavement structures. The term “reopening time” is different from “restoration time” and is intended to represent the approximate amount of time that would be needed to return highways to a minimum state of repair sufficient to support the limited movement of emergency and response vehicles. Additionally, the HSST is referred to as a “screening tool,” as it uses network- or system-level asset management information that WSDOT provided to assess seismic-related hazards and vulnerabilities of highways; it does not conduct an asset-level engineering analysis of seismic-related vulnerabilities. Accordingly, the results of the HSST analysis should only be used for system- or corridor-level planning and investment, and not for engineering or management decisions at the asset level. The HSST is also applied to assess the exposure of statewide rail systems to soil liquefaction and PGD hazards. However, little information is available about the sensitivity or vulnerability of railways and rail yards to such hazards, and therefore researchers were unable to determine approximate reopening times for the state’s rail system following a CSZ earthquake.

58 GEOSCIENCES↗

DOE OSTI2019

Assessment of Existing Capabilities and Future Needs for Designing Networked Microgrids

This is a review of existing microgrid design tool capabilities, such as the Microgrid Design Tool (MDT), LANL PNNL NRECA Optimal Resilience Model (LPNORM), Distributed Energy Resource-Customer Adoption Model (DER-CAM), Renewable Energy Optimization (REopt), and the Hybrid Optimization Model for Multiple Energy Resources (HOMER). Additionally, other simulation and analysis tools which may provide fundamental support will be examined. These will include GridLAB-DTM, OpenDSS, and the hierarchical Engine for Large-scale Infrastructure Co-Simulation (HELICS). Their applicability to networked microgrid operations will be evaluated, and strengths and gaps of existing tools will be identified. This review will help to determine which elements of the proposed optimal design and operations (OD&D) tool should be formulated from first principles, and which elements should be integrated from past DOE investments.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2024

Analyzing Risks of Virtual Private Network Connections

The use of Splunk for analyzing VPN logs is an effective approach for identifying vulnerabilities in network endpoints. Splunk, a powerful platform for searching, monitoring, and analyzing machine-generated data, enables organizations to aggregate VPN logs in real-time, providing insights into network activity, user behavior, and potential security risks. By indexing VPN traffic and authentication logs, security teams can track abnormal patterns such as multiple failed login attempts, unusual IP addresses, or unexpected changes in bandwidth usage, all of which could indicate potential vulnerabilities or breaches. With Splunk’s advanced search and reporting capabilities, users can create custom dashboards and alerts to detect suspicious activities. Automated searches can flag endpoints exhibiting unusual behavior, while correlation analysis can identify links between compromised devices and broader network vulnerabilities. In particular, Splunk's machine learning capabilities can be leveraged to predict and prevent threats by identifying trends that might otherwise be missed in traditional log analysis. This proactive approach to monitoring VPN logs allows for the early detection of security weaknesses, enabling rapid response and minimizing potential damage to network integrity. By enhancing endpoint visibility, Splunk plays a crucial role in securing remote connections and safeguarding sensitive information. Additionally, Splunk’s automation and alerting features allow teams to create custom workflows that notify them of vulnerable or misconfigured endpoints identified through Shodan. This synergy between Splunk’s log analysis and Shodan’s device intelligence enhances an organization’s ability to proactively identify and mitigate security risks, improving the overall resilience of their VPN infrastructure.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2021

Evaluating China’s Passenger Vehicle Market under the Vehicle Policies of 2021–2023

China is well known for its determination on large-scale vehicle electrification, which currently is mainly driven by fuel economy and electric vehicle policies mixed with the extensive charging infrastructure support and monetary incentives from the government. This study adopted the New Energy and Oil Consumption Credits (NEOCC) model 2020 version, a vehicle policy analysis tool developed by the Oak Ridge National Laboratory, in order to systematically quantify the potential impacts of the “Passenger Cars Corporate Average Fuel Consumption and New Energy Vehicle Credit Regulation”, which is a revised version released in June 2020 for the timeframe 2021–2023, the so-called dual credit policy (2021–2023). It was found that, under the dual credit policy (2021–2023), the sales of hybrid electric vehicles could reach 0.91 million by the end of 2023, which would increase much faster than they did in 2018–2020. The annual sales share of plug-in electric vehicles (PEVs) could reach 11.7%, and the PEV stocks could achieve 11.70 million by the end of 2023 if it keeps the expansion to the level of how it was in 2017. In addition, the BEVs with long electric range (such as 400 km) and the plug-in hybrid electric SUVs could be the most popular PEV types.

33 ADVANCED PROPULSION SYSTEMS↗

NASA NTRS1992

A risk-based approach to robotic mission requirements

A NASA Risk Team has developed a method for the application of risk management to the definition of robotic mission requirements for the Space Exploration Initiative. These requirements encompass environmental information, infrastructural emplacement in advance, and either technology testing or system/subsystems demonstration. Attention is presently given to a method for step-by-step consideration and analysis of the risk component inherent in mission architecture, followed by a calculation of the subjective risk level. Mitigation strategies are then applied with the same rules, and a comparison is made.

Dias, William C.↗

NASA NTRS1998

A Method for Evaluating the Safety Impacts of Air Traffic Automation

This report describes a methodology for analyzing the safety and operational impacts of emerging air traffic technologies. The approach integrates traditional reliability models of the system infrastructure with models that analyze the environment within which the system operates, and models of how the system responds to different scenarios. Products of the analysis include safety measures such as predicted incident rates, predicted accident statistics, and false alarm rates; and operational availability data. The report demonstrates the methodology with an analysis of the operation of the Center-TRACON Automation System at Dallas-Fort Worth International Airport.

Kostiuk, Peter↗

NASA NTRSDate not supplied

SLS Payload Launch Loads Analysis Using the NTRC Method

Norton-Thevenin Receptance Coupling (NTRC), as described in several NASA Engineering and Safety Center (NESC) papers shows promise in enabling loads development for payloads with less computational cost and analyst time as compared to a full integrated vehicle coupled loads analysis (CLA). NTRC allows the free vehicle responses and impedance (accelerance) at the payload to vehicle interface (derived from integrated vehicle CLA) to be used with a payload model in a payload CLA. Due to the NTRC damping differing from the full integrated vehicle damping, results are slightly different, so coverage factors were developed to ensure NTRC results enveloped results from a traditional CLA. NTRC results with coverage factors were very close to full CLA results. The Space Launch System (SLS) coupled loads team has developed an implementation of the NTRC method to enable support of co-manifested payloads with analysis needs that do not fit the primary SLS load cycle schedule. The NTRC method was successfully used to support the European System Providing Refueling, Infrastructure, and Communications (ESPRIT) module for Gateway planned to fly on Artemis V and has been adopted as the SLS approach for supporting payload CLA requests that do not align with vehicle load cycles.

SLS↗

NASA NTRSDate not supplied

Application of Norton-Thevenin Receptance Coupling (NTRC) to Space Launch System (SLS) Payload Coupled Loads Analysis (CLA)

SLS↗

NASA NTRS2024

Kentucky Disasters: Multi-Hazard Approach to Mapping Flood Susceptibility and Vulnerability in Kentucky

Flooding is the most common and costly natural disaster in Kentucky, with major flood events in 2022 and 2023 highlighting the need for flood risk assessment. In partnership with the National Weather Service Jackson and Paducah Forecast Offices and the Kentucky Climate Center, we mapped flood risk in Kentucky using a multi-hazard approach that considered two dimensions of risk: flood susceptibility based on a weighted combination of seven physical factors and flood vulnerability based on 13 socioeconomic and infrastructure factors. We additionally analyzed NASA Soil Moisture Active Passive (SMAP) observations of surface soil moisture to explore the utility of SMAP observations for future analysis of flood risk. By analyzing flood susceptibility, we found that with equal rainfall, western Kentucky generally displays a higher propensity to flood than eastern Kentucky. In contrast, our flood vulnerability analysis indicated that more vulnerable areas were generally concentrated in the eastern part of the state. Through a combined perspective, our flood risk analysis identified much of the state as having moderate degrees of flood susceptibility and vulnerability. Our parallel analysis of antecedent soil moisture found that SMAP soil moisture levels were variable in the months leading up to each flood event but were drier than normal in the month prior to the 2023 event, as shown by negative soil moisture anomalies. These results were limited by challenges with weighting input parameters and a lack of validation but overall demonstrate the feasibility of using GIS and Earth observations for mapping flood risk and soil moisture.

analytic hierarchy process↗

DOE OSTI2020

Real-time Intrusion Detection for High-bandwidth Research Networks using Unsupervised Deep Learning

Nowadays, data generated by large-scale scientific experiments are on the scale of petabytes per month. These data are transferred through dedicated high-bandwidth networks across distributed collaboration sites for processing, storage, and analysis. Like general purpose networks, research networks experience attacks. However, monitoring traffic in such high-bandwidth network environments is challenging with conventional deep packet inspection (DPI)- based network intrusion detection systems (NIDS). Recently, machine learning (ML) algorithms have shown potential in detecting anomalous patterns with better generalizability and faster speed. Compared to the general purpose networks, traffic movement in research network is less volatile and more likely to benefit from the ML-based anomaly analysis. We present ThunderSecure, an efficient AI-powered system for detecting anomalies in 100G research networks. ThunderSecure implements a high-throughput packet processing infrastructure using multi-cores and GPUs. It takes time-series statistics from a variety of network measurements and uses 2D Convolutional Neural Network (CNN) to explore both spatial and temporal dependencies in network data stream. Patterns of science data traffic are learned with a one-class neural network, which blends an Adversarial Autoencoder (AAE) with Gaussian mixture density estimation. Testing traffic flows exhibiting significant deviations from the learned baseline of normality are marked as anomalies. We trained ThunderSecure on hundreds of billions of science data packets recorded from a 100G research network at Fermi National Accelerator Laboratory. The detection performance was evaluated on traffic captured from the same research network days and weeks after the training with different types of attack flows injected. To the best of our knowledge, this is the first ML work in the area of network anomaly detection that has been validated on such extreme scale datasets. Results show that ThunderSecure can recognize science data traffic that are captured long after the training and make nearly certain detection on those with anomalous flows injected, even in the case when the anomaly-to-normal mixing ratio is 0.1%.

Gong, Qian↗

DOE OSTI2017

Scalable data management, analysis and visualization (SDAV) Institute. Final Scientific/Technical Report

The purpose of the SDAV institute is to provide tools and expertise in scientific data management, analysis, and visualization to DOE’s application scientists. Our goal is to actively work with application teams to assist them in achieving breakthrough science, and to provide technical solutions in the data management, analysis, and visualization regimes that are broadly used by the computational science community. Over the last 5 years members of our institute worked directly with application scientists and DOE leadership-class facilities to assist them by applying the best tools and technologies at our disposal. We also enhanced our tools based on input from scientists on their needs. Many of the applications we have been working with are based on connections with scientists established in previous years. However, we contacted additional scientists though our outreach activities, as well as engaging application teams running on leading DOE computing systems. Our approach is to employ an evolutionary development and deployment process: first considering the application of existing tools, followed by the customization necessary for each particular application, and then the deployment in real frameworks and infrastructures. The institute is organized into three areas, each with area leaders, who keep track of progress, engagement of application scientists, and results. The areas are: (1) Data Management, (2) Data Analysis, and (3) Visualization. Kitware has been involved in the Visualization area. This report covers Kitware’s contributions over the last 5 years (February 2012 – February 2017). For details on the work performed by the SDAV institute as a whole, please see the SDAV final report.

97 MATHEMATICS AND COMPUTING↗

NASA NTRS1991

Lunar campsite concept: Space transfer concepts and analysis for exploration missions

The lunar Campsite concept responds to a perceived need to identify early manned science and exploration missions that require minimal initial funding. The Campsite concept defers the build-up of many infrastructure components without escalating total program costs. The lunar Campsite has been sized nominally for four crew for 42 days (1 lunar night and 2 lunar days), but can be modified to span two lunar nights up to 60 days. Total mission fulfillment requires five Earth-to-LEO launches, four (100 mt class launch vehicle) for the two vehicle assemblies and one (PLS or NSTS) for the crew. The lunar Campsite mission mode is tandem direct using a booster stage and a lander stage. The booster is separated from the lander after the TLI burn and is expended into the Earth's atmosphere. In the Campsite mode, the lander lands on the surface not to be returned. In the crew delivery mode, the lander is guided to a precision landing about 500 m from the Campsite, and with enough propellant to return the crew to Earth. The Campsite consists of a habitat and airlock, body mounted radiators with a surface shield, sun tracking solar arrays, and an Earth-tracking high-gain antenna. The CV is very similar to the campsite delivery vehicle. The CV does not, however, have radiators or solar arrays. The vehicle stacks are essentially common in that they utilize the same structure system and engines, the same propellant tanks, the same 'cut-out' in which the CRV and payloads are incorporated, and the same RCS locations. The booster and lander stage propellant tank propellant capacities are identical and have margins which would allow additional fueling for propulsive capture of the boost stage into Earth orbit. This contractual study was performed to identify Campsite and vehicle interfaces and vehicle requirements, and to surface issues related to the integration of the Campsite and LTV's.

Source record↗

DOE OSTI2023

Next-Generation Intensity-Duration-Frequency Curves for Diverse Land across the Continental United States

The current methods for designing hydrological infrastructure rely on precipitation-based intensity-duration-frequency curves. However, they cannot accurately predict flooding caused by snowmelt or rain-on-snow events, potentially leading to underdesigned infrastructure and property damage. To address these issues, next-generation intensity-duration-frequency (NG-IDF) curves have been developed for the open condition, characterizing water available for runoff from rainfall, snowmelt, and rain-on-snow. However, they lack consideration of land use land cover (LULC) factors, which can significantly affect runoff processes. We address this limitation by expanding open area NG-IDF dataset to include eight vegetated LULCs over the continental United States, including forest (deciduous, evergreen, mixed), shrub, grass, pasture, crop, and wetland. This NG-IDF 2.0 dataset offers a comprehensive analysis of hydrological extreme events and their associated drivers under different LULCs at a continental scale. It will serve as a useful resource for improving standard design practices and aiding in the assessment of infrastructure design risks. Additionally, it provides useful insights into how changes in LULC impact flooding magnitude, mechanisms, timing, and snow water supply.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2023

DER Digital Supply Chain Gap Analysis

Solar photovoltaic (PV) cybersecurity is a growing field of research. As deployments of solar PV have increased, cyber risk has also increased. Utility solar PV installations, however, are not required to comply with the North American Electric Reliability Corporation (NERC) Critical Infrastructure Protection (CIP) plan unless they meet a minimum generation threshold of 75 MW. Individual residential-scale solar PV deployments will not meet that generation threshold and are therefore excluded from the NERC CIP requirements. With most solar installations less than 75 MW, solar PV has been deployed with minimal oversight and highly variable cybersecurity maturity. The resources that comprise the digital supply chain can include software, code, data, and other digital components. But as clean energy technologies advance, cybersecurity threats and vulnerabilities continue to evolve and grow in sophistication. Supply chain cybersecurity represents a critical area for ensuring safe operations as the U.S. moves toward a clean energy future.

cybersecurity↗

NASA NTRS2004

Advanced Technology Lifecycle Analysis System (ATLAS)

Developing credible mass and cost estimates for space exploration and development architectures require multidisciplinary analysis based on physics calculations, and parametric estimates derived from historical systems. Within the National Aeronautics and Space Administration (NASA), concurrent engineering environment (CEE) activities integrate discipline oriented analysis tools through a computer network and accumulate the results of a multidisciplinary analysis team via a centralized database or spreadsheet Each minute of a design and analysis study within a concurrent engineering environment is expensive due the size of the team and supporting equipment The Advanced Technology Lifecycle Analysis System (ATLAS) reduces the cost of architecture analysis by capturing the knowledge of discipline experts into system oriented spreadsheet models. A framework with a user interface presents a library of system models to an architecture analyst. The analyst selects models of launchers, in-space transportation systems, and excursion vehicles, as well as space and surface infrastructure such as propellant depots, habitats, and solar power satellites. After assembling the architecture from the selected models, the analyst can create a campaign comprised of missions spanning several years. The ATLAS controller passes analyst specified parameters to the models and data among the models. An integrator workbook calls a history based parametric analysis cost model to determine the costs. Also, the integrator estimates the flight rates, launched masses, and architecture benefits over the years of the campaign. An accumulator workbook presents the analytical results in a series of bar graphs. In no way does ATLAS compete with a CEE; instead, ATLAS complements a CEE by ensuring that the time of the experts is well spent Using ATLAS, an architecture analyst can perform technology sensitivity analysis, study many scenarios, and see the impact of design decisions. When the analyst is satisfied with the system configurations, technology portfolios, and deployment strategies, he or she can present the concepts to a team, which will conduct a detailed, discipline-oriented analysis within a CEE. An analog to this approach is the music industry where a songwriter creates the lyrics and music before entering a recording studio.

O'Neil, Daniel A.↗