SEARCH · Search NASA

Results for “federated”

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.

SourceResearch connectionFrom yearThrough year

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 37 records · Page 2

All sources NASA NTRS DOE OSTI

DOE OSTI2021

Federal Water Management Planning Manual

This Federal Water Management Resource Guide provides direction to Federal agencies on how optimize water use at Federal facilities, use water balance analysis methods, identify design elements and procurement best practices, and expand use of alternative water. It provides best practices for managing water to be considered when developing or improving an effective facility water management program. PNNL developed this guide for the Federal Energy Management Program (FEMP). FEMP was directed by the Council on Environmental Quality (CEQ) to develop a Federal Water Management Resource Guide per the Implementing Instructions for Executive Order (E.O.) 13834, Efficient Federal Operations. This document serves to satisfy this requirement in order to provide best water management practices within the Federal Government.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

NASA NTRS2005

Overview of NASA MSFC IEC Federated Engineering Collaboration Capability

The MSFC IEC federated engineering framework is currently developing a single collaborative engineering framework across independent NASA centers. The federated approach allows NASA centers the ability to maintain diversity and uniqueness, while providing interoperability. These systems are integrated together in a federated framework without compromising individual center capabilities. MSFC IEC's Federation Framework will have a direct affect on how engineering data is managed across the Agency. The approach is directly attributed in response to the Columbia Accident Investigation Board (CAB) finding F7.4-11 which states the Space Shuttle Program has a wealth of data sucked away in multiple databases without a convenient way to integrate and use the data for management, engineering, or safety decisions. IEC s federated capability is further supported by OneNASA recommendation 6 that identifies the need to enhance cross-Agency collaboration by putting in place common engineering and collaborative tools and databases, processes, and knowledge-sharing structures. MSFC's IEC Federated Framework is loosely connected to other engineering applications that can provide users with the integration needed to achieve an Agency view of the entire product definition and development process, while allowing work to be distributed across NASA Centers and contractors. The IEC DDMS federation framework eliminates the need to develop a single, enterprise-wide data model, where the goal of having a common data model shared between NASA centers and contractors is very difficult to achieve.

Moushon, Brian↗

DOE OSTI2023

Understanding Emerging Building Technologies in Federal Performance Contracting Markets

Every year, federal technology evaluation programs test and assess the performance of building technologies that are being considered for wider deployment in federal facilities. Despite these investments in technology validation, many of the technologies evaluated by federal programs struggle to achieve broad penetration into the federal performance contracting market. This report contains the results of a market-research survey conducted by the National Renewable Energy Laboratory to understand barriers to the wider adoption of validated building technologies in the federal performance contracting market and how this information can be used to increase their deployment. The initial phase of this research focuses on the energy service company community and the barriers they perceive to wider adoption of these evaluated technologies. The report ends with an examination of recommendations for how the U.S. Department of Energy's Federal Energy Management Program can assist agencies in deploying emerging building technologies through energy savings performance contracts.

25 ENERGY STORAGE↗

DOE OSTI2024

Federated Access from DOE Labs to Distributed Storage in the EIC Era of Computing

The Electron Ion Collider (EIC) collaboration and future experiment is a unique scientific ecosystem within Nuclear Physics as the experiment starts right off as a crosscollaboration between Brookhaven National Lab (BNL) & Jefferson Lab (JLab). As a result, this muti-lab computing model tries at best to provide services accessible from anywhere by anyone who is part of the collaboration. While the computing model for the EIC is not finalized, it is anticipated that the computational and storage resources will be made accessible to a wide range of collaborators across the world. The use of federated ID seems to be a critical element to the strategy of providing such services, allowing seamless access to each lab site computing resources. However, providing Federated access to a Federated storage is not a trivial matter and has its share of technical challenges. In this contribution, we focus on the steps we took towards the deployment of a distributed object storage system that integrates with Amazon S3 and Federated ID. We will first cover for and explain the first stage storage solutions provided to the EIC during the detector design phase. Our initial test deployment consisted of Lustre storage using MinIO, hence providing an S3 interface. High Availability load balancers were added later to provide the initial scalability it lacked. Performance of that system will be shown. While this embryonic solution worked well, it had many limitations. Looking ahead, the Ceph object storage is considered a top-of-the-line solution in the storage community - since the Ceph Object Gateway is compatible with the Amazon S3 API out of the box, our next phase will use a native S3 storage. Our Ceph deployment will consist of erasure coded storage nodes to maximize storage potential along with multiple Ceph Object Gateways for redundant access. We will compare performance of our next stage implementations. Finally, we will present how to leverage OpenID Connect with the Ceph Object Gateway’s to enable Federated ID access. We hope this contribution will serve the community needs as we move forward with cross-lab collaborations and the need for Federated ID access to distributed compute facilities.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

DOE OSTI2024

Toward Practical Federal Spectrum Sharing for Advanced Wireless Technologies

Wireless communications have become critical to modern society as the foundation of vital services, business interactions, and social connections. As technology develops and new scenarios emerge, wireless communications become increasingly entrenched as a foundation for modern life. The materialization of 5G has been especially impactful as an enabler for a wide range of new scenarios and increased dependence. With increasing demand for wireless communications comes increasing demand for the radio frequency (RF) spectrum necessary to support these communications. This spectrum demand, largely driven by desire to realize ubiquitous 5G networks, both public and private, is currently driving a reconsideration of spectrum assignments and forcing legacy users of spectrum to update their use of spectrum. As one of the largest legacy users of spectrum, the U.S. Government (USG) is particularly impacted by the increasing demand for spectrum for non USG use. Unfortunately, there is no clear pathway for U.S. federal agencies to modernize their spectrum use and instead these agencies are forced to simply react to spectrum repurposing driven by overwhelming demand. Here we present preliminary work toward a roadmap for federal agencies to modernize their spectrum use through practical analysis and leveraging emerging spectrum sharing (SS) technologies. This effort utilizes the example of DOE spectrum use in the context of National Telecommunications and Information Administration (NTIA) and Federal Communications Commission (FCC) repurposing to examine the factors that must be considered to facilitate spectrum sharing by federal agencies. Furthermore, this work provides an initial survey of the SS models and SS technologies of highest relevance to federal use. We particularly examine the emerging capabilities of 5G itself to enable SS and unlicensed access through transformative capabilities such as new radio unlicensed (NR-U). This work is a step toward translating the large body of academic discussion on SS into a practical roadmap for federal agencies.

5G↗

DOE OSTI2022

The Third Assessment of the Effects of Climate Change on Federal Hydropower

Understanding the future changes in projected water supplies is a vital objective for federal hydropower facilities tasked with providing low-cost, reliable electricity across a large regional footprint that encompasses a growing customer base, alternative market structures for marketing the electricity, and a more diverse generation asset mix than was historically present when a majority of federal hydropower facilities were built. This study, The Third Assessment of the Effects of Climate Change on Federal Hydropower, directed by Section 9505 of the SECURE Water Act of 2009 (SWA), is the third quinquennial report on evaluating the effects of climate change on hydroelectric energy generated from 132 US federal hydropower plants marketed by four US Department of Energy (DOE) Power Marketing Administrations (PMAs). The technical assessment is conducted by DOE’s Oak Ridge National Laboratory, Pacific Northwest National Laboratory, and Texas A&M University under the guidance of DOE’s Water Power Technologies Office. This study is the result of extensive consultation with the four federal PMAs (Bonneville Power Administration [BPA], Western Area Power Administration [WAPA], Southwestern Power Administration [SWPA], and Southeastern Power Administration [SEPA]), as well as other agencies, including federal hydropower owners/operators (the US Army Corps of Engineers, US Bureau of Reclamation [Reclamation]), US Geological Survey, and National Oceanic and Atmospheric Administration). The main findings of this assessment, along with the PMA administrators’ recommendations, will be included in a subsequent DOE report to Congress. The assessment method and the technical findings are described in this report.

13 HYDRO ENERGY↗

DOE OSTI2023

Procurement Options for Low Temperature Geothermal Technologies at Federal Facilities

Federal agencies are moving towards more efficient and resilient facilities by increasingly implementing energy projects in an effort to meet federally mandated goals, agency needs, and administration priorities. Low temperature geothermal technologies, which include geothermal heat pumps (GHP) and district heating systems, can contribute to meeting these goals. These technologies provide facilities with heating and cooling while reducing facility energy use and improving resilience. However, in order to implement these solutions, federal facilities must identify and execute a strategy for the procurement of these technologies. Federal facilities have successfully completed energy efficiency projects using a variety of procurement options. The purpose of this document is to provide federal agencies with a comprehensive overview of the procurement options available for low temperature geothermal technologies and other energy efficiency projects. The procurement options discussed include third-party financing mechanisms such as energy saving performance contracts (ESPCs), ESPC energy sales agreements (ESPC ESAs), and utility energy service contracts (UESCs), as well as dedicated funds in the form of federal grants and appropriated funds.

15 GEOTHERMAL ENERGY↗

NASA NTRS1993

Pathways to excellence: A Federal strategy for science, mathematics, engineering, and technology education

This Strategic Plan was developed by the Federal Coordinating Council for Science, Engineering, and Technology (FCCSET) through its Committee on Education and Human Resources (CEHR), with representatives from 16 Federal agencies. Based on two years of coordinated interagency effort, the Plan confirms the Federal Government's commitment to ensuring the health and well-being of science, mathematics, engineering, and technology education at all levels and in all sectors (i.e., elementary and secondary, undergraduate, graduate, public understanding of science, and technology education). The Plan represents the Federal Government's efforts to develop a five-year planning framework and associated milestones that focus Federal planning and the resources of the participating agencies toward achieving the requisite or expected level of mathematics and science competence by all students. The priority framework outlines the strategic objectives, implementation priorities, and components for the Strategic Plan and serves as a road map for the Plan. The Plan endorses a broad range of ongoing activities, including continued Federal support for graduate education as the backbone of our country's research and development enterprise. The Plan also identifies three tiers of program activities with goals that address issues in science, mathematics, engineering, and technology education meriting special attention. Within each tier, individual agency programs play important and often unique roles that strengthen the aggregate portfolio. The three tiers are presented in descending order of priority: (1) reforming the formal education system; (2) expanding participation and access; and (3) enabling activities.

Source record↗

DOE OSTI2021

INTERFUEL: FAST - FY 2020 Federal Fleet Dataset [Slides]

This presentation provides an overview of the fiscal year (FY) 2020 federal motor vehicle fleet dataset collected through the Federal Automotive Statistical Tool (FAST). FAST is a web-based information system sponsored by GSA's Office of Government-wide Policy and DOE's Federal Energy Management Program to collect information about the US federal government's fleet of motor vehicles. The presentation discusses the size of the dataset; a high-level look at what the information collected shows about the makeup and operation of the federal motor vehicle fleet during FY 2020 and how that compares with recent years; the process used to review the agency submissions comprising the dataset and the impact of some of the agency-provided corrections to identified issues; activities following the finalization of the dataset.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

DOE OSTI2021

FEDFLEET: FAST (FY2020 Federal Fleet Dataset Overview) [Slides]

This presentation provides an overview of the fiscal year (FY) 2020 federal motor vehicle fleet dataset collected through the Federal Automotive Statistical Tool (FAST). FAST is a web-based information system sponsored by GSA's Office of Government-wide Policy and DOE's Federal Energy Management Program to collect information about the US federal government's fleet of motor vehicles. The presentation discusses the size of the dataset; provides a high-level look at what the collected information shows about the makeup and operation of the federal motor vehicle fleet during FY 2020 and how that compares with recent years; discusses the process used to review the agency submissions comprising the dataset and the impact of some of the agency-provided corrections to identified issues.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2022

FY 2021 Federal Vehicle Fleet Data Overview [Slides]

This presentation provides an overview of the fiscal year (FY) 2021 federal motor vehicle fleet dataset collected through the Federal Automotive Statistical Tool (FAST). FAST is a web-based information system sponsored by GSA's Office of Government-wide Policy and DOE's Federal Energy Management Program to collect information about the US federal government's fleet of motor vehicles. The presentation discusses the size of the dataset; provides a high-level look at what the collected information shows about the makeup and operation of the federal motor vehicle fleet during FY 2021 and how that compares with recent years; discusses the process used to review the agency submissions comprising the dataset; and discusses how overall quality of the dataset has been assessed.

99 GENERAL AND MISCELLANEOUS↗

DOE OSTI2024

Predicting U.S. federal fleet electric vehicle charging patterns using internal combustion engine vehicle fueling transaction statistics

Utilizing fueling transactions from internal combustion engine vehicles (ICEVs), the authors estimated how frequently midday public charging would be required for U.S. federal fleet battery electric vehicles (BEVs). Fueling transaction summary statistics are more widely available than trip-level telematics data, making this methodology more accessible and transferable to other researchers and fleet managers considering BEV replacements. For example, readers can easily apply a linear model using only the count of back-to-back fueling events at gas stations over 57 straight-line miles apart to predict days exceeding range. This linear regression predicted binned days exceeding 250 miles at 80% accuracy on a hold-out test set from the same fleet as the training data and 66 % accuracy on a new fleet displaying different driving behaviors. The authors additionally provide linear equations for days exceeding 200 and 300 miles as alternative range estimates to account for differences in BEV range and temperature impacts. Beyond the single-feature linear models which readers can apply, the authors tuned and trained other machine learning models on a variety of fueling transaction statistics including consecutive transaction distances, transaction distance from garage, estimated miles traveled from fuel economy and fuel quantity, and transaction periodicity. Utilizing a subset of 1678 light-duty federal fleet vehicles which contained daily vehicle miles traveled (VMT) in addition to fueling statistics, the authors determined which fueling transaction statistics were most relevant in predicting driving days exceeding 250 miles (an approximation of BEV rated driving range). In support of the U.S. federal fleet transition to zero-emission vehicles (ZEVs), the authors used these statistics and machine learning models to predict the frequency of BEV midday charging. After training models on the subset with VMT, the authors predicted days exceeding rated range for 112,902 light-duty vehicles operating in similar circumstances in the federal fleet using a Support Vector Regressor (SVR). In conclusion, they then used the projections as part of the ZEV Planning and Charging (ZPAC) tool to identify optimal candidates for BEVs for the federal fleet. An anonymized version of ZPAC is included in the supplementary materials.

25 ENERGY STORAGE↗

DOE OSTI2022

NEFTSec: Networked federation testbed for cyber-physical security of smart grid: Architecture, applications, and evaluation

As today's power grid is evolving into a densely interconnected cyber-physical system (CPS), a high fidelity and multifaceted testbed environment is needed to perform cybersecurity experiments in a realistic grid environment. Traditional standalone CPS testbeds lack the ability to emulate complex cyber-physical interdependencies between multiple smart grid domains in a real-time environment. Therefore, there are ongoing research and development (R&D) efforts to develop an interconnected CPS testbed by sharing geographically dispersed testbed resources to perform distributed simulation while analysing simulation fidelity. This paper presents a networked federation testbed for cybersecurity evaluation of today's and emerging smart grid environments. Specifically, it presents two novel testbed architectures, including cyber federation and cyber-physical federation, identifies R&D applications, and also describes testbed building blocks with experimental case studies. It also presents a novel co-simulation interface algorithm to facilitate distributed simulation within cyber-physical federation. The resources available at the PowerCyber CPS security testbed at Iowa State University (ISU) and the US Army Research Laboratory are utilised to develop this platform for performing multiple experimental case studies pertaining to wide-area protection and control applications in power system. Finally, experimental results are presented to analyse the simulation fidelity and real-time performance of the testbed federation.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2025

FEDERATED LEARNING ON STOCHASTIC NEURAL NETWORKS

Federated learning is a machine learning paradigm that leverages edge computing on client devices to optimize models while maintaining user privacy by ensuring that local data remain on the device. However, since all data are collected by clients, federated learning is susceptible to latent noise in local datasets. Factors such as limited measurement capabilities or human errors may introduce inaccuracies in client data. To address this challenge, we propose the use of a stochastic neural network as the local model within the federated learning framework. Stochastic neural networks not only facilitate the estimation of the true underlying states of the data but also enable the quantification of latent noise. We refer to our federated learning approach, which incorporates stochastic neural networks as local models, as federated stochastic neural networks. In this work we will present numerical experiments demonstrating the performance and effectiveness of our method, particularly in handling nonindependent and identically distributed data.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2022

Golden Gate National Recreation Area Federal Fleet Tiger Team EVSE Site Assessment

The U.S. Department of Energy Federal Energy Management Program (FEMP) helps federal agencies reduce petroleum consumption and increase alternative fuel use through its resources for sustainable federal fleets. A key element of this assistance involves supporting agencies in the transition to zero-emission vehicles (ZEVs). Fleet electrification is part of a federal policy to achieve net-zero emissions economy-wide and a carbon pollution-free electricity sector, established through two executive orders (EOs) - EO 14008: Tackling the Climate Crisis at Home and Abroad and EO 14057: Catalyzing America's Clean Energy Industries and Jobs through Federal Sustainability. This site report supports the development of a ZEV deployment plan for the Golden Gate National Recreation Area, which can ultimately be incorporated into the overall U.S. Department of the Interior ZEV fleet strategy.

33 ADVANCED PROPULSION SYSTEMS↗

DOE OSTI2022

Yellowstone National Park Federal Fleet Tiger Team EVSE Site Assessment [Slides]

The U.S. Department of Energy Federal Energy Management Program (FEMP) helps federal agencies reduce petroleum consumption and increase alternative fuel use through its resources for sustainable federal fleets. A key element of this assistance involves supporting agencies in the transition to zero-emission vehicles (ZEVs). Fleet electrification is part of a federal policy to achieve net-zero emissions economy-wide and a carbon pollution-free electricity sector, established through two executive orders (EOs) - EO 14008: Tackling the Climate Crisis at Home and Abroad and EO 14057: Catalyzing America's Clean Energy Industries and Jobs through Federal Sustainability. This site report supports the development of a ZEV deployment plan for Yellowstone National Park, which can ultimately be incorporated into the overall U.S. Department of the Interior ZEV fleet strategy.

33 ADVANCED PROPULSION SYSTEMS↗

DOE OSTI2025

Applications of Federated Learning in Semiconductor Manufacturing [Poster]

As semiconductor manufacturers explore advanced data analytics and modeling techniques and data hungry machine learning models increase in popularity due to their accuracy in solving generalized problems and ability to learn complex relationships, federated learning emerges as a privacy preserving machine learning technique for preserving data privacy and ensuring intellectual property protection. Federated Learning is a machine learning technique focused on training models using distributed data that never needs to be centrally stored, allowing the use of advanced machine learning techniques without compromising data privacy, and in the semiconductor manufacturing industry advanced machine learning techniques can reduce cost and time, but maintaining data privacy is essential to maintaining a competitive advantage. This paper systematically reviews existing literature on applications of federated learning in the semiconductor manufacturing industry with a focus on identifying common themes, algorithms, and gaps within the literature to drive future research directions. The findings reveal five key themes, including improvements in quality assurance, virtual models, privacy preservation, reliable data practices, and emerging trends and developments. By identifying key themes in literature on federated learning and semiconductor manufacturing and analyzing gaps and discussed methodologies, this study highlights several potential future research directions to expand the application of federated learning techniques in the semiconductor manufacturing domain.

42 ENGINEERING↗

DOE OSTI2020

NCBI’s Virus Discovery Codeathon: Building “FIVE” —The Federated Index of Viral Experiments API Index

Viruses represent important test cases for data federation due to their genome size and the rapid increase in sequence data in publicly available databases. However, some consequences of previously decentralized (unfederated) data are lack of consensus or comparisons between feature annotations. Unifying or displaying alternative annotations should be a priority both for communities with robust entry representation and for nascent communities with burgeoning data sources. To this end, during this three-day continuation of the Virus Hunting Toolkit codeathon series (VHT-2), a new integrated and federated viral index was elaborated. This Federated Index of Viral Experiments (FIVE) integrates pre-existing and novel functional and taxonomy annotations and virus–host pairings. Variability in the context of viral genomic diversity is often overlooked in virus databases. As a proof-of-concept, FIVE was the first attempt to include viral genome variation for HIV, the most well-studied human pathogen, through viral genome diversity graphs. As per the publication of this manuscript, FIVE is the first implementation of a virus-specific federated index of such scope. FIVE is coded in BigQuery for optimal access of large quantities of data and is publicly accessible. Many projects of database or index federation fail to provide easier alternatives to access or query information. To this end, a Python API query system was developed to enhance the accessibility of FIVE.

59 BASIC BIOLOGICAL SCIENCES↗