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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 19 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Privacy Preserving Federated Learning for Advanced Scientific Ecosystems

We present a framework to provide privacy preserving (PP) federating learning (FL) across multiple computational and experimental facilities. This work joins the compute capabilities of National Energy Research Scientific Computing Center (NERSC) and Oak Ridge National Laboratory Research Cloud (ORC) with simulated experimental data, such as those produced at the SLAC National Accelerator Laboratory and Spallation Neutron Source (SNS). We describe the software infrastructure developed to provide privacy for computational and experimental networks. We developed algorithmic privacy across the federated system by embedding database security, computation, and communication into the federation architecture, utilizing scientific tools developed by the experimental community.

Archibald, Rick [ORNL] (ORCID:0000000245389780)↗
DOE OSTI2025

Performance Evaluation of Vertical Federated Machine Learning Against Adversarial Threats on Wide-Area Control System: Preprint

Federated machine learning (FL) is gaining significant popularity to develop cybersecurity solutions in power grids because of its advanced capability to support decentralized data handing at local devices, its privacy preservation, and its low-bandwidth requirement. However, the evolving adversarial machine learning (AML) threats raise significant concerns for the cybersecurity of FL architectures. The FL-based split neural network (SplitNN) achieves high performance through the decentralized training of local neural network models while preserving data privacy across multiple entities. In this paper, we propose a methodology for evaluating the performance of a vertical FLbased anomaly detector against different types of AML attacks, including denial-of-service attacks, adversarial data injection attacks, and replay attacks on the trained local models deployed in the grid network. For a case study, we consider the modified IEEE 13-bus system, and we develop SplitNN-based binary and multiclass classification models to detect, locate, and identify different types of data integrity attacks on the volt-watt control with two pooling layers: maximum pooling and AvgPool. Our experimental results, computed through performance metrics, reveal that the severity of these AML attacks varies with the integrated pooling mechanism, the type of classification model, and the nature of the cyberattack. Further, the AML attacks negatively impacted the prediction time per sample for the pretrained SplitNN during the online testing.

adversarial threats↗
NASA NTRS1986

Uniform relocation assistance and real property acquisition for Federal and federally-assisted programs

The 'Uniform Relocation Assistance and Real Property Acquisition for Federal and Federally-Assisted Programs' (14 CFR Part 1208) is set forth in this handbook and is hereby incorporated into the NASA Directives System. This handbook is applicable to all NASA installations. An initial supply of this handbook has been furnished to all NASA installations. Additional copies for internal use may be obtained through normal distribution channels.

Source record↗
DOE OSTI2025

Land of Opportunity: Potential for Renewable Energy on Federal Lands

Renewable energy (RE) in the United States has historically been deployed primarily on private lands, but the growing interest in RE development, generally across the country and specifically on federal lands, raises questions about the potential for RE on public lands. This study seeks to estimate RE technical potential on federal lands and project the amount of RE to be developed on federal lands under decarbonization scenarios for the contiguous United States. The study applied a combination of high-resolution geospatial analysis and power sector modeling and relied on multiple partner federal agencies and land administrators from the Bureau of Land Management, the U.S. Fish and Wildlife Service, the U.S. Forest Service, the U.S. Department of Defense, and the U.S. Department of Energy. In our reference siting access case, we estimate 44 million acres of federal land across the contiguous United States is potentially suitable for UPV development, which corresponds to a capacity technical potential of 5,750 gigawatts (GW) (Figure ES-1). Federal land area available for wind development is similar to UPV but wind’s generating capacity technical potential is lower (875 GW). The technical potential is estimated for two geothermal technologies, hydrothermal and enhanced geothermal systems (EGS), both of which have a smaller amount of federal land available for development (12 and 27 million acres, respectively). However, in terms of capacity, the technical potential for EGS (975 GW) is approximately equal to wind’s technical potential and there is an estimated 130 GW of hydrothermal potential. We also developed cases with more-limited land available for UPV and wind (for federal and non-federal lands) resulting in 96% reduction of wind capacity potential and 70% reduction for UPV. In a case with additional constraints applied to non-federal lands only (Limited Private), the overall (federal and non-federal) technical potential declines but the share of that technical potential on federal lands is higher than in the other siting cases. The technical potential is the maximum amount that could be developed, but only a small fraction would be developed or needed in the future. Across seven scenarios that achieve 100% carbon-free electricity by 2035, we estimate 26 GW to 270 GW of RE capacity could be deployed on federal lands by 2035. The three central scenarios have 51–84 GW of RE deployed by 2035 and requiring about 500,000 to 1,000,000 acres of total land area. Direct land consumption is less than total land use required, thus enabling co-use opportunities. The large technical potential estimates and the increasing deployment projections from the collection of scenarios show the opportunities for RE development on federal lands. Capturing these opportunities-while minimizing conflicts with other land uses, federal department or agency missions, and public interest, and simultaneously maximizing the economic, grid, and social value of the projects-would require collaborative planning among federal land administrators, grid planners, project developers, the public, and other stakeholders.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2025

Federal Home-to-Work Electric Vehicle Program Guide

This document serves as a comprehensive resource for Federal agencies in developing their own program resources that promote the efficient and effective use of electric vehicles (EVs) for home-to-work travel while ensuring compliance with Federal regulations and sustainability objectives. One mission of the U.S. Department of Energy's Federal Energy Management Program (FEMP) Fleet program is to help federal fleet managers meet or exceed statutory requirements related to energy and environmental performance while improving overall fleet efficiency, reducing costs, and meeting mission requirements. To further this mission, FEMP provides resources to support Federal agencies with increasing alternative fuel vehicle (AFV) acquisitions and reducing petroleum use. EVs are AFVs and help agencies meet federal fleet requirements. Federal fleets include government-owned EVs used for home-to-work travel. The purpose of this document is to serve as a guide for Federal agencies in developing their own internal program documents to manage government-owned EVs used for home-to-work travel. Federal agencies should consult their counsel and consider their own policies and authorities in the implementation of any policies or best practices regarding government-owned EVs used for home-to-work travel. The guide provides key considerations for agencies, including launching a pilot program to fine-tune best practices, conducting a cost-benefit analysis to compare home versus public charging, and exploring cost-effective solutions, such as installing standard outlets instead of dedicated charging stations. The guide underscores the importance of legal and financial considerations, such as verifying agency authority to install home charging infrastructure at an employee's home, ensuring the availability and appropriateness of using agency funds for home charging infrastructure, and understanding the tax implications of reimbursements.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
NASA NTRS2012

Three Dimensional Computer Graphics Federates for the 2012 Smackdown Simulation

The Simulation Interoperability Standards Organization (SISO) Smackdown is a two-year old annual event held at the 2012 Spring Simulation Interoperability Workshop (SIW). A primary objective of the Smackdown event is to provide college students with hands-on experience in developing distributed simulations using High Level Architecture (HLA). Participating for the second time, the University of Alabama in Huntsville (UAHuntsville) deployed four federates, two federates simulated a communications server and a lunar communications satellite with a radio. The other two federates generated 3D computer graphics displays for the communication satellite constellation and for the surface based lunar resupply mission. Using the Light-Weight Java Graphics Library, the satellite display federate presented a lunar-texture mapped sphere of the moon and four Telemetry Data Relay Satellites (TDRS), which received object attributes from the lunar communications satellite federate to drive their motion. The surface mission display federate was an enhanced version of the federate developed by ForwardSim, Inc. for the 2011 Smackdown simulation. Enhancements included a dead-reckoning algorithm and a visual indication of which communication satellite was in line of sight of Hadley Rille. This paper concentrates on these two federates by describing the functions, algorithms, HLA object attributes received from other federates, development experiences and recommendations for future, participating Smackdown teams.

Fordyce, Crystal↗
DOE OSTI2026

Enhancing segmentation fairness through curriculum learning and progressive loss: a centralized and federated perspective on radiograph analysis

Bias in medical image segmentation can lead to unequal performance across demographic subgroups, raising concerns about fairness and reliability in clinical AI systems. While deep learning models have achieved high segmentation accuracy, ensuring equitable performance across race and gender remains a significant challenge, particularly in privacy-sensitive healthcare environments. This study investigates fairness-aware medical image segmentation for hip and knee radiographs using deep learning models evaluated in both centralized and Federated Learning (FL) settings. We introduce Curriculum Learning (CL) strategies and Progressive Loss (PL) functions to regulate sample difficulty during training. In addition, we propose two novel fairness-oriented federated learning algorithms, Federated Intersection over Union (FedIoU) and Federated Intersection over Union with Outlier Analysis (FedIoUoutlier). Experiments are conducted using multiple segmentation backbones and simulated multi-site data partitions derived from the Osteoarthritis Initiative dataset. Model performance is evaluated using Intersection over Union (IoU), IoU standard deviation, Skewed Error Ratio (SER), and Min-Max Disparity across race and gender subgroups. Statistical significance was verified using paired t-tests to compare per-sample IoU performance against baseline configurations. Across both hip and knee segmentation tasks, curriculum learning and progressive loss strategies consistently improved segmentation accuracy and reduced demographic performance disparities in centralized training. In federated settings, fairness-aware aggregation further enhanced performance. Notably, FedIoUoutlier combined with balanced curriculum learning and tiered progressive loss achieved the highest mean IoU while yielding the lowest SER and Min-Max Disparity, indicating improved fairness without sacrificing accuracy. In several configurations, federated models matched or exceeded the performance of optimized centralized models, with statistically significant improvements in per-sample IoU over baseline configurations. The results demonstrate that structured training strategies and fairness-aware federated aggregation can jointly improve accuracy, stability, and demographic fairness in medical image segmentation. By integrating curriculum learning, progressive loss, and novel FL algorithms, this work provides a practical pathway toward equitable and privacy-preserving AI systems for medical imaging.

97 MATHEMATICS AND COMPUTING↗
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↗
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 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 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 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↗
NASA NTRS1998

Site Remediation Technology InfoBase: A Guide to Federal Programs, Information Resources, and Publications on Contaminated Site Cleanup Technologies. First Edition

Table of Contents: Federal Cleanup Programs; Federal Site Remediation Technology Development Assistance Programs; Federal Site Remediation Technology Development Electronic Data Bases; Federal Electronic Resources for Site Remediation Technology Information; Other Electronic Resources for Site Remediation Technology Information; Other Electronic Resources for Site Remediation Technology Information; Selected Bibliography: Federal Publication on Alternative and Innovative Site Remediation; and Appendix: Technology Program Contacts.

INFORMATION RESOURCES↗
DOE OSTI2025

CAFE AU LAIT: Compute-Aware Federated Augmented Low-Rank AI Training

Federated finetuning is crucial for unlocking the knowledge embedded in pretrained Large Language Models (LLMs) when data are geographically distributed across clients. Unlike finetuning with data from a single institution, federated finetuning allows collaboration across multiple institutions, enabling the utilization of diverse and decentralized datasets while preserving data privacy. Given the high computing costs of LLM training and the emphasis on energy efficiency in Federated Learning (FL), Low-Rank Adaptation (LoRA) has emerged as a widely adopted algorithm due to its significantly reduced number of trainable parameters. However, this assumes that all data silos have the necessary computing resources to compute local updates of LLMs. Nevertheless, in practice, the computing resources across clients are highly heterogeneous: while some may have access to hundreds of GPUs, others might have limited or no GPU access. Recently, federated finetuning using synthetic data has been proposed, allowing clients to participate in a collaborative training run without training LLMs locally. However, our experimental results reveal a performance gap between models trained using synthetic data and those trained using local updates. Motivated by the observed heterogeneity in computing resources and the performance gap, we propose a novel two-stage algorithm that leverages the storage and computing capabilities of a strong server. In the first stage, under the coordination of the strong server, clients with limited computing resources collaborate to generate synthetic data, which is transferred to and stored on the strong server. In the second stage, the strong server uses this synthetic data on behalf of the resource-constrained clients to perform federated LoRA finetuning alongside clients with sufficient computing resources. This approach ensures that all clients can participate in the finetuning process. Experimental results demonstrate that incorporating local updates from even a small fraction of clients improves performance compared to using synthetic data for all clients. Furthermore, we incorporate the Gaussian mechanism in both stages to guarantee client-level differential privacy.

Wang, Jiayi [ORNL]↗
NASA NTRS1989

Utilization and Maintenance of the Federal Catalog System (FCS)

The Federal Catalog System (FCS) was established and substantiated by law to aid the national economy and promote greater efficiency in supply management operations throughout the Federal Government. This Handbook establishes policies and procedures to be followed by NASA installations and certain contractors in cataloging items of supply in the Federal Catalog System and prescribes use of the system in supply management operations. This Handbook is not intended to duplicate the Federal Cataloging Manuals or Federal Cataloging Handbooks. For the most part, it describes actions that are peculiar to NASA.

Source record↗
NASA NTRS1995

Curation of Federally Owned Archeological Collections at NASA Langley Research Center

As a Federal agency, NASA has a moral and legal obligation to the public to manage the archeological heritage resources under its control. Archeological sites are unique, nonrenewable resources that must be preserved so that future generations may experience and interpret the material remains of the past. These sites are protected by a wide array of federal regulations. These regulations are intended to ensure that our nation's cultural heritage is preserved for the study and enjoyment of future generations. Once a site has been excavated, all that remains of it are the artifacts and associated records which, taken together, allow researchers to reconstruct the past. With the contextual information provided by associated records such as field notes, maps and photographs, archeological collections can provide important information about life in the past. An integral component of the federal archeology program is the curation of these databases so that qualified scholars will have access to them in years to come. Standards for the maintenance of archeological collections have been codified by various professional organizations and by the federal government. These guidelines focus on providing secure, climate-controlled archival storage conditions for the collections and an adequate study area in which researchers can examine the artifacts and documents. In the 1970's and early 1980's, a group of NASA employees formed the LRC Historical and Archeological Society (LRCHAS) in order to pursue studies of the colonial plantations that ha been displaced by Langley Research Center (LaRC). They collected data on family histories and land ownership as well as conducting archeological surveys and excavations at two important 17th-20th century plantation sites in LaRC, Cloverdale and Chesterville. The excavations produced a wealth of information in the form of artifacts, photographs, maps and other documents. Unfortunately, interest on the part of the LRCHAS membership waned before a report was written, and since 1982 the artifacts have moldered in a flimsy trailer with no climate controls, which had once served as a field laboratory but which threatened to become a tomb for the collection. A recent analysis of Langley's cultural resources by Gray & Pape, Inc. recommended that the collection be organized, cataloged, and placed in a proper curation facility in accordance with Federal regulations. The project for the LARSS program was to research curation standards, organize the collection, catalog it, and prepare it for transfer to a facility which could provide adequate long-term curation conditions for the artifacts and documents. The first phase was to organize the artifacts, which were lying about the lab in various stages of cleaning, analysis, and conservation. Once all of the artifacts from the various excavation units and levels had been regrouped, they were cleaned and/or repackaged in archivally-stable materials. A basic catalog was prepared which will provide interested parties with a rough idea of what we have and where it can be found. Another aspect of the project was to organize the records left by the LRCHAS. Bundles of papers, photographs, and field data found in every corner and drawer of the laboratory trailer were put into order and, where appropriate, copies were made on acid-free Permabond paper for long term storage. Finally, the entire collection and most of the lab equipment was transferred into a secure, climate controlled room which will serve as an archive and study space for qualified scholars interested in exploring LaRC's rich historical heritage.

Eastman, John Arnold↗
NASA NTRS1998

NASA/DOD Aerospace Knowledge Diffusion Research Project. Paper 67: Maximizing the Results of Federally-Funded Research and Development Through Knowledge Management: A Strategic Imperative for Improving US Competitiveness

Federally-funded research and development (R&D) represents a significant annual investment (approximately $79 billion in fiscal year 1996) on the part of U.S. taxpayers. Based on the results of a 10-year study of knowledge diffusion in U.S. aerospace industry, the authors take the position that U.S. competitiveness will be enhanced if knowledge management strategies, employed within a capability-enhancing U.S. technology policy framework, are applied to diffusing the results of federally-funded R&D. In making their case, the authors stress the importance of knowledge as the source of competitive advantage in today's global economy. Next, they offer a practice-based definition of knowledge management and discuss three current approaches to knowledge management implementation-mechanistic, "the learning organization," and systemic. The authors then examine three weaknesses in existing U.S. public policy and policy implementation-the dominance of knowledge creation, the need for diffusion-oriented technology policy, and the prevalence of a dissemination model- that affect diffusion of the results of federally-funded R&D. To address these shortcomings, they propose the development of a knowledge management framework for diffusing the results of federally-funded R&D. The article closes with a discussion of some issues and challenges associated with implementing a knowledge management framework for diffusing the results of federally-funded R&D.

Pinelli, Thomas E.↗