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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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All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Unmanned Aerial Vehicle Synthetic Aperture RADAR for Surface Change Monitoring

Space-based and airplane-based synthetic aperture RADAR (SAR) can monitor ground height using interferometric SAR (InSAR) collections. However, fielding the airplane-based SAR is expensive and coordinating the frequency and timing of ground experiments with space-based SAR is challenging. This research explored the possibility of using a small, mobile unmanned aerial vehicle- base (UAV) SAR to see if it could provide a quick and inexpensive InSAR option for the Source Physics Experiment (SPE) Phase III project. Firstly, a local feasibility collection using a UAV-based SAR showed that InSAR products and height measurements were possible, but that in-scene fiducials were needed to assist in digital elevation model (DEM) construction. Secondly, an InSAR collection was planned and executed over the SPE Phase III site using the same platform configuration. We found that the image formation by the SAR manufacturer creates discontinuities, and that noise impacted the generation and accuracy of height maps. These processing artifacts need to be overcome to generate an accurate height map.

47 OTHER INSTRUMENTATION↗
DOE OSTI2025

Advanced Transmission Technologies (ATTs) Supplier Cohort Workshops Cohort Summary [Slides]

This Summary slide deck summarizes the key outcomes of the Advanced Transmission Technologies (ATTs) supplier cohort, part of Idaho National Laboratory’s (INL) Technical Assistance for Digital Assurance (TADA) program. The program aimed to strengthen grid resilience through cybersecurity controls, supply-chain security, and Cyber-Informed Engineering (CIE) for advanced transmission technologies. The cohort brought together vendors representing the full range of Grid-Enhancing Technologies (GETs), including providers of Dynamic Line Ratings (DLR), Advanced Power Flow Control (APFC), Transmission Topology Optimization (TTO), and High-Performance Conductors (HPCs). Discussions focused on institutional, integration, and operational barriers limiting GET adoption; cybersecurity risks at EMS/SCADA, cloud, and network integration points; and supply-chain transparency issues such as semiconductor dependence and SBOM/HBOM expectations. Participants also addressed operator trust, human-in-the-loop requirements, and challenges with utility adoption, while exploring how CIE can support secure deployment of GETs. This deck represents a consolidated summary of challenges and risks identified by vendors, cross-cutting themes and technology-specific insights from three cohort workshops, and actionable mitigations to guide utilities, vendors, and the Department of Energy in advancing secure, trusted deployment of GETs.

24 - POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2026

Data Center Cybersecurity, Supply Chain Risk Management, and Emerging Regulation Cohort Summary: Takeaways and Action Plans

This report summarizes the outcomes of the Data Center Cohort under the Department of Energy’s Technical Assistance for Digital Assurance (TADA) initiative, aimed at enhancing grid resilience through cybersecurity, supply chain risk management (SCRM), and Cyber-Informed Engineering (CIE). The cohort engaged 17 organizations across utilities, data center operators, vendors, and technology providers in three sessions combining presentations, discussions, and exercises. Key topics included AI-driven load behavior, cybersecurity vulnerabilities in UPS/BESS and cooling systems, governance gaps at utility–data center boundaries, and supply chain integrity. Five cross-cutting themes emerged: interconnection architecture vulnerabilities, fragmented governance, AI-driven stability risks, lack of regulatory frameworks, and long-term supply chain concerns. Actionable recommendations were developed, including implementing DMZ segmentation, formalizing vendor access agreements, designing AI workload limits, and advancing standards through NERC and state-level programs. These strategies aim to strengthen resilience, clarify responsibilities, and ensure secure integration of data centers into the grid.

24 - POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2020

Explainable Artificial Intelligence Recommendation System by Leveraging the Semantics of Adverse Childhood Experiences: Proof-of-Concept Prototype Development

The study of adverse childhood experiences and their consequences has emerged over the past 20 years. Although the conclusions from these studies are available, the same is not true of the data. Accordingly, it is a complex problem to build a training set and develop machine-learning models from these studies. Classic machine learning and artificial intelligence techniques cannot provide a full scientific understanding of the inner workings of the underlying models. This raises credibility issues due to the lack of transparency and generalizability. Explainable artificial intelligence is an emerging approach for promoting credibility, accountability, and trust in mission-critical areas such as medicine by combining machine-learning approaches with explanatory techniques that explicitly show what the decision criteria are and why (or how) they have been established. Hence, thinking about how machine learning could benefit from knowledge graphs that combine “common sense” knowledge as well as semantic reasoning and causality models is a potential solution to this problem. In this study, we aimed to leverage explainable artificial intelligence, and propose a proof-of-concept prototype for a knowledge-driven evidence-based recommendation system to improve mental health surveillance. We used concepts from an ontology that we have developed to build and train a question-answering agent using the Google DialogFlow engine. In addition to the question-answering agent, the initial prototype includes knowledge graph generation and recommendation components that leverage third-party graph technology. To showcase the framework functionalities, we here present a prototype design and demonstrate the main features through four use case scenarios motivated by an initiative currently implemented at a children’s hospital in Memphis, Tennessee. Ongoing development of the prototype requires implementing an optimization algorithm of the recommendations, incorporating a privacy layer through a personal health library, and conducting a clinical trial to assess both usability and usefulness of the implementation. This semantic-driven explainable artificial intelligence prototype can enhance health care practitioners’ ability to provide explanations for the decisions they make.

adverse childhood experiences↗
NASA NTRS1995

Easing The Calculation Of Bolt-Circle Coordinates

Bolt Circle Calculation (BOLT-CALC) computer program used to reduce significant time consumed in manually computing trigonometry of rectangular Cartesian coordinates of holes in bolt circle as shown on blueprint or drawing. Eliminates risk of computational errors, particularly in cases involving many holes or in cases in which coordinates expressed to many significant digits. Program assists in many practical situations arising in machine shops. Written in BASIC. Also successfully compiled and implemented by use of Microsoft's QuickBasic v4.0.

Burley, Richard K.↗
DOE OSTI2023

Traffic Signal Optimization by Integrating Reinforcement Learning and Digital Twins

Machine learning (ML) methods, especially reinforcement learning (RL), have been widely considered for traffic signal optimization in intelligent transportation systems. Most of these ML methods are centralized, lacking in scalability and adaptability in large traffic networks. Further, it is challenging to train such ML models due to the lack of training platforms and/or the cost of deploying and training in a real traffic networks. This paper presents an approach for the integration of decentralized graph-based multi-agent reinforcement learning (DGMARL) with a Digital Twin (DT) to optimize traffic signals for the reduction of traffic congestion and network-wide fuel consumption related to stopping. Specifically, the DGMARL agents learn traffic state patterns and make decisions regarding traffic signal control with assistance from a Digital Twin module, which simulates and replicates the traffic behaviors of a real traffic network. The proposed approach was evaluated using PTV-Vissim [1], a microscopic traffic simulation platform. PTV-Vissim is also the simulation engine of the DT, enabling emulation and optimization of the traffic signals on the MLK Smart Corridor in Chattanooga, Tennessee. Compared to an actuated signal control baseline approach, experiment results show that Eco_PI, a developed performance measure capturing the impact of stops on fuel consumption, was reduced by 44.27% in a 24-hour and an average of 29.88% in a PM-peak-hour scenario.

Multi-Agent Reinforcement Learning, Digital Twin, ↗
DOE OSTI2025

Digitize O&M Procedure Guidelines for Illu Workflows (CRADA Final Report)

NREL will provide technical assistance to help digitize best practice operations and maintenance (“O&M”) procedure guidelines utilizing the Illu platform for specific distributed solar-storage system configurations. NREL shall test Illu workflows in a real inspection environment for clarity of instruction and ease of documentation.

14 SOLAR ENERGY↗
NASA NTRS1993

Digital mammography, cancer screening: Factors important for image compression

The use of digital mammography for breast cancer screening poses several novel problems such as development of digital sensors, computer assisted diagnosis (CAD) methods for image noise suppression, enhancement, and pattern recognition, compression algorithms for image storage, transmission, and remote diagnosis. X-ray digital mammography using novel direct digital detection schemes or film digitizers results in large data sets and, therefore, image compression methods will play a significant role in the image processing and analysis by CAD techniques. In view of the extensive compression required, the relative merit of 'virtually lossless' versus lossy methods should be determined. A brief overview is presented here of the developments of digital sensors, CAD, and compression methods currently proposed and tested for mammography. The objective of the NCI/NASA Working Group on Digital Mammography is to stimulate the interest of the image processing and compression scientific community for this medical application and identify possible dual use technologies within the NASA centers.

Clarke, Laurence P.↗
NASA NTRS1981

Wetland mapping from digitized aerial photography

Computer assisted interpretation of small scale aerial imagery was found to be a cost effective and accurate method of mapping complex vegetation patterns if high resolution information is desired. This type of technique is suited for problems such as monitoring changes in species composition due to environmental factors and is a feasible method of monitoring and mapping large areas of wetlands. The technique has the added advantage of being in a computer compatible form which can be transformed into any georeference system of interest.

Scarpace, F. L.↗
NASA NTRS1990

Data compression in digitized lines

The problem of data compression is very important in digital photogrammetry, computer assisted cartography, and GIS/LIS. In addition, it is also applicable in many other fields such as computer vision, image processing, pattern recognition, and artificial intelligence. Consequently, there are many algorithms available to solve this problem but none of them are considered to be satisfactory. In this paper, a new method of finding critical points in a digitized curve is explained. This technique, based on the normalized symmetric scattered matrix, is good for both critical points detection and data compression. In addition, the critical points detected by this algorithm are compared with those by zero-crossings.

Thapa, Khagendra↗
DOE OSTI2024

Machine learning at the edge to improve in-field safeguards inspections

Artificial intelligence (AI) and machine learning (ML) are near-ubiquitous in day-to-day life; from cars with automated driver-assistance, recommender systems, generative content platforms, and large language chatbots. Implementing AI as a tool for international safeguards could significantly decrease the burden on safeguards inspectors and nuclear facility operators. The use of AI would allow inspectors to complete their in-field activities quicker, while identifying patterns and anomalies and freeing inspectors to focus on the uniquely human component of inspections. Sandia National Laboratories has spent the past two and a half years developing on-device machine learning to develop both a digital and robotic assistant. This combined platform, which we term inspecta, has numerous on-device machine learning capabilities that have been demonstrated at the laboratory scale. Here this work describes early successes implementing AI/ML capabilities to reduce the burden of tedious inspector tasks such as seal examination, information recall, note taking, and more.

98 NUCLEAR DISARMAMENT, SAFEGUARDS, AND PHYSICAL P↗
DOE OSTI2026

The Next Stage for Smart Cities: Metrics to Spur an Evolution from Technology for Efficiency to Technology for People's Needs

The Smart Cities movement is about a decade old, energized by the USDOT Smart Cities grant that sparked cities to dream big. Now, 10 years later, and multitudes of conferences and papers later, the biggest question regarding Smart Cities is whether any progress has been made? Smart Cities forums are typically led by the technology sector, either telecommunications, internet access, or most recently artificial intelligence (AI) concerns. Each of these areas have grown and found new applications in the context of Smart Cities, however, these technology applications are just tools toward ends. Do digital communications and assistance enable or foster improved access to real, tactile, non-virtual goods and services such as transportation, food, housing, trash removal, health care, education, social interaction? These and other topics are not 'big tech', but are each critical to quality of life outcomes and are day-to-day necessities, comprising many of the touch points through which citizens interact with municipal agencies. Regarding Smart Cities, relationships have not been fully refined as to how best to merge the emerging generation of technology and related trends with more mundane but essential requirements of tactile services. How should municipal managers and operators bring the range of new technology tools (e.g. on-demand transit, automated vehicles, digital connectivity) to bear to improve functions serving real- physical- non-virtual needs? How do we make cities more efficient in providing access to services and opportunities for improved quality of life? This paper examines the current Smart City movement, emerging trends, and the need to put metrics with respect to the delivery, access, and provision of real-life needs. Using a few case studies of past, present, and projected future scenarios, this paper attempts to bring Smart Cities into the real cities domain, perhaps pushing the moniker away from 'Smart' to 'Efficient' - something that can be measured, counted, and assessed. Through the consideration of the next stage of the evolution of cities from 'Smart' to 'Efficient', the combined efforts and experience of multiple researchers at the NREL and their network of collaborators highlights the need for updates metrics that can be used to start guiding efforts toward productive initiatives to improve quality of life.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
NASA NTRS1975

Computer measurement and representation of the heart in two and three dimensions

Methods for accurate measurement and meaningful display of cardiac dimensions as obtained from fluoroscopy were surveyed. Recorded images were scaled back to actual dimensions and cardiac motion was displayed to assess functional changes. This was accomplished by use of a low-cost but flexible ensemble of computer and video equipment which assisted in (1) digitizing image outlines or points, (2) filing digitized images for later recall in any sequence, (3) correcting recorded images for distortion, (4) modeling heart geometry, (5) analyzing dimensional changes, and (6) displaying both heart geometry and results of analysis in a useful form.

Rasmussen, D.↗
NASA NTRS1981

Application of SEASAT-1 Synthetic Aperture Radar (SAR) data to enhance and detect geological lineaments and to assist LANDSAT landcover classification mapping

Digital SEASAT-1 synthetic aperture radar (SAR) data were used to enhance linear features to extract geologically significant lineaments in the Appalachian region. Comparison of Lineaments thus mapped with an existing lineament map based on LANDSAT MSS images shows that appropriately processed SEASAT-1 SAR data can significantly improve the detection of lineaments. Merge MSS and SAR data sets were more useful fo lineament detection and landcover classification than LANDSAT or SEASAT data alone. About 20 percent of the lineaments plotted from the SEASAT SAR image did not appear on the LANDSAT image. About 6 percent of minor lineaments or parts of lineaments present in the LANDSAT map were missing from the SEASAT map. Improvement in the landcover classification (acreage and spatial estimation accuracy) was attained by using MSS-SAR merged data. The aerial estimation of residential/built-up and forest categories was improved. Accuracy in estimating the agricultural and water categories was slightly reduced.

Sekhon, R.↗
NASA NTRS1992

Flight deck benefits of integrated data link communication

A fixed-base, piloted simulation study was conducted to determine the operational benefits that result when air traffic control (ATC) instructions are transmitted to the deck of a transport aircraft over a digital data link. The ATC instructions include altitude, airspeed, heading, radio frequency, and route assignment data. The interface between the flight deck and the data link was integrated with other subsystems of the airplane to facilitate data management. Data from the ATC instructions were distributed to the flight guidance and control system, the navigation system, and an automatically tuned communication radio. The co-pilot initiated the automation-assisted data distribution process. Digital communications and automated data distribution were compared with conventional voice radio communication and manual input of data into other subsystems of the simulated aircraft. Less time was required in the combined communication and data management process when data link ATC communication was integrated with the other subsystems. The test subjects, commercial airline pilots, provided favorable evaluations of both the digital communication and data management processes.

Waller, Marvin C.↗
DOE OSTI2023

Capturing high‐resolution digital images for use in forensic document examination

Abstract In the past, pattern disciplines within forensic science have periodically faced criticism due to their subjective and qualitative nature and the perceived absence of research evaluating and supporting the foundations of their practices. Recently, however, forensic scientists and researchers in the field of pattern evidence analysis have developed and published approaches that are more quantitative, objective, and data driven. This effort includes automation, algorithms, and measurement sciences, with the end goal of enabling conclusions to be informed by quantitative models. Before employing these tools, forensic evidence must be digitized in a way that adequately balances high‐quality detail and content capture with minimal background noise imparted by the selected technique. While the current work describes the process of optimizing a method to digitize physical documentary evidence for use in semi‐automated trash mark examinations, it could be applied to assist other disciplines where the digitization of physical items of evidence is prevalent. For trash mark examinations specifically, it was found that high‐resolution photography provided optimal digital versions of evidentiary items when compared to high‐resolution scanning.

Riley, Paige↗
DOE OSTI2022

Physics-based modeling and information-theoretic sensor and settings selection for tool wear detection in precision machining

Precision machining of metals is an energy intensive process with applications and impacts across the manufacturing industry. The energy efficiency, product yield, and maintenance of the precision machine require a digital twin that can assist with prognostics and health management. In this report a physics-based model is developed and validated against face milling data, and then used for the timely and precise inference of machining faults that cannot be measured directly. Computer numerical control (CNC) measurements of power and force are used through this physics-based machining model to predict deviations of the outputs of power consumption and cutting forces during normal operation. A model-based fault detection and isolation methodology is applied to determine the optimal (traditional and available) sensor suite and the test settings (admissible input values) that improve the inference of tool wear in face milling. The optimal sensor suite and input test settings are obtained by solving a mixed integer non-linear program that optimizes information-theoretic metrics relevant to the detection and isolation of tool wear from steady-state or transient machining measurements. Dynamic time warping and k—NN classification are then used to validate the robustness of the optimal design for fault detection test design, including the optimal sensor suite.

42 ENGINEERING↗