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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 235 records · Page 13
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Ir(hkl) Surface Electrochemistry in a Nonadsorbing Acidic Medium

The fundamental properties of electrochemical materials depend on the multiple and often complex interactions between electrode surface sites and electrolyte species at the electrochemical interface. Despite Iridium use in electrolyzer systems, much of its surface electrochemistry remains underexplored. This study investigates the surface electrochemistry of Ir(111), Ir(100), and Ir(110) surfaces in acidic media. Using cyclic voltammetry and CO charge displacement experiments, we establish the charge states and adsorbate coverages as a function of the electrode potential, revealing the presence of hydrogen and hydroxyl co-adsorption at low potentials on (111), and almost no coverage of H ad on (110) facet. In situ Shell Isolated Nanoparticle Enhanced Raman Spectroscopy experiments provide direct evidence of the formation of key adsorbate species, such as hydrogen, hydroxyl, and oxygen, but most importantly, their interactions with interfacial water, confirmed by Density Functional Theory calculations. Our findings highlight the role of co-adsorption and interspecies interactions, with microkinetic adsorption voltammetry simulations corroborating the influence of lateral interactions on adsorption dynamics, particularly for Ir(100) where the OHad formation occurs as a sharp adsorption/desorption current. Our results underscores the importance of interfacial water and hydrogen bonding networks in shaping the electrochemical behavior on Ir surfaces, refining our baseline understanding of the Ir surface electrochemistry necessary for the development of advanced Ir-based electrochemical materials.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

A Resilient Integrated Resource Planning Framework for Transmission Systems: Analysis and Optimization

This article presents a resilient Integrated Resource Planning (IRP) framework designed for transmission systems, with a specific focus on analyzing and optimizing responses to High-Impact Low-Probability (HILP) events. The framework aims to improve the resilience of transmission networks in the face of extreme events by prioritizing the assessment of events with significant consequences. Unlike traditional reliability-based planning methods that average the impact of various outage durations, this work adopts a metric based on the proximity of outage lines to generators to select HILP events. The system’s baseline resilience is evaluated by calculating load curtailment in different parts of the network resulting from HILP outage events. The transmission network is represented as an undirected graph. Graph-theoretic techniques are used to identify islands with or without generators, potentially forming segmented grids or microgrids. This article introduces Expected Load Curtailment (ELC) as a metric to quantify the system’s resilience. The framework allows for the re-evaluation of system resilience by integrating additional generating resources to achieve desired resilience levels. Optimization is performed in the re-evaluation stage to determine the optimal placement of distributed energy resources (DERs) for enhancing resilience, i.e., minimizing ELC. Case studies on the IEEE 24-bus system illustrate the effectiveness of the proposed framework. In the broader context, this resilient IRP framework aligns with energy sustainability goals by promoting robust and resilient transmission networks, as the optimal placement of DERs for resilience enhancement not only strengthens the system’s ability to withstand and recover from disruptions but also contributes to efficient resource utilization, advancing the overarching goal of energy sustainability.

24 POWER TRANSMISSION AND DISTRIBUTION↗
NASA NTRS1980

Block 1, phase 1 very long baseline interferometry implementation

The Block 1, phase 1 VBLI System, implemented in the Deep Space Network and currently undergoing system testing, is discussed. The system can be characterized as the modification of existing equipment and the addition of new software in the 64 m subnet and the addition of new hardware and software in the Network Operations and Control Center. It is to be operational to support Voyager project navigation requirements and is to provide, on a weekly basis, the information related to station clock synchronization, UT1, and polar motion.

Wilcher, J. H.↗
NASA NTRS2001

Baseline Maritime Aerosol: Methodology to Derive the Optical Thickness and Scattering Properties

Satellite Measurements of the global distribution of aerosol and their effect on climate should be viewed in respect to a baseline aerosol. In this concept, concentration of fine mode aerosol particles is elevated above the baseline by man-made activities (smoke or urban pollution), while coarse mode by natural processes (e.g. dust or sea-spray). Using 1-3 years of measurements in 10 stations of the Aerosol Robotic network (ACRONET we develop a methodology and derive the optical thickness and properties of this baseline aerosol for the Pacific and Atlantic Oceans. Defined as the median for periods of stable optical thickness (standard deviation < 0.02) during 2-6 days, the median baseline aerosol optical thickness over the Pacific Ocean is 0.052 at 500 am with Angstrom exponent of 0.77, and 0.071 and 1.1 respectively, over the Atlantic Ocean.

Kaufman, Yoram J.↗
NASA NTRS2022

Research Campaign: Lunar Gravitational-wave Antenna

The Lunar Gravitational-wave Antenna (LGWA) is a mission conceived as a network of stations to measure the vibrations of the Moon caused by gravitational waves (GWs). The long-term vision is to accomplish ground-breaking, paradigm shifting science in the coming decades on the Moon. LGWA will lead to the first observation of GW signals in the decihertz band greatly expanding our understanding of the universe and laying out a path to eventually probe the moment of its creation. LGWA stations will also be unique contributions to a lunar geophysical network shedding light on the Moon’s formation history. LGWA is a project of inclusion and international collaboration, where space-faring and space-aspiring nations can contribute to the LGWA network with their own stations whose cost depends on the targeted station lifetime and deployment location. With respect to the baseline concept proposed here, different sensor technologies can be implemented over time with the goal to continually increase the performance of the LGWA network. The broader context of lunar GW detection and its long-term vision are outlined in an accompanying topical white paper.

Lorella Angelini↗
DOE OSTI2021

Modulation and Modeling of Three-Dimensional Nanowire Assemblies Targeting Gas Sensors with High Response and Reliability

Despite improved sensitivity, simple downsizing of gas-sensing components to randomly arranged nanostructures often faces challenges associated with unpredictable electrical conduction pathways. In the present study, controlled fabrication of three-dimensional (3D) metal oxide nanowire networks is demonstrated that can greatly improve both signal stability and sensor response compared to random nanowire arrays. For example, the highest ever reported H 2 S gas response value, and a 5 times lower relative standard deviation of baseline resistance than that of random nanowires assemblies, are achieved with the ordered 3D nanowire network. Systematic engineering of 3D geometries and their modeling, utilizing equivalent circuit components, provide additional insights into the electrical conduction and gas-sensing response of 3D assemblies, revealing the critical importance of wire-to-wire junction points and their arrangement. Here these findings suggest new design rules for both enhanced performance and reliability of chemical sensors, which may also be extended to other devices based on nanoscale building blocks.

36 MATERIALS SCIENCE↗
NASA NTRSDate not supplied

Collision Avoidance Approach Using Deep Reinforcement Learning

A method to enable autonomous robots moving in a 2D space collision free motivates the purposed approach for collision avoidance for autonomous UAM vehicles. Challenges of autonomous collision free navigation for both problems are similar. Agents in each environment do not know the intent, or goal, of the other. Finding the time efficient paths require some level of anticipation with neighboring agents which is computationally expensive. In the original work, these obstacles were overcome with a novel application of deep reinforcement learning which offloads the online computation to an offline learning algorithm. A value network that encodes the estimated time to the goal given the agent’s state and the observable portion of the other agent’s state is trained on a baseline policy and further refined with reinforcement learning to promote time efficient collision free navigation. Online, the value network efficiently informs the agent’s decision making in the face of uncertainty of the other agent’s next move. In this paper, challenges extending this methodology to the 3D environment of autonomous UAM vehicles with kinematic constraints are discussed and initial results shown.

Collision Avoidance↗
NASA NTRSDate not supplied

Collision Avoidance Approach Using Deep Reinforcement Learning

A method to enable autonomous robots moving in a 2D space collision free motivates the purposed approach for collision avoidance for autonomous UAM vehicles. Challenges of autonomous collision free navigation for both problems are similar. Agents in each environment do not know the intent, or goal, of the other. Finding the time efficient paths require some level of anticipation with neighboring agents which is computationally expensive. In the original work, these obstacles were overcome with a novel application of deep reinforcement learning which offloads the online computation to an offline learning algorithm. A value network that encodes the estimated time to the goal given the agent’s state and the observable portion of the other agent’s state is trained on a baseline policy and further refined with reinforcement learning to promote time efficient collision free navigation. Online, the value network efficiently informs the agent’s decision making in the face of uncertainty of the other agent’s next move. In this paper, challenges extending this methodology to the 3D environment of autonomous UAM vehicles with kinematic constraints are discussed and initial results shown.

Collision Avoidance↗
DOE OSTI2026

Community detection robustness of graph neural networks

Graph neural networks (GNNs) are increasingly widely used for community detection in attributed networks. They combine structural topology with node attributes through message passing and pooling. However, their robustness or lack thereof with respect to different perturbations and targeted attacks in conjunction with community detection tasks is not well understood. To shed light on latent mechanisms behind GNN sensitivity on community detection tasks, we conduct a systematic computational evaluation of six widely adopted GNN architectures graph convolutional network, graph attention network, graph sample and aggregate (GraphSAGE), differentiable pooling (DiffPool), minimum cut pooling (MinCUT), and deep modularity networks (DMoN). The analysis covers three perturbation categories: node attribute manipulations, edge topology distortions, and adversarial attacks. We use element-centric similarity as the evaluation metric on synthetic benchmarks and real-world citation networks. Our findings indicate that supervised GNNs tend to achieve higher baseline accuracy, while unsupervised methods, particularly DMoN, maintain stronger resilience under targeted and adversarial perturbations. Furthermore, robustness appears to be strongly influenced by community strength, with well-defined communities reducing performance loss. Across all models, node attribute perturbations associated with targeted edge deletions and shifts in attribute distributions tend to cause the largest degradation in community recovery. These findings highlight important trade-offs between accuracy and robustness in GNN-based community detection and offer insights into selecting architectures resilient to noise and adversarial attacks.

Goel, Jaidev [Virginia Polytechnic Inst. and State↗
DOE OSTI2024

DECIDER

This software offers methods and functions for building failure detectors for deep image classification models with the aid of vision-language models and LLMs. It includes functionalities for training baseline image classifiers, debiasing classifiers using vision-language models and LLMs, evaluating failure between models along with baselines. Developed using PyTorch, this software is compatible with standard neural network architectures used for imaging data. Additionally, it provides capabilities to compute evaluation metrics for assessing the performance and quality of the detectors.

Narayanaswamy, Vivek Sivaraman↗
DOE OSTI2025

Best Practices for Grid Communications

As the grid evolves, the communications architecture will need to evolve with it. That architecture affords a structured means by which the evolving complexities of the modern electric grid can be managed. This document provides best practices that can be implemented in the grid of today and evolve towards the grid and grid architecture of the future. The evolving grid and its control communications increasingly rely on commercial communications providers and a variety of technologies, from wireless (e.g., 5G, microwave, Wi-Fi) to wireline (fiber, copper) to radio communications (P25, other repeater-based systems), and all these communications systems rely on electric power. A reliable and resilient grid must account for this complex set of interdependencies in its planning activities, especially those involving restoration and recovery. The participation of all relevant parties in both planning and exercising of plans can prevent unexpected conditions that impede the reliable operation and recovery of the grid. Best practices for grid communications include using a Network Management System to document the operational state, define and monitor baselines, detect changes, and accelerate response to abnormalities. If transitioning from SONET to IP/packet-based systems, translating grid requirements into communications requirements for latency, bandwidth and throughput, IP packet delay variation, packet loss, and availability should inform and drive technology planning and selection as well as that communication system’s Quality of Service (QoS) policies and Service Level Agreements (SLAs). Secure and reliable timing is another key component of a reliable and resilient grid that can operate through adverse events. A trusted internal NTP configuration, an integrated and diverse timing delivery system, optimizing the timing architecture based on the transport technologies of the communications system, and using established standards can deliver the level of timing accuracy required by a range of time-sensitive power system applications.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2025

Edge ML for CAN bus intrusion detection in AVs

Autonomous Vehicles (AVs) are revolutionizing transportation, but their reliance on interconnected cyber-physical systems exposes them to unprecedented cybersecurity risks. This study addresses the critical challenge of detecting real-time cyber intrusions in self-driving vehicles by leveraging a dataset from the Udacity self-driving car project. We simulate four high-impact attack vectors, Denial of Service (DoS), spoofing, replay, and fuzzy attacks, by injecting noise into spatial features (e.g., bounding box coordinates) to replicate adversarial scenarios. We develop and evaluate two lightweight neural network architectures (NN-1 and NN-2) alongside a logistic regression baseline (LG-1) for intrusion detection. The models achieve exceptional performance, with NN-2 attaining an AUC score of 93.15% and 93.15% accuracy, demonstrating their suitability for edge deployment in AV environments. Through explainable AI techniques, we uncover unique forensic fingerprints of each attack type, such as spatial corruption in fuzzy attacks and temporal anomalies in replay attacks, offering actionable insights for feature engineering and proactive defense. Visual analytics, including confusion matrices, ROC curves, and feature importance plots, validate the models' robustness and interpretability. This research sets a new benchmark for AV cybersecurity, delivering a scalable, field-ready toolkit for Original Equipment Manufacturers (OEMs) and policymakers. By aligning intrusion fingerprints with SAE J3061 automotive security standards, we provide a pathway for integrating machine learning into safety-critical AV systems. Our findings underscore the urgent need for security-by-design AI, ensuring that AVs not only drive autonomously but also defend autonomously. This work bridges the gap between theoretical cybersecurity and life-preserving engineering, offering a leap toward safer, more secure autonomous transportation.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS1984

Storage, retrieval, and analysis of ST data

Space Telescope can generate multidimensional image data, very similar in nature to data produced with microdensitometers. An overview is presented of the ST science ground system between carrying out the observations and the interactive analysis of preprocessed data. The ground system elements used in data archival and retrieval are described and operational procedures are discussed. Emphasis is given to aspects of the ground system that are relevant to the science user and to general principles of system software development in a production environment. While the system being developed uses relatively conservative concepts for the launch baseline, concepts were developed to enhance the ground system. This includes networking, remote access, and the utilization of alternate data storage technologies.

Albrecht, R.↗
NASA NTRS1986

The VEGA Venus balloon experiment

In June 1985, two instrumented balloons were placed in the atmosphere of Venus as part of the VEGA mission. Each balloon traveled about 30 percent of the way around the planet at a float altitude near 54 kilometers. In situ sensors measured pressure, temperature, vertical wind velocity, cloud particle backscatter, ambient light level, and frequency of lightning. A ground-based network of 20 radio antennas tracked the balloons by very long baseline interferometry (VLBI) techniques to monitor the Venus winds. The history, organization, and principal characteristics of this international balloon experiment are described.

Sagdeev, R. Z.↗
NASA NTRS1997

An Efficient Multiblock Method for Aerodynamic Analysis and Design on Distributed Memory Systems

The work presented in this paper describes the application of a multiblock gridding strategy to the solution of aerodynamic design optimization problems involving complex configurations. The design process is parallelized using the MPI (Message Passing Interface) Standard such that it can be efficiently run on a variety of distributed memory systems ranging from traditional parallel computers to networks of workstations. Substantial improvements to the parallel performance of the baseline method are presented, with particular attention to their impact on the scalability of the program as a function of the mesh size. Drag minimization calculations at a fixed coefficient of lift are presented for a business jet configuration that includes the wing, body, pylon, aft-mounted nacelle, and vertical and horizontal tails. An aerodynamic design optimization is performed with both the Euler and Reynolds Averaged Navier-Stokes (RANS) equations governing the flow solution and the results are compared. These sample calculations establish the feasibility of efficient aerodynamic optimization of complete aircraft configurations using the RANS equations as the flow model. There still exists, however, the need for detailed studies of the importance of a true viscous adjoint method which holds the promise of tackling the minimization of not only the wave and induced components of drag, but also the viscous drag.

Reuther, James↗
NASA NTRS2011

The EVN Galactic Plane Survey - EGaPS

I present a catalogue of the positions and correlated flux densities of 109 compact extragalactic radio sources in the Galactic plane determined from an analysis of a 48-h Very Long Baseline Interferometry (VLBI) experiment at 22 GHz with the European VLBI Network. The median position uncertainty is 9 mas. The correlated flux densities of the detected sources are in the range of 2-300 mJy. In addition to the target sources, nine water masers have been detected, of which two are new. I derived the positions of the masers with an accuracy of 30-200 mas and determined the velocities of the maser components and their correlated flux densities. The catalogue and the supporting material are available at http://astrogeo.org/egaps.

Petrov, Leonid↗
DOE OSTI2023

Evaluating U.S. Natural Gas Environmental Performance

This work summarizes the U.S. Department of Energy National Energy Technology Laboratory's (NETL) modeling and analysis of natural gas environmental performance. Over the past year, NETL updated its U.S. natural gas supply chain life cycle baseline model to incorporate 2020 data, regionalize the transmission and distribution network into six regions, incorporate improved gathering and boosting stage and distribution stage emissions factors, and assign environmental burdens among the various co-products (i.e., crude oil, natural gas liquids, natural gas) of the natural gas supply chain. The updated natural gas model helps generate more comprehensive and regionalized natural gas profiles, mapping production basins to the relevant transmission and distribution regions downstream. The work incorporates data from multiple peer reviewed measurement-based studies to achieve the objective of providing a comprehensive understanding of emissions from the U.S. natural gas supply chain. This presentation also explores future work by NETL to develop a platform that enables stakeholders to customize key parameters within the natural gas LCA model and observe the corresponding effects on the carbon intensity of the overall supply chain. The platform aims to offer a convenient method for stakeholders to obtain relevant insights from the updated model, without diving deep into the intricacies of the NETL natural gas model with hundreds of parameters and linkages.

Khutal, Harshvardhan↗
DOE OSTI2026

Subsets of geostationary satellite data over international observing network sites for studying the diurnal dynamics of energy, carbon, and water cycles

The latest generation of geostationary satellites provide Earth observations similar to widely used polar-orbiting sensors but at intervals as frequently as every 5–10 min, making them ideal for studying the diurnal dynamics of land–atmosphere interactions. The NASA Earth Exchange (NEX) group created the GeoNEX datasets by collating data from several geostationary platforms, including GOES-16/17/18, Himawari-8/9, and GK-2A, and placing them on a common grid to facilitate use by the Earth science community. Here, we document the GeoNEX Coincident Ground Observations (GeCGO) dataset for terrestrial ecosystem studies and provide examples for its use. Currently, GeCGO provides GOES-16 Advanced Baseline Imager (ABI) data over a 10 km × 10 km area surrounding 1586 network sites across the Americas. GeCGO makes it easy to compare the time series of geostationary data with the diurnal ground observations, including carbon/water fluxes and aerosol optical depth, and is extensible to other regions. We also develop GeoNEXTools to facilitate analyses that require both GeoNEX data and other NASA satellite data. The objectives of this paper are to introduce GeCGO and GeoNEXTools and demonstrate their applications. First, we describe the details of GeCGO and GeoNEXTools. Second, we explain how GeCGO can be integrated with other satellite data. Finally, we showcase comparisons between GeCGO and observations from three ground-based networks. GeCGO is available at https://doi.org/10.25966/y5pe-xp41 (Hashimoto et al., 2025).

Hashimoto, Hirofumi [NASA Ames Research Center (AR↗