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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 NTRS1997

Summary of Research Report Cooperative Agreement

Several areas of work related to commercialization of technology developed at NASA Ames Research Center (ARC) are discussed in this report. The areas are: (1) perform a feasibility study to develop a software commercialization center is at ARC; (2) perform preliminary work for formation of joint development of sensor technology for telemedicine applications; (3) development of a discovery interview process and staff training to assist the commercialization of technology developed at Ames, specifically aimed at working with researchers; (4) develop partners to further develop and commercialize image compression technology developed at AMES; (5) assist efforts to commercialize a software technology which imparts the ability to establish relevance-based retrieval in the handling of large repositories of information; (6) explore the development of cryocooler technology using pulse tube refrigeration; (7) assess interest in commercialization of a new method of measuring skin friction drag on wind tunnel models using liquid crystal material; (8) attempt to incorporate emerging technologies in the infrastructure of natural hazards mitigation; and (9) forming a nonprofit organization, "The Bootstrap Alliance", whose mission is to promote the use of digital technologies for collaborative problem solving. The results of these initiatives are discussed.

Source record↗

NASA NTRS2009

ISAAC - A Case of Highly-Reusable, Highly-Capable Computing and Control Platform for Radar Applications

ISAAC is a highly capable, highly reusable, modular, and integrated FPGA-based common instrument control and computing platform for a wide range of instrument needs as defined in the Earth Science National Research Council (NRC) Decadal Survey Report. This paper presents its motivation, technical approach, and the infrastructure elements. It also describes the first prototype, ISAAC I, and its application in the design of SMAP L-band radar digital filter.

He, Yutao↗

NASA NTRS2016

Advanced Manufacturing Technologies

Advanced Manufacturing Technologies (AMT) is developing and maturing innovative and advanced manufacturing technologies that will enable more capable and lower-cost spacecraft, launch vehicles and infrastructure to enable exploration missions. The technologies will utilize cutting edge materials and emerging capabilities including metallic processes, additive manufacturing, composites, and digital manufacturing. The AMT project supports the National Manufacturing Initiative involving collaboration with other government agencies.

Materials↗

DOE OSTI2025

White Paper: Scalable Digital Twin Capabilities for Aging and Surveillance of Engineered Systems

This white paper presents a multi-year initiative to develop practical, secure, and scalable digital twin capabilities for engineered systems in aging and surveillance contexts—an approach pioneered at the National Nuclear Security Administration (NNSA) Lawrence Livermore National Laboratory (LLNL) that maps directly onto the needs and ambitions of the Navy for ship- and fleet-level digital twins. LLNL’s work in building part- and process-level digital twins for advanced manufacturing, with a vision to scale up to entire factory floors and, ultimately, enterprise-wide digital twins, offers an adaptable pathway for the Navy as it seeks to modernize lifecycle management, readiness, and predictive maintenance across ships and fleets. For our application, we integrate physics-based modeling with automated data ingestion, processing, and AI-driven calibration, creating hybrid models that are both interpretable and data responsive. We modernized legacy workflows, established centralized data infrastructure, automated experimental pipelines, and demonstrated end-to-end coupling of accelerated aging data with finite element simulations via optimization and surrogate modeling. The result is a generalizable framework that supports part-level digital twins today and lays the groundwork for future system-level twins suitable for Navy applications.

36 MATERIALS SCIENCE↗

DOE OSTI2024

Leveraging generative AI for urban digital twins: a scoping review on the autonomous generation of urban data, scenarios, designs, and 3D city models for smart city advancement

The digital transformation of modern cities by integrating advanced information, communication, and computing technologies has marked the epoch of data-driven smart city applications for efficient and sustainable urban management. Despite their effectiveness, these applications often rely on massive amounts of high-dimensional and multi-domain data for monitoring and characterizing different urban sub-systems, presenting challenges in application areas that are limited by data quality and availability, as well as costly efforts for generating urban scenarios and design alternatives. As an emerging research area in deep learning, Generative Artificial Intelligence (GenAI) models have demonstrated their unique values in content generation. This paper aims to explore the innovative integration of GenAI techniques and urban digital twins to address challenges in the planning and management of built environments with focuses on various urban sub-systems, such as transportation, energy, water, and building and infrastructure. The survey starts with the introduction of cutting-edge generative AI models, such as the Generative Adversarial Networks (GAN), Variational Autoencoders (VAEs), Generative Pre-trained Transformer (GPT), followed by a scoping review of the existing urban science applications that leverage the intelligent and autonomous capability of these techniques to facilitate the research, operations, and management of critical urban subsystems, as well as the holistic planning and design of the built environment. Based on the review, we discuss potential opportunities and technical strategies that integrate GenAI models into the next-generation urban digital twins for more intelligent, scalable, and automated smart city development and management.

3D city modeling↗

DOE OSTI2025

NFPA Distributed Energy Resources Safety Training (DERST) For Emergency Responders

The National Fire Protection Association, with support from the Department of Energy, executed a multi-year initiative to develop, enhance, and disseminate Distributed Energy Resources Safety Training (DERST) tools for U.S. emergency responders. As Distributed Energy Resources (DER)—such as solar photovoltaics, battery energy storage systems (ESS), electric vehicles (EVs), and associated infrastructure—become increasingly prevalent, the NFPA identified a critical need for up-to-date standardized, accessible, and effective safety training tailored for the fire service and related public safety professionals. The project delivered a comprehensive suite of educational resources to improve responders’ abilities to safely manage DER-related incidents. This included: • Revised Modular Training Courses: Updated classroom-based DER safety courses, now modular and accessible nationwide through fire academies and the North American Fire Training Directors (NAFTD) network. • Live Burn Testing & Research: A full-scale controlled burn of a DER-equipped residential structure provided real-world data and insights, forming the basis for updated best practices. • A Gamified Simulation Tool – Firefighters Incident Response Simulation Tool (FIRST): A first-of-its-kind, multiplayer, scenario-based simulation using the Unreal Engine 5.0 to train responders in a realistic virtual, multi-DER incident environment. • Field Familiarization Software Tools & Prop Guide: Digital DER field familiarization evolutions software guide and a prop development manual to support field-based DER training exercises, enhancing responders' hands-on familiarity with DER infrastructure and collaboration on virtual incident responses. • National Dissemination Strategy: Strategic partnerships with NAFTD, Vector Solutions, and others enabled wide-scale distribution, with over 5,000 departments accessing resources and 1,100+ departments adopting the simulator in the first seven months. Also provided a web portal for easy access to all training and simulation programs developed under this grant for the U.S. responder community. Key findings from the project—particularly from the burn test—led to paradigm shifts in fire response tactics. For example, traditional approaches to garage fires may be hazardous if DERs are present, due to explosive off gassing and thermal runaway risks. The new training emphasizes scene assessment, stand-off approaches, thermal imaging verification, and careful post-incident cooling of DER components to prevent reignition. This initiative has had a significant national impact, raising awareness, enhancing preparedness, and supporting safer DER incident response practices. Significant engagement from the media, public safety organizations, and PBS coverage has further amplified the reach and adoption of NFPA’s DER safety training, tools, and simulations.

14 SOLAR ENERGY↗

NASA NTRS1998

Modeling, Simulation and Analysis of Public Key Infrastructure

Security is an essential part of network communication. The advances in cryptography have provided solutions to many of the network security requirements. Public Key Infrastructure (PKI) is the foundation of the cryptography applications. The main objective of this research is to design a model to simulate a reliable, scalable, manageable, and high-performance public key infrastructure. We build a model to simulate the NASA public key infrastructure by using SimProcess and MatLab Software. The simulation is from top level all the way down to the computation needed for encryption, decryption, digital signature, and secure web server. The application of secure web server could be utilized in wireless communications. The results of the simulation are analyzed and confirmed by using queueing theory.

Liu, Yuan-Kwei↗

DOE OSTI2024

Efficient Anomaly Detection Driven By Different Machine Learning Architectures And Models

The rapid growth and ubiquitous adoption of the internet and cyber-physical systems (CPS) have fundamentally transformed modern communication, work, and human-system interactions. While networks now form the backbone of critical digital ecosystems, enabling seamless data transmission across diverse, interconnected systems, this increased connectivity also expands the attack surface, making real-time detection of network intrusions and anomalies a pressing challenge. Detecting unusual activities within network infrastructure requires advanced data traffic analysis to differentiate between legitimate and malicious interactions. Traditional approaches to network anomaly detectionâ??such as rule-based and signature-based systemsâ??often depend on predefined patterns to identify known anomalies, limiting their effectiveness against emerging, stealthy, or previously unseen threats. These conventional methods suffer from high false alarm rates and fail to adapt to the ever-evolving nature of network traffic, particularly in large-scale, decentralized environments where data volume, velocity, and variety are constantly increasing. This dissertation presents artificial intelligence (AI)-driven approaches to anomaly detection that leverage graphics processing unit (GPU)-enabled high-performance computing (HPC) platforms for processing massive network traffic data and monitoring the components of cyber-physical systems (CPS) for potentially hazardous conditions. The research advances several key contributions: (1) Designing efficient machine learning techniques for CPS condition monitoring and anomaly detection; (2) enabling federated learning (FL) frameworks that enable distributed detection while preserving data privacy and system resilience; (3) exploring graph-based methodologies combining graph neural networks (GNN) and graph machine learning (ML) approaches for the Internet of Things (IoT) and automotive network security, and (4) performing distributed edge computing optimizations that integrate FL with scalable technologies for reduced communication overhead. Through extensive experiments, these methodologies demonstrate that complex anomaly detection and condition monitoring tasks can be achieved while balancing computational efficiency and detection accuracy through fine-grained network information processing. The frameworks developed in this research establish a robust foundation for network anomaly detection, providing scalable, adaptive, and privacy-preserving solutions for safeguarding CPS and IoT networks in an increasingly interconnected digital landscape. The practical implications of these research findings are significant, as they can inform the development of next-generation network security systems and contribute to the protection of critical infrastructure against sophisticated cyber attacks.

Marfo, William↗

DOE OSTI2026

Cyber-Informed Engineering: Incorporating CIE into Engineering Curricula

Cyber-Informed Engineering (CIE) is an engineering approach that mitigates the consequences of cyber risk to critical infrastructure by integrating engineered controls into system design and operation. CIE-focused education is necessary to prepare future engineers and technicians to understand and mitigate digital risk in modern engineered systems. This session explores how universities can incorporate CIE into their curricula, provides examples of how existing universities are already leveraging CIE in their programs, and highlights resources to support adoption.

99 - GENERAL AND MISCELLANEOUS↗

DOE OSTI2025

Grid Communications: Digital Assurance and Supply Chain Challenges and Emerging Regulation Session Two

The TADA Grid Communications Workshops are designed to strengthen cybersecurity and digital assurance across the energy sector by focusing on secure deployment and management of grid communications technologies. These workshops bring together state energy offices, utilities, and technology suppliers to explore the intersection of communications infrastructure, supply chain risks, and emerging regulatory requirements. Participants will apply Cyber-Informed Engineering (CIE) principles to reduce risks in communications systems, engage with INL’s procurement guidance, and explore future tools. Through scenario-based exercises and peer exchange, attendees will develop risk-based security strategies and actionable compliance roadmaps tailored to their grid communications projects. The workshops also help participants navigate evolving regulatory frameworks such as FEOC rules in the OBBB, NERC CIP-013, and NDAA 2024, while identifying compliance gaps in mixed-technology environments. A key outcome is the formation of a practitioner network with ongoing access to INL expertise and resources, fostering long-term resilience in the digital energy ecosystem. This is Session 2 of 3 (Full Version).

29 - ENERGY PLANNING, POLICY AND ECONOMY↗

DOE OSTI2025

Grid Communications: Cybersecurity and Supply Chain Challenges and Emerging Regulation Session Three

29 - ENERGY PLANNING, POLICY AND ECONOMY↗

DOE OSTI2025

Grid Communications Supply Chain & Emerging Regulation Challenges Session 1

29 - ENERGY PLANNING, POLICY AND ECONOMY↗

NASA NTRS2011

Space-Borne Radio-Sounding Investigations Facilitated by the Virtual Wave Observatory (VWO)

The goal of the Virtual Wave Observatory (VWO) is to provide userfriendly access to heliophysics wave data. While the VWO initially emphasized the vast quantity of wave data obtained from passive receivers, the VWO infrastructure can also be used to access active sounder data sets. Here we use examples from some half-million Alouette-2, ISIS-1, and ISIS-2 digital topside-sounder ionograms to demonstrate the desirability of such access to the actual ionograms for investigations of both natural and sounder-stimulated plasma-wave phenomena. By this demonstration, we wish to encourage investigators to make other valuable space-borne sounder data sets accessible via the VWO.

Benson, Robert F.↗

NASA NTRSDate not supplied

Materials Informatics at NASA GRC: Machine Learning Surrogate Modeling, Data Management, and Integrated Toolsets for Establishing/Maintaining the Digital Thread

Integrated Computational Materials Engineering (ICME) has recently received widespread attention due to its promises in reducing dependence on physical testing for engineering design by relying on simulation, reducing both time and cost to market for various applications. ICME however requires validated multiscale material models, which heavily depend on available test data with full material and test pedigree, including material processing, test and measurement equipment, raw data collection, and analysis methodology and results that is findable and usable, along with integrated, efficient toolsets for effectively passing information across various length and time scales across such models. At the NASA Glenn Research Center under the Transformational Tools and Technologies Project, significant recent efforts have been directed towards establishing the required cyberinfrastructure to enable optimized ICME processes and the design of “fit-for-purpose” materials to achieve the goals outlined in the NASA Vision 2040 report. Such efforts include development of multiscale physics-based material models, which can be used to train highly efficient surrogate machine learning models, development of best practices and infrastructure for effective, traceable materials information management, and development of toolsets that integrate with physics-based codes, machine learning models, and an information management system to enable high throughput of materials data collection and analysis, establishment of digital twins and the digital thread, and automation of the ICME design process for material optimization.

Machine Learning↗

DOE OSTI2026

Alabama Carbon Storage: Data Sharing and Engagement (Final Report)

This report is the final technical report on Alabama Carbon Storage: Data Sharing Engagement (ACS:DSE) project activities. The goals of the ACS:DSE project are to compile geologic, geophysical, infrastructure, and other relevant CCUS datasets for the study area and develop a geologic model of the study area; develop an online platform to serve data to stakeholders; engage with the public, students, and industry to educate them about CCUS and the data platform; and ensure energy and environmental justice is central to all aspects of the project. Datasets compiled and expanded include formation depths and elevations, digital geophysical well logs, reservoir properties, geologic structures, and geologic models. The geologic data were used to create a three-dimensional geologic model, structure grids, structure contour maps, and fault trace maps. In addition to downloadable datasets, links to CCUS relevant regulatory agencies (e.g., OGB, U.S. Environmental Protection Agency) and sources for infrastructure and educational information were included on the website Educational materials on CCUS for use by K-12 teachers were produced as part of the ACS:DSE project.

01 COAL, LIGNITE, AND PEAT↗

DOE OSTI2025

Synopsis of NREL's Automated Mobility District (AMD) Research Program and Associated Publications

An automated mobility district (AMD) envisions a system of integrated mobility options that serves major activity centers such as campuses, central business districts, and large medical facilities. The National Renewable Energy Laboratory (NREL) has been investigating the implementation prospects for fully automated passenger transport systems that are deployed to operate within dense urban settings. This document provides a synopsis of findings revealed over the last three phases of work, which have yielded insights into the creation and management of AMDs anticipated to use automated vehicle (AV) technology over the next decade. Phase I and Phase II tracked the deployment and lessons learned from 10 early-stage demonstrations of automated shuttle deployments, and their associated insights into the challenges for automated driving systems to achieve safe operations within district-scale deployments. Phase III began in-depth investigations of critical subsystem components, as automation, electrification, and on-demand service continue to converge within initial AMD operations. These directed studies focus on elements of electrification, curbfront/station management, the role of infrastructure sensing, and overall integration of AMD safety management in central, simultaneous coordination of multiple AMD fleets. Future research in AMDs includes systems engineering methodology (more frequently referred to as "digital twins") for planning, design, testing, and ongoing operation of AMDs; location (or co-location) of management functions; and human supervision and passenger communications for safety and security in unattended vehicles. The synopsis references the foundational research products (papers and presentations) that have been published through conference proceedings, journal articles, and NREL reports.

33 ADVANCED PROPULSION SYSTEMS↗

NASA NTRS2012

Investigation into Cloud Computing for More Robust Automated Bulk Image Geoprocessing

Geospatial resource assessments frequently require timely geospatial data processing that involves large multivariate remote sensing data sets. In particular, for disasters, response requires rapid access to large data volumes, substantial storage space and high performance processing capability. The processing and distribution of this data into usable information products requires a processing pipeline that can efficiently manage the required storage, computing utilities, and data handling requirements. In recent years, with the availability of cloud computing technology, cloud processing platforms have made available a powerful new computing infrastructure resource that can meet this need. To assess the utility of this resource, this project investigates cloud computing platforms for bulk, automated geoprocessing capabilities with respect to data handling and application development requirements. This presentation is of work being conducted by Applied Sciences Program Office at NASA-Stennis Space Center. A prototypical set of image manipulation and transformation processes that incorporate sample Unmanned Airborne System data were developed to create value-added products and tested for implementation on the "cloud". This project outlines the steps involved in creating and testing of open source software developed process code on a local prototype platform, and then transitioning this code with associated environment requirements into an analogous, but memory and processor enhanced cloud platform. A data processing cloud was used to store both standard digital camera panchromatic and multi-band image data, which were subsequently subjected to standard image processing functions such as NDVI (Normalized Difference Vegetation Index), NDMI (Normalized Difference Moisture Index), band stacking, reprojection, and other similar type data processes. Cloud infrastructure service providers were evaluated by taking these locally tested processing functions, and then applying them to a given cloud-enabled infrastructure to assesses and compare environment setup options and enabled technologies. This project reviews findings that were observed when cloud platforms were evaluated for bulk geoprocessing capabilities based on data handling and application development requirements.

Brown, Richard B.↗

NASA NTRS2014

Data Preservation, Information Preservation, and Lifecyle of Information Management at NASA GES DISC

Data lifecycle management awareness is common today; planners are more likely to consider lifecycle issues at mission start. NASA remote sensing missions are typically subject to life cycle management plans of the Distributed Active Archive Center (DAAC), and NASA invests in these national centers for the long-term safeguarding and benefit of future generations. As stewards of older missions, it is incumbent upon us to ensure that a comprehensive enough set of information is being preserved to prevent the risk for information loss. This risk is greater when the original data experts have moved on or are no longer available. Preservation of items like documentation related to processing algorithms, pre-flight calibration data, or input-output configuration parameters used in product generation, are examples of digital artifacts that are sometimes not fully preserved. This is the grey area of information preservation; the importance of these items is not always clear and requires careful consideration. Missing important metadata about intermediate steps used to derive a product could lead to serious challenges in the reproducibility of results or conclusions. Organizations are rapidly recognizing that the focus of life-cycle preservation needs to be enlarged from the strict raw data to the more encompassing arena of information lifecycle management. By understanding what constitutes information, and the complexities involved, we are better equipped to deliver longer lasting value about the original data and derived knowledge (information) from them. The NASA Earth Science Data Preservation Content Specification is an attempt to define the content necessary for long-term preservation. It requires new lifecycle infrastructure approach along with content repositories to accommodate artifacts other than just raw data. The NASA Goddard Earth Sciences Data and Information Services Center (GES DISC) setup an open-source Preservation System capable of long-term archive of digital content to augment its raw data holding. This repository is being used for such missions as HIRDLS, UARS, TOMS, OMI, among others. We will provide a status of this implementation; report on challenges, lessons learned, and detail our plans for future evolution to include other missions and services.

data management↗