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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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NASA NTRS1994

NADA/DOD Aerospace Knowledge Diffusion Research Project. Report number 19: The US government technical report and the transfer of federally funded aerospace R/D: An analysis of five studies

The U.S. government technical report is a primary means by which the results of federally funded research and development are transferred to the U.S. aerospace industry. However, little is known about this information product in terms of its actual use, importance, and value in the transfer of federally funded R&D. To help establish a body of knowledge, the U.S. government technical report is being investigated as part of the 'NASA/DoD Aerospace Knowledge Diffusion Research Project'. In this report, we summarize the literature on technical reprts and provide a model that depicts the transfer of federally funded aerospace R&D via the U.S. government technical report. We present results from five studies of our investigation of aerospace knowledge diffusion vis-a-vis the U.S. government technical report and close with a brief overview of on-going research into the use of the U.S. government technical report as a rhetorical device for transferring federally funded aerospace R&D.

Pinelli, Thomas E.↗

NASA NTRS1991

NASA/DOD Aerospace Knowledge Diffusion Research Project. Paper 12: The diffusion of federally funded aerospace research and development (R/D) and the information seeking behavior of US aerospace engineers and scientists

In this paper, the diffusion of federally funded aerospace R&D is explored from the perspective of the information-seeking behavior of U.S. aerospace engineers and scientists. The following three assumptions frame this exploration: (1) knowledge production, transfer, and utilization are equally important components of the aerospace R&D process; (2) the diffusion of knowledge resulting from federally funded aerospace R&D is indispensable for the U.S. to remain a world leader in aerospace; and (3) U.S. government technical reports, produced by NASA and DOD, play an important, but as yet undefined, role in the diffusion of federally funded aerospace R&D. A conceptual model for federally funded aerospace knowledge diffusion, one that emphasizes U.S. goverment technical reports, is presented. Data regarding three research questions concerning the information-seeking behavior of U.S. aerospace engineers and scientists are also presented.

Pinelli, Thomas E.↗

NASA NTRS1966

An Analysis of the Impact of the Federal Budgetary Cycle Upon the NASA Manned Spacecraft Center Research and Development Budget Formulation Process

The subsequent dissertation represents an analysis of the impact of the Federal Budgetary Cycle upon the National Aeronautics and Space Administration (NASA) Manned Spacecraft Center (MSC) Research and Development (R&D) budget formulation process. The author's objectives may therefore be seen as the following: (1) to analyze the Federal Budgetary Cycle; (2) to analyze MSC R&D estimates and growth trends in relation to their implications on the Federal Cycle; (3) to identify relevant problems; and, (4) to-recommend solutions which display promise and feasibility. Any research involving the Federal Budgetary Cycle can well be characterized as of almost infinite scope and enormous complexity. For such reasons one must meticulously delineate all operational parameters and there-afore maintain their integrity. To do otherwise is to invite intellectual dilution and hazard a paltry effort.

Peterson, Ronald Keith↗

NASA NTRS2019

Federated Cloud Challenges in NASA's Earth Science Data Systems (Why So Difficult?)

NASA is presented with a number of opportunities and challenges in federating its Earth Science Data Systems in the burgeoning world of cloud computing. Cloud hosting of Earth Science data provides a new way of bringing data together, at least from a virtual location sense, and is one of the main motives for NASA to host data there. However, NASA is also faced with a Big Data Variety challenge, brought on by the variety of the EO datasets in its archives. This diversity requires many diverse science archives to service the different science communities. As a result, nearly every major function in the Earth Observing System Data and Information System (EOSDIS) must also be federated across its data centers. This pattern is repeated with many of the outside agencies and organizations that EOSDIS federates with, such as the Committee for Earth Observing Satellites, leading to pioneering work on "deep federation" in a joint project with the European Space Agency to develop a Multi-Mission Algorithm and Analysis Platform.

Lynnes, Christopher↗

NASA NTRSDate not supplied

Using Federated Learning to Overcome Data Gravity in Space

Humans intend to take longer missions to outer space. Understanding the impact that space has on human health is paramount to the success of these missions. Controlled experiments with model organisms are run to infer the impact of space conditions on human health, but the data these experiments generate are too large to transfer to Earth for building models. The same is true for space-relevant data generated on Earth. Ideally, these datasets should be combined to improve statistical power and model accuracy without having to transfer data. Federated learning is such a method which trains an algorithm across decentralized computing systems, each of which has their own local copy of training and testing data. In this research, made possible by NASA@Work, the AI for Life in Space group at NASA demonstrates the use of federated learning to train an ensemble of causality inference models on a combination of data residing on the International Space Station (ISS) and in the cloud. Our work leverages CRISP, a causal inference platform developed during the 2020 Frontier Development Lab’s “Astronaut Health Challenge.” We also leverage the OpenFL federated learning library which was collaboratively developed at Intel and UPenn. We used publicly available data from the NASA Ames Life Sciences Data Archive to identify features in ionizing radiation experiments as causal of changes in cardiac blood velocity. This research demonstrates, for the first time, the possibility of running machine learning algorithms on datasets separated by astronomical distances. In this experiment, all the data were generated in terra, half of which were transferred to the ISS and analyzed on the Spaceborne Computer. In the future, our research will leverage federated learning on data generated in situ on the ISS with data generated terrestrially to predict the impact of spaceflight on mammalian female reproductive capacity.

James Casaletto↗

DOE OSTI2020

AI-Science for Performance Optimization and Diagnosis of Science Instrument Federations

Next generation of science workflows are expected to be executed over complex federations composed of supercomputers, science instruments, storage systems and networks, with new additions of the edge and cloud systems and services. The sheer complexity of these multi-domain federations makes it hard to manage them and optimize their performance, as small impedance mismatches (that can dynamically develop between systems) could drastically degrade the entire federation performance. Recent proliferation of Software Defined Everything (SDX) technologies combined with containerization frameworks provide custom instruments that can monitor and collect critical measurements at various levels to support diagnoses and performance optimization; but their data too enormous for human operators and analysts to process and generate decisions. Machine Learning (ML) methods that extract critical parameters, relationships and trends from the data offer general solutions. Artificial Intelligence (AI) and ML methods must be custom-developed for these problems based on solid, rigorous foundations, since black-box approaches are often ineffective and unsound.We propose to develop comprehensive AI-Science for the performance of science federations to (i) monitor and control storage, networks, experiments, and computing systems across multiple domains via softwarization layers, at speeds and scales orders of magnitude superior to current practice, (ii) optimally realize and orchestrate complex workflows with high performance by using dynamic state and performance estimation methods, and (iii) aggregate measurements across sites and time to develop infrastructure-level profiles, optimizations and diagnoses using AI-Science based on foundational principles from ML, game theory, and information fusion areas.

Rao, Nageswara↗

DOE OSTI2020

Federal Utility Partnerships & Clean Production of Natural Gas

Karen Thomas from NREL will discuss utility partnership programs with the federal government under the Federal Energy Management Program (FEMP) of the Department of Energy (DOE). These programs offer a way for a utility to partner with federal agencies. She will describe the federal government's ability to work directly with their serving natural gas utility to implement successful energy efficiency projects. Also, Jill Engel-Cox from NREL's Joint Institute for Strategic Energy Analysis will briefly share their analysis to identify opportunities to reduce emissions and increase efficiency of the production and delivery of natural gas.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2021

Science Federation Emulation Testbed: Demonstration of VFSIE Functionalities

The Virtual Federated Science Instrument Environment (VFSIE) is a digital twin of a federated infrastructure of multiple sites with science instruments and computing systems that are geographically dispersed. It emulates the federation using containers and virtual hosts (vhosts) connected over local and wide-area networks for the main purpose of supporting the development and testing of science workflows and software stacks, which will subsequently transition to the physical infrastructure. In this demo, we present VFSIE of a four sites scenario and illustrate the following use cases: (i) execution of federation stack components that expose resources to enable a science user to execute workflows, (ii) execution of remote control commands of beam-line instruments to collect measurements and position the sensor, and (iii) remote execution and access of Jupyter Notebook for tomographic image computations

Al Najjar, Anees↗

DOE OSTI2020

Federal Activities Report on the Bioeconomy: Algae

The Algae Interagency Working Group under the Biomass Research and Development (BR&D) Board meets to discuss U.S. federal funding and regulatory activities to increase awareness and coordination among algae relevant government programs. The Federal Activities Report on the Bioeconomy: Algae is a follow on report to the BR&D Board’s Federal Activities Report on the Bioeconomy that summarizes Bioeconomy Initiative activities for the algae stakeholder community.

09 BIOMASS FUELS↗

DOE OSTI2022

INTERFUEL: FY22 Federal Fleet Reporting [Slides]

This presentation presents an overview of and timeline for coming changes to how federal agencies with motor vehicles will submit required information about the motor vehicles, fueling centers, and electric vehicle supply equipment (EVSE) inventory through the Federal Automotive Statistical Tool (FAST). The presentation provides information on changes throughout the FAST system as well as changes planned for implementation in FY 2023 and later. This presentation is intended for delivery via WebEx/GoToMeeting at the September 14, 2022 meeting of the DOE-sponsored INTERFUEL working group. FAST is a web-based information management tool developed and maintained by Idaho National Laboratory and funded by GSA's Office of Government-wide Policy and DOE's Federal Energy Management Program.

99 GENERAL AND MISCELLANEOUS↗

DOE OSTI2022

FedFleet 2022: Federal Automotive Statistical Tool - FY 2021 Fleet Trends and Data Quality

This presentation presents a brief overview of the collection of information about the US government's fleet of motor vehicles using the Federal Automotive Statistical Tool (FAST), discusses the makeup and operation of the vehicle fleet during FY 2021, discusses quality of the submitted data, and touches on future aspects of fleet data collection and reporting. 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; FAST is developed, maintained, and supported by DOE's Idaho National Laboratory (INL).

99 GENERAL AND MISCELLANEOUS↗

DOE OSTI2025

American Made Energy Infrastructure - Evolution of Federal Incentives and Requirements

This presentation, "Evolution of Federal Incentives and Requirements," explores the development and impact of federal cybersecurity regulations, tax credits, and domestic content requirements on the energy sector. It covers key legislation such as the ARRA of 2009, IIJA, and IRA, and their implications for grid modernization, manufacturing, and deployment of energy technologies. The presentation also addresses definitions and restrictions related to Foreign Entities of Concern (FEOC) and their impact on federal procurement. Additionally, it introduces the DOE's cybersecurity framework for energy supply chains and practical actions for compliance planning.

25 - ENERGY STORAGE↗

DOE OSTI2026

FIRM: federated image reconstruction using multimodal tomographic data

Here, we propose a federated algorithm for reconstructing images using multimodal tomographic data sourced from dispersed locations, addressing the challenges of traditional unimodal approaches that are prone to noise and reduced image quality, as well as the limitations of centralized multimodal approaches that require extensive data transfer, leading to significant communication overhead, storage demands, and potential data privacy concerns. Our approach formulates a joint inverse optimization problem incorporating multimodality constraints and solves it in a federated framework through local gradient computations complemented by lightweight central operations, thereby ensuring data decentralization. Leveraging the connection between our federated algorithm and the quadratic penalty method, we introduce an adaptive step-size rule with guaranteed sublinear convergence. Numerical results demonstrate superior computational efficiency and improved image reconstruction quality compared to existing approaches.

federated algorithm↗

DOE OSTI2025

FedEFsz: Fair Cross-Silo Federated Learning System With Error-Bounded Lossy Compression

Cross-Silo federated learning systems have been identified as an efficient approach to scaling DNN training across geographically-distributed data silos to preserve the privacy of the training data. Communication efficiency and fairness are two major issues that need to be both satisfied when federated learning systems are deployed in practice. Simultaneously guaranteeing both of them, however, is exceptionally difficult because simply combining communication reduction and fairness optimization approaches often causes non-converged training or drastic accuracy degradation. Here, to bridge this gap, we propose FedEFsz. On the one hand, it integrates the state-of-the-art error-bounded lossy compressor SZ3 into cross-silo federated learning systems to significantly reduce communication traffic during the training. On the other hand, it achieves a high fairness (i.e., rather consistent model accuracy and performance across different clients) through a carefully designed heuristic algorithm that can tune the error-bound of SZ3 for different clients during the training. Extensive experimental results based on a GPU cluster with 65 GPU cards show that FedEFsz improves the fairness across different benchmarks by up to 60.88% and meanwhile reduces the communication traffic by up to 315×.

Cross-Silo Federated Learning Systems↗

DOE OSTI2023

Federal Automotive Statisical Tool: FAST Changes & Data Call Preparation (INTERFUEL - September 2023) [Slides]

This presentation presents an overview of and timeline for coming changes to how federal agencies with motor vehicles will submit required information about the motor vehicles, fueling centers, and electric vehicle supply equipment (EVSE) inventory through the Federal Automotive Statistical Tool (FAST). The presentation provides information on changes throughout the FAST system as well as changes planned for implementation in FY 2023 and later. This presentation is intended for delivery via WebEx/GoToMeeting at the September 13, 2023 meeting of the DOE-sponsored INTERFUEL working group. FAST is a web-based information management tool developed and maintained by Idaho National Laboratory and funded by GSA's Office of Government-wide Policy and DOE's Federal Energy Management Program.

33 ADVANCED PROPULSION SYSTEMS↗

DOE OSTI2025

Hourly Load Profile Dataset for Federal, State, and Municipal Electric Vehicle Fleets in the United States

The electrification of U.S. federal, state, and municipal fleets is accelerating rapidly, driven by an increased availability of competitive electric vehicle (EV) options and supportive policies and targets. The dataset described in this report, accessible at data.nrel.gov/submissions/280, provides a critical foundation for identifying fleet electricity demand, projecting these future demands, and developing actionable strategies to support the widespread electrification of government fleets. The dataset incorporates available fleet data, including 54% of federal agency vehicles approved for analysis (notably, the U.S. Postal Service is absent). Additionally, it includes data from 50,000 state government vehicles and 94,000 local government vehicles. While this represents a small fraction of the 4.4 million vehicles owned by state and local governments reported by the Federal Highway Administration (2022), the framework supports future expansion as more fleet inventory data become available.

33 ADVANCED PROPULSION SYSTEMS↗

NASA NTRS2003

NASA's Secured Advanced Federated Environment

In 1999, a NASA-wide team initially set out to create a collaborative environment to enable NASA's scientists and engineers to share information and tools across NASA locations and with world-wide partners. This paper describes the team's development process and solutions in resolving conflicting security issues of building a complex intra/inter-enterprise collaborative system. Based on the federated, hierarchical, compartmentalized principles, the Secured Advanced Federated Environment (SAFE) developed by the team is becoming a foundational element for building a collaborative infrastructure for NASA. This paper also introduces the concept of a Micro Security Domain which can achieve the balance between the need to collaborate and the need to enforce enterprise and local security rules. SAFE'S federated security concepts enables networks to be formed around the functional/security requirements. With the SAFE technologies and approaches, security will not be an afterthought of the enterprise network design.

network designs↗

NASA NTRS2012

The Earth System Grid Federation : an Open Infrastructure for Access to Distributed Geospatial Data

The Earth System Grid Federation (ESGF) is a multi-agency, international collaboration that aims at developing the software infrastructure needed to facilitate and empower the study of climate change on a global scale. The ESGF's architecture employs a system of geographically distributed peer nodes, which are independently administered yet united by the adoption of common federation protocols and application programming interfaces (APIs). The cornerstones of its interoperability are the peer-to-peer messaging that is continuously exchanged among all nodes in the federation; a shared architecture and API for search and discovery; and a security infrastructure based on industry standards (OpenID, SSL, GSI and SAML). The ESGF software is developed collaboratively across institutional boundaries and made available to the community as open source. It has now been adopted by multiple Earth science projects and allows access to petabytes of geophysical data, including the entire model output used for the next international assessment report on climate change (IPCC-AR5) and a suite of satellite observations (obs4MIPs) and reanalysis data sets (ANA4MIPs).

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