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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 325 records · Page 18
All sourcesNASA NTRSDOE OSTI
NASA NTRS2019

Advancing the Standards for Unmanned Air System Communications, Navigation and Surveillance

Under NASA program NNA16BD84C, new architectures were identified and developed for supporting reliable and secure Communications, Navigation and Surveillance (CNS) needs for Unmanned Air Systems (UAS) operating in both controlled and uncontrolled airspace. An analysis of architectures for the two categories of airspace and an implementation technology readiness analysis were performed. These studies produced NASA reports that have been made available in the public domain and have been briefed in previous conferences. We now consider how the products of the study are influencing emerging directions in the aviation standards communities. The International Civil Aviation Organization (ICAO) Communications Panel (CP), Working Group I (WG-I) is currently developing a communications network architecture known as the Aeronautical Telecommunications Network with Internet Protocol Services (ATN/IPS). The target use case for this service is secure and reliable Air Traffic Management (ATM) for manned aircraft operating in controlled airspace. However, the work is more and more also considering the emerging class of airspace users known as Remotely Piloted Aircraft Systems (RPAS), which refers to certain UAS classes. In addition, two Special Committees (SCs) in the Radio Technical Commission for Aeronautics (RTCA) are developing Minimum Aviation System Performance Standards (MASPS) and Minimum Operational Performance Standards (MOPS) for UAS. RTCA SC-223 is investigating an Internet Protocol Suite (IPS) and AeroMACS aviation data link for interoperable (INTEROP) UAS communications. Meanwhile, RTCA SC-228 is working to develop Detect And Avoid (DAA) equipment and a Command and Control (C2) Data Link MOPS establishing LBand and C-Band solutions. These RTCA Special Committees along with ICAO CP WG/I are therefore overlapping in terms of the Communication, Navigation and Surveillance (CNS) alternatives they are seeking to provide for an integrated manned- and unmanned air traffic management service as well as remote pilot command and control. This paper presents UAS CNS architecture concepts developed under the NASA program that apply to all three of the aforementioned committees. It discusses the similarities and differences in the problem spaces under consideration in each committee, and considers the application of a common set of CNS alternatives that can be widely applied. As the works of these committees progress, it is clear that the overlap will need to be addressed to ensure a consistent and safe framework for worldwide aviation. In this study, we discuss similarities and differences in the various operational models and show how the CNS architectures developed under the NASA program apply.

Ponchak, Denise S.↗
NASA NTRSDate not supplied

NNREPAIR: Constraint-based Repair of Neural Network Classifiers

We present NNREPAIR, a constraint-based technique for repairing neural network classifiers by i) fixing the logic of the network at an intermediate layer, and ii) fixing the decision conditions of the network at the last layer. NNREPAIR first uses fault localization to find potentially faulty network parameters (specifically weights) and then performs repair using constraint solving to apply small modifications to remedy the defects. We present novel strategies to enable precise yet efficient repair such as inferring correctness specifications to act as oracles for intermediate layer repair, and generation of experts wrt each class. We demonstrate the technique in the context of three different scenarios: (1) Improving the over all accuracy of a model, (2) Fixing security vulnerabilities caused by poisoning of training data and (3) Improving the robustness of the network against adversarial attacks. Our evaluation on MNIST and CIFAR-10 models shows that NNREPAIR can improve the accuracy by 45.56% on poisoned data and 11.11% on adversarial data. NNREPAIR also provides improvement, albeit modest, in the overall accuracy of models, without requiring new data or re-training.

Repair↗
NASA NTRSDate not supplied

Runtime Monitoring with R2U2 for Aircraft Systems with Neural Networks

R2U2 (Realizable, Responsive, Unobtrusive Unit) is a hardware-supported tool and framework for real-time system monitoring and software health management of cyber-physical systems. During system operation, R2U2 continuously monitors properties about safety, performance, and security of the vehicle and its vital components and can perform diagnostic reasoning. Efficient observers for past-time and future-time Metric Temporal Logic, fast reasoners for Bayesian Networks, and model-based prognostics algorithms are key components of R2U2 and designed for minimal computational footprint. R2U2 has been implemented in software supporting ROS, NASA's cFS/cFE, and Simulink and as an FPGA configuration. The synergistic combination of monitors and observers in R2U2 makes it possible to design powerful models for system runtime monitoring, diagnostics, software health management, prognostics, and security monitoring. In this presentation, I will give a detailed overview of the R2U2 architecture and its features and will discuss the application of R2U2 for safety-monitoring of a neural-network based autonomous centerline tracking system (ACT) for autonomous aircraft.

Runtime Monitoring↗
NASA NTRS2003

Additional Security Considerations for Grid Management

The use of Grid computing environments is growing in popularity. A Grid computing environment is primarily a wide area network that encompasses multiple local area networks, where some of the local area networks are managed by different organizations. A Grid computing environment also includes common interfaces for distributed computing software so that the heterogeneous set of machines that make up the Grid can be used more easily. The other key feature of a Grid is that the distributed computing software includes appropriate security technology. The focus of most Grid software is on the security involved with application execution, file transfers, and other remote computing procedures. However, there are other important security issues related to the management of a Grid and the users who use that Grid. This note discusses these additional security issues and makes several suggestions as how they can be managed.

Eidson, Thomas M.↗
DOE OSTI2025

Scan2Sim: Software to Convert Network Scans to Emulations

Within operational technology (OT) systems design, the construction of testing environments for simulation is often a tedious, manual process that slows down safety and security evaluations. This document details the design and functionality of Scan2Sim, a program designed to construct high-fidelity topological schematics for OT systems without significant manual human input. Scan2Sim may take as input a detailed network scan of a system, and produces an instruction set to re-create the original scanned network within a virtualized simulation network. This construction is achieved via heuristic methods of machine template selection, which allows for a fast, performant approach to automated environment construction. The current tool is designed to produce topology schematics compatible with the Minimega, a tool designed by Sandia National Laboratories for repeatable experimentation management.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS1991

The Management and Security Expert (MASE)

The Management and Security Expert (MASE) is a distributed expert system that monitors the operating systems and applications of a network. It is capable of gleaning the information provided by the different operating systems in order to optimize hardware and software performance; recognize potential hardware and/or software failure, and either repair the problem before it becomes an emergency, or notify the systems manager of the problem; and monitor applications and known security holes for indications of an intruder or virus. MASE can eradicate much of the guess work of system management.

Miller, Mark D.↗
DOE OSTI2025

Enhancing Automotive Intrusion Detection Through Multi-Modal Fusion: A CAN FD-LiDAR Approach

As vehicles become smarter and more autonomous, they increasingly depend on advanced sensors and communication technologies to operate securely. However, such growing dependence on technology—whether it’s CAN (Controller Area Network) for internal communication or LiDAR (Light Detection and Ranging) for sensing the world around them—also expands the attack surface for the types of cyber attacks. Traditional intrusion detection systems (IDS) typically monitor these systems in isolation, limiting their ability to detect sophisticated, crosssystem attacks. To address this, we propose a multi-modal fusion approach that combines real-world CAN FD signals (from the HCRL dataset) with LiDAR features (from the nuScenes dataset) to enhance attack detection. Our method employs a twostage ensemble approach. Calibrated XGBoost and LightGBM models initially process CAN FD (Fuzzing Data) and LiDAR data independently, detecting timing anomalies and space abnormalities. They are subsequently logarithmically combined with a logistic regression meta-model along with 17 engineered features capturing cross-modal behavior, prediction conflicts, and nonlinear interactions. This approach achieves an AUC of 0.87 and an F1-score of 0.82, surpassing single-modality baselines and early fusion methods, at merely 2 ms inference latency. Compared with deep learning competitors, it is 3 times more efficient, providing a lightweight, interpretable, and real time solution to automotive cybersecurity.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS1993

Distributed Virtual System (DIVIRS) project

As outlined in the continuation proposal 92-ISI-50R (revised) on NASA cooperative agreement NCC 2-539, the investigators are developing software, including a system manager and a job manager, that will manage available resources and that will enable programmers to develop and execute parallel applications in terms of a virtual configuration of processors, hiding the mapping to physical nodes; developing communications routines that support the abstractions implemented; continuing the development of file and information systems based on the Virtual System Model; and incorporating appropriate security measures to allow the mechanisms developed to be used on an open network. The goal throughout the work is to provide a uniform model that can be applied to both parallel and distributed systems. The authors believe that multiprocessor systems should exist in the context of distributed systems, allowing them to be more easily shared by those that need them. The work provides the mechanisms through which nodes on multiprocessors are allocated to jobs running within the distributed system and the mechanisms through which files needed by those jobs can be located and accessed.

Schorr, Herbert↗
NASA NTRS2010

Situational Awareness from a Low-Cost Camera System

A method gathers scene information from a low-cost camera system. Existing surveillance systems using sufficient cameras for continuous coverage of a large field necessarily generate enormous amounts of raw data. Digitizing and channeling that data to a central computer and processing it in real time is difficult when using low-cost, commercially available components. A newly developed system is located on a combined power and data wire to form a string-of-lights camera system. Each camera is accessible through this network interface using standard TCP/IP networking protocols. The cameras more closely resemble cell-phone cameras than traditional security camera systems. Processing capabilities are built directly onto the camera backplane, which helps maintain a low cost. The low power requirements of each camera allow the creation of a single imaging system comprising over 100 cameras. Each camera has built-in processing capabilities to detect events and cooperatively share this information with neighboring cameras. The location of the event is reported to the host computer in Cartesian coordinates computed from data correlation across multiple cameras. In this way, events in the field of view can present low-bandwidth information to the host rather than high-bandwidth bitmap data constantly being generated by the cameras. This approach offers greater flexibility than conventional systems, without compromising performance through using many small, low-cost cameras with overlapping fields of view. This means significant increased viewing without ignoring surveillance areas, which can occur when pan, tilt, and zoom cameras look away. Additionally, due to the sharing of a single cable for power and data, the installation costs are lower. The technology is targeted toward 3D scene extraction and automatic target tracking for military and commercial applications. Security systems and environmental/ vehicular monitoring systems are also potential applications.

Freudinger, Lawrence C.↗
NASA NTRS2017

Considerations for an Integrated UAS CNS Architecture

The National Aeronautics and Space Administration (NASA) Glenn Research Center (GRC) is investigating revolutionary and advanced universal, reliable, always available, cyber secure and affordable Communication, Navigation, Surveillance (CNS) options for all altitudes of UAS operations. In Spring 2015, NASA issued a Call for Proposals under NASA Research Announcements (NRA) NNH15ZEA001N, Amendment 7 Subtopic 2.4. Boeing was selected to conduct a study with the objective to determine the most promising candidate technologies for Unmanned Air Systems (UAS) air-to-air and air-to-ground data exchange and analyze their suitability in a post-NextGen NAS environment. The overall objectives are to develop UAS CNS requirements and then develop architectures that satisfy the requirements for UAS in both controlled and uncontrolled air space. This contract is funded under NASAs Aeronautics Research Mission Directorates (ARMD) Aviation Operations and Safety Program (AOSP) Safe Autonomous Systems Operations (SASO) project and proposes technologies for the Unmanned Air Systems Traffic Management (UTM) service.There is a need for accommodating large-scale populations of Unmanned Air Systems (UAS) in the national air space. Scale obviously impacts capacity planning for Communication, Navitation, and Surveillance (CNS) technologies. For example, can wireless communications data links provide the necessary capacity for accommodating millions of small UASs (sUAS) nationwide? Does the communications network provide sufficient Internet Protocol (IP) address space to allow air traffic control to securely address both UAS teams as a whole as well as individual UAS within each team? Can navigation and surveillance approaches assure safe route planning and safe separation of vehicles even in crowded skies?Our objective is to identify revolutionary and advanced CNS alternatives supporting UASs operating at all altitudes and in all airspace while accurately navigating in the absence of navigational aids. These CNS alternatives must be reliable, redundant, always available, cyber-secure, and affordable for all types of vehicles including small UAS to large transport category aircraft. The approach will identify CNS technology candidates that can meet the needs of the range of UAS missions to specific air traffic management applications where they will be most beneficial and cost effective.

Templin, Fred L.↗
NASA NTRS2017

Considerations for an Integrated UAS CNS Architecture

The National Aeronautics and Space Administration (NASA) Glenn Research Center (GRC) is investigating revolutionary and advanced universal, reliable, always available, cyber secure and affordable Communication, Navigation, Surveillance (CNS) options for all altitudes of UAS operations. In Spring 2015, NASA issued a Call for Proposals under NASA Research Announcements (NRA) NNH15ZEA001N, Amendment 7 Subtopic 2.4. Boeing was selected to conduct a study with the objective to determine the most promising candidate technologies for Unmanned Air Systems (UAS) air-to-air and air-to-ground data exchange and analyze their suitability in a post-NextGen NAS environment. The overall objectives are to develop UAS CNS requirements and then develop architectures that satisfy the requirements for UAS in both controlled and uncontrolled air space. This contract is funded under NASAs Aeronautics Research Mission Directorates (ARMD) Aviation Operations and Safety Program (AOSP) Safe Autonomous Systems Operations (SASO) project and proposes technologies for the Unmanned Air Systems Traffic Management (UTM) service.There is a need for accommodating large-scale populations of Unmanned Air Systems (UAS) in the national air space. Scale obviously impacts capacity planning for Communication, Navigation, and Surveillance (CNS) technologies. For example, can wireless communications data links provide the necessary capacity for accommodating millions of small UASs (sUAS) nationwide? Does the communications network provide sufficient Internet Protocol (IP) address space to allow air traffic control to securely address both UAS teams as a whole as well as individual UAS within each team? Can navigation and surveillance approaches assure safe route planning and safe separation of vehicles even in crowded skies?Our objective is to identify revolutionary and advanced CNS alternatives supporting UASs operating at all altitudes and in all airspace while accurately navigating in the absence of navigational aids. These CNS alternatives must be reliable, redundant, always available, cyber-secure, and affordable for all types of vehicles including small UAS to large transport category aircraft. The approach will identify CNS technology candidates that can meet the needs of the range of UAS missions to specific air traffic management applications where they will be most beneficial and cost effective.

Templin, Fred L.↗
DOE OSTI2026

Automated, reliable, and efficient continental-scale replication of 7.3 petabytes of computational simulation data: A case study

We report on our experiences replicating 7.3 petabytes (PB) of Earth System Grid Federation (ESGF) computational simulation data from Lawrence Livermore National Laboratory (LLNL) in California to Argonne National Laboratory (ANL) in Illinois and Oak Ridge National Laboratory (ORNL) in Tennessee—a task motivated by a need for increased reliability, capacity, and performance. This task presented significant challenges: the need to move 29 million files twice under time pressure from aging storage hardware; a source file system bottleneck limiting throughput to 1.5 GB/s; frequent site maintenance windows; and the need for complete reliability at scale. We addressed these challenges using a simple replication tool that invoked Globus to transfer large bundles of files while tracking progress in a database, dynamically rerouting transfers to work around maintenance periods and file system limitations. Under the covers, Globus organized transfers to make efficient use of the high-speed Energy Sciences network (ESnet) and the data transfer nodes deployed at participating sites, and also addressed security, integrity checking, and recovery from a variety of transient failures. This success demonstrates the considerable benefits that can accrue from the adoption of performant data replication infrastructure. The replication tool is available at https://github.com/esgf2-us/data-replication-tools.

Globus↗
DOE OSTI2026

Joint scheduling of energy, fast and primary frequency response reserves in integrated transmission–distribution networks

Inverter-based distributed energy resources (DERs) connected to distribution networks (DNs) can provide fast frequency support, but their reserve deliverability depends on feeder constraints and differs from synchronous primary frequency response (PFR). Existing transmission–distribution coordination studies usually treat reserve generically or neglect feeder-level feasibility, while frequency-security scheduling studies rarely represent distribution feeders explicitly. This paper develops a bi-level day-ahead scheduling framework for integrated transmission–distribution networks that jointly clears energy, transmission-side PFR, and distribution-side fast frequency response (FFR) under exogenous hourly inertia and largest-loss inputs from an external unit commitment (UC) schedule. The transmission problem is modeled with DC-optimal power flow (OPF) and closed-form second-order cone (SOC) frequency-security constraints, whereas each DN is represented by a reserve-aware branch-flow AC-OPF so that scheduled fast reserves remain deliverable during activation. The bi-level problem is reformulated through Karush–Kuhn–Tucker (KKT) conditions into a mixed-integer SOC program, and a penalty term is used to tighten the distribution-network relaxation. In the reduced test system, lower exogenous inertia increased the required primary response from 179.64 MW to 191.08 MW, distribution-side fast response reduced total frequency-response procurement by up to 4.9%, and neglecting distribution constraints overstated the combined distribution-side energy and reserve award by up to 18%. In the expanded study, the largest case was solved in 2.02 s with a 0.00% optimality gap. Time-domain simulations kept the frequency nadir above 59.0 Hz in all tested hours. These results demonstrate the value of fast-response modeling and distribution-feasible reserve delivery in coordinated market clearing.

Noh, Seung-Gil↗
DOE OSTI2026

Mapping Critical Vulnerabilities in Natural Gas Pipeline Systems through Network Centrality and GIS Analytics

Natural gas plays a central role in the US energy landscape, providing 43% of electricity generation in 2023. Its exclusive recovery ability on pipelines for transmission underscores the importance of understanding the disruption recovery ability of this infrastructure. This study employs a network-based analytical framework integrating geographic information systems (GIS) with multiple centrality measures—betweenness, closeness, degree, and eigenvector—to pinpoint key segments and evaluate the structural robustness of the national pipeline network. Pipelines are grouped by System ID and Operator ID to capture variations across organizational and physical structures. The analysis reveals uneven patterns of network influence, where certain pipelines function as critical connectors or dominant hubs. Spatial mapping highlights geographic dependencies and potential chokepoints, offering a clear view of where targeted risk prevention measures would be most effective. The findings provide practical guidance for prioritizing maintenance, enhancing system robustness, and mitigating risks to ensure a stable and secure energy supply. Future research will expand the framework to incorporate dynamic operational data and real-time network behavior.

Peterson, Steven [ORNL] (ORCID:0000000287672998)↗
NASA NTRS2019

Software and System Health Management with R2U2

R2U2 (Realizable, Responsive, Unobtrusive Unit) is a hardware-supported tool and framework for the real-time system and software health management of cyber-physical systems. R2U2 continuously monitors properties about safety, performance, and security of the vehicle and can perform diagnostic reasoning. Efficient observers for past-time and future-time Metric Temporal Logic, reasoners for Bayesian Networks, and model-based prognostics algorithms are major components of R2U2. Their combination makes it possible to design powerful models for system runtime monitoring, diagnostics, software health management, prognostics, and security monitoring. The R2U2 monitoring engine is designed for minimal runtime overhead and is available as Simulink block or as a software component for integration into the flight software stack, and enables R2U2 to monitor complex cyber-physical systems without any instrumentation of the flight software. In this presentation, we give an overview of R2U2 architecture and reasoning algorithms, present its features, and give a life demo of the tool.

Schumann, Johann↗
DOE OSTI2026

Firmware Tampering Detection in Heavy-Duty Vehicles through J1939 CAN Analysis

Modern heavy-duty vehicles rely on complex networks of Electronic Control Units (ECUs) that communicate using the J1939 protocol. While this system makes it easier to update and configure vehicle components, it also opens the door to serious cybersecurity risks if not properly secured. This work investigates the potential for firmware tampering through the J1939 communication protocol, which enables ECU configuration and reprogramming over the Controller Area Network (CAN) bus. By monitoring CAN traffic during legitimate maintenance operations and reverse-engineering OEM diagnostic software, we identified common and proprietary J1939 message identifiers, authentication patterns, and vulnerabilities within Unified Diagnostic Services (UDS). These findings demonstrate that inadequate authentication mechanisms can allow malicious actors to alter ECU firmware or disable safety functions, posing severe operational and safety risks. Our analysis contributes to the development of vehicle intrusion detection systems capable of recognizing abnormal reprogramming activity and future firmware fingerprinting methods to verify software integrity across ECUs. This work highlights the importance of standardizing secure firmware authentication across manufacturers to strengthen cyber resilience in heavy-duty vehicle systems.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2024

Artificial Intelligence/Machine Learning Technology in Power System Applications

The primary purpose of this report is to provide an overview of the advancement in artificial intelligence and machine learning (AI/ML) technologies and their applications in power systems. It offers a foundation for understanding the transformative role of AI/ML in power systems and aims to stimulate further research and development in this area. This report begins with a historical perspective of AI/ML technologies, then explores their advancement to today’s prominence. The document highlights key contributors to the success of AI/ML technologies, including increased computational power, greater data availability, innovative algorithms, and advanced tools. It further introduces various AI/ML techniques, including supervised, unsupervised and reinforcement learning, graph neural networks, and generative AI. It also emphasizes the critical importance of ensuring the safety, security, and trustworthiness of these AI/ML techniques within this sector. The report reviews the recent representative advancements in various power system applications enhanced by AI/ML techniques, underscoring key developments and their transformative impact as evidenced by numerous studies. It also explores both the opportunities and challenges associated with the application of AI/ML technologies to improve power system applications. While the report extensively covers AI/ML applications in power systems, focusing primarily on the technical and operational aspects, it may not thoroughly explore the sociopolitical, economic, and broader regulatory implications of AI/ML integration in power systems. AI/ML techniques hold significant potential for enhancing power system applications; however, they are not omnipotent. It is crucial to acknowledge their limitations and understand that they may not be able to address all challenges in the power system domain. Various factors must be considered that influence the implementation, adoption, and effectiveness of AI/ML solutions, including but not limited to safety, security, transparency, and trustworthiness. Additionally, the incorporation of advanced human–machine interfaces is essential, as it enables humans to validate the effectiveness of AI/ML solutions while remaining actively engaged, fostering trust in AI/ML deployment. Finally, the report summarizes AI/ML research activities supported by the Department of Energy (DOE) Office of Electricity (OE) through the Advanced Grid Modeling (AGM) program. The work aligns with the interests and mission of DOE-OE AGM, with the report serving as a resource for identifying existing progress and for pinpointing future applications within AI/ML that need further exploration and support.

24 POWER TRANSMISSION AND DISTRIBUTION↗
NASA NTRS2002

Continuously Connected With Mobile IP

Cisco Systems developed Cisco Mobile Networks, making IP devices mobile. With this innovation, a Cisco router and its connected IP devices can roam across network boundaries and connection types. Because a mobile user is able to keep the same IP address while roaming, a live IP connection can be maintained without interruption. Glenn Research Center jointly tested the technology with Cisco, and is working to use it on low-earth-orbiting research craft. With Cisco's Mobile Networks functionality now available in Cisco IOS Software release 12.2(4)T, the commercial advantages and benefits are numerous. The technology can be applied to public safety, military/homeland security, emergency management services, railroad and shipping systems, and the automotive industry. It will allow ambulances, police, firemen, and the U.S. Coast Guard to stay connected to their networks while on the move. In the wireless battlefield, the technology will provide rapid infrastructure deployment for U.S. national defense. Airline, train, and cruise passengers utilizing Cisco Mobile Networks can fly all around the world with a continuous Internet connection. Cisco IOS(R) Software is a registered trademark of Cisco Systems.

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