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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 397 records · Page 22
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

A High-Speed, High-Resolution Transition Edge Sensor Spectrometer for Soft X-Rays at the Advanced Photon Source

This project explores the design and development of a transition edge sensor (TES) spectrometer for resonant soft X-ray scattering (RSXS) measurements developed in collaboration between Argonne National Laboratory (ANL) and the National Institute of Standards and Technology (NIST). Soft X-ray scattering is a powerful technique for studying the electronic and magnetic properties of materials on a microscopic level. However, the lack of high-performance soft X-ray spectrometers has limited the potential of this technique. TES spectrometers have the potential to overcome these limitations due to their high energy resolution, high efficiency, and broad energy range. This project aims to optimize the design of a TES spectrometer for RSXS measurements and more generally soft X-ray spectroscopy at the Advanced Photon Source (APS) 29-ID, leading to improved understanding of advanced materials. We will present a detailed description of the instrument design and implementation. The spectrometer consists of a large array of approximately 250 high-speed and high-resolution pixels. The pixels have saturation energies of approximately 1 keV, sub-ms pulse duration and energy resolution of approximately 1 eV. The array is read out using microwave multiplexing chips with MHz bandwidth per channel, enabling efficient data throughput. To facilitate measurement of samples in situ under ultra-high vacuum conditions at the beamline, the spectrometer is integrated with an approximately 1 m long snout.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2025

Design and Construction of a Prototype B1pF Large Aperture Rutherford Cable Superconducting Magnet for the EIC Interaction Region

Brookhaven National Laboratory (BNL) was chosen to host the international Electron-Ion Collider (EIC), which will collide high energy and highly polarized hadron and electron beams with a center of mass energy up to 140 GeV. The Interaction Region (IR) requires several large aperture, relatively high field superconducting dipole and quadrupole magnets, some of which are very closely spaced. B1pF is a large aperture (300 mm coil ID), medium field (4.2 T), 3 meter long superconducting dipole magnet. Its size is larger than the largest superconducting magnet in the RHIC accelerator (180 mm DX dipole, 4.2 T, 3.7 m long). As such, B1pF was chosen as the representative magnet to be prototyped to demonstrate the technological choice, design and construction details. Here, this paper describes the analyses and considerations which informed both the magnet and magnet tooling designs. This paper will summarize the construction results so far, in particular the plan for novel coil winding tooling and the test coil winding & curing program that was carried out prior to coil and magnet construction.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2020

On the Performance of Detecting Injection of Fabricated Messages into the CAN Bus

There have been several public demonstrations of attacks on connected vehicles showing the ability of an attacker to take control of a targeted vehicle by injecting messages into their Controller Area Network (CAN) bus. In this article, using injected speed reading and Revolutions Per Minute (RPM) reading messages in in-motion vehicle, we examine the ability of the Pearson correlation and the unsupervised learning methods k-means clustering and Hidden Markov Model (HMM) to differentiate ’no-attack’ and ’under-attack’ states of the given vehicle. We found that the Pearson correlation distinguishes the two states, the k-means clustering method has an acceptable accuracy but high false positive rate and HMM detects attacks with acceptable detection rate but has a high false positive in detecting attacks from speed readings when there is no attack. The accuracy of these unsupervised learning methods are comparable to the ones of the supervised learning methods used by CAN bus Intrusion Detection System (IDS) suppliers. In addition, the article shows that studying CAN anomaly detection techniques using off-vehicle test facilities may not properly evaluate the performance of the detection techniques. Finally, the results suggest using other features besides the data content of the CAN messages and integrate knowledge about how the Electronic Control Units (ECUs) collaborate in building effective techniques for the detection of injection of fabricated message attacks.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Detecting Masquerade Attacks in Controller Area Networks Using Graph Machine Learning

Modern vehicles rely on a myriad of electronic control units (ECUs) interconnected via controller area networks (CANs) for critical operations. Despite their ubiquitous use and reliability, CANs are susceptible to sophisticated cyberattacks, particularly masquerade attacks, which inject false data that mimic legitimate messages at the expected frequency. These attacks pose severe risks such as unintended acceleration, brake deactivation, and rogue steering. Traditional intrusion detection systems (IDS) often struggle to detect these subtle intrusions due to their seamless integration into normal traffic. This paper introduces a novel framework for detecting masquerade attacks in the CAN bus using graph machine learning (ML). We hypothesize that the integration of shallow graph embeddings with time series features derived from CAN frames enhances the detection of masquerade attacks. We show that by representing CAN bus frames as message sequence graphs (MSGs) and enriching each node with contextual statistical attributes from time series, we can enhance detection capabilities across various attack patterns compared to using graph-based features only. Our method ensures a comprehensive and dynamic analysis of CAN frame interactions, improving robustness and efficiency. Extensive experiments on the ROAD dataset validate the effectiveness of our approach, demonstrating statistically significant improvements in the detection rates of masquerade attacks compared to a baseline that uses graph-based features only as confirmed by Mann-Whitney U and Kolmogorov-Smirnov tests (p < 0.05) .

Marfo, William [Univ. of Texas, El Paso, TX (Unite↗
DOE OSTI2023

RX-ADS: Interpretable Anomaly Detection Using Adversarial ML for Electric Vehicle CAN Data

Recent year has brought considerable advancements in Electric Vehicles (EVs) and associated infrastructures/ communications. Intrusion Detection Systems (IDS) are widely deployed for anomaly detection in such critical infrastructures. This paper presents an Interpretable Anomaly Detection System (RX-ADS) for intrusion detection in CAN protocol communication in EVs. Contributions include: 1) window based feature extraction method; 2) deep Autoencoder based anomaly detection method; and 3) adversarial machine learning based explanation generation methodology. The presented approach was tested on two benchmark CAN datasets: OTIDS and Car Hacking. The anomaly detection performance of RX-ADS was compared against the state-of-the-art approaches on these datasets: HIDS and GIDS. The RX-ADS approach presented performance comparable to the HIDS approach (OTIDS dataset) and has outperformed HIDS and GIDS approaches (Car Hacking dataset). Further, the proposed approach was able to generate explanations for detected abnormal behaviors arising from various intrusions. Furthermore, these explanations were later validated by information used by domain experts to detect anomalies. Other advantages of RX-ADS include: 1) the method can be trained on unlabeled data; 2) explanations help experts in understanding anomalies and root course analysis, and also help with AI model debugging and diagnostics, ultimately improving user trust in AI systems.

42 ENGINEERING↗
DOE OSTI2024

The Effects of Threshold Voltage and Number of Fins per Transistor on the TID Response of GF 12LP Technology

This abstract presents a comprehensive analysis of total ionizing dose (TID) response in GlobalFoundries’ 12LP 12nm bulk FinFET technology using 10keV X-rays. Devices with higher threshold voltages (VTs) demonstrated lower increases in off-state leakage current (IDS,OFF) post-irradiation, highlighting the mitigating role of high VT in TID response. Our data shows that transistors with fewer fins exhibit superior TID resistance, implying lower susceptibility to radiation effects. Our study also probed two bias conditions, "Gate-On" and "Pass-Gate," with the former displaying more severe TID degradation. Interestingly, p-type devices displayed negligible degradation, underscoring their inherent resilience to TID effects. Additionally, medium thick n-type devices echoed the fin-count-dependent TID response observed in other transistor types, further strengthening our findings. These results underscore the importance of strategic transistor selection and design for enhancing the TID resilience of future CMOS FinFET architectures, particularly critical in radiation-intense environments.

42 ENGINEERING↗
DOE OSTI2024

So you think you can track?

This work introduces a multi-camera tracking dataset consisting of 234 hours of video data recorded concurrently from 234 overlapping HD cameras covering a 4.2 mile stretch of 8-10 lane interstate highway near Nashville, TN. Video is recorded in cooperation with Tennessee State Department of Transportation and its policies. The video is recorded during a period of high traffic density with 500+ objects typically visible within the scene and typical object longevities of 3-15 minutes. GPS trajectories from 270 vehicle passes through the scene are manually corrected in the video data to provide a set of ground-truth trajectories for recall-oriented tracking metrics, and object detections are provided for each camera in the scene (159 million total before cross-camera fusion). Initial benchmarking of tracking-by-detection algorithms is performed against the GPS trajectories, and a best HOTA of only 9.5% is obtained (best recall 75.9% at IOU 0.1, 47.9 average IDs per ground truth object), indicating the benchmarked trackers do not perform sufficiently well at the long temporal and spatial durations required for traffic scene understanding. Video data, scene information, and vehicle trajectories are made publicly available at i24motion.org.

42 ENGINEERING↗
DOE OSTI2024

Experimental Investigation of the Effect of Air-Handling and DME-Propane Blends on the Performance and Emissions of a 4-Cylinder CI Engine

Dimethyl ether (DME) is considered an excellent alternative to diesel because of its higher cetane number and lower carbon content. Additionally, DME can be blended with abundantly available propane with minimal modifications to the propane infrastructure. This paper focuses on an experimental investigation of the effect of air-handling i.e., boost pressure and exhaust gas recirculation (EGR), and DME-propane blends on the combustion and emissions performance of a light-duty, four-cylinder, compression ignition (CI) engine. Here, the boost pressure and EGR sweeps were carried out and showed that higher boost pressures resulted in increased brake thermal efficiencies (BTE) at the expense of higher NOx emissions which could be reduced by an increase in EGR. The fuel sweeps were carried out at 0, 15 and 25% propane (neat, 85% and 75% DME) with 0% and 25% EGR. The fuel sweeps indicated that the ignition delay (ID) increased and burn duration (BD) decreased monotonically when the blend increased to 25% propane/75% DME. The results suggest optimum engine performance with neat DME at 105 kPa boost pressure and 25% EGR with propane addition improving the BTE with negligible increase in emissions. Higher contents of Propane, up to 25%, did not affect the variability of combustion, with standard deviations of burn duration and peak cylinder pressures below 1% for all test cases.

air-handling↗
DOE OSTI2021

Feasibility of Lead Fast Reactor Heat Exchanger Tube Online Monitoring

Within virtually all reactor systems with heat exchangers, the thinnest barriers between primary and secondary fluids are the heat exchanger tubes. Consistent with light water reactor experience, the advanced reactor heat exchangers are the most susceptible regions for corrosion and leakage (NRC, 2019). Moreover, traditional inspections and repairs using robotics will be expensive or impractical. On-line structural health corrosion monitoring (OLSHM) is therefore desirable to detect tube degradation prior to leaks that may allow mixing of heat exchanger fluids. Rhetorically many molten salt reactor (MSR) designers plan for on-line monitoring of critical reactor components including heat exchangers but there are no proven OLSHM sensors or systems for MSR heat exchangers. Guided wave ultrasound from the tube-end or ID using conventional low-temperature piezoelectric sensors has been shown to be sensitive to pits and cracks less than 50% through-wall in long pipes and tubes (Narayanan M.M., 2019) . Mode and frequency selection are important for practical sensor design decisions and ultimate performance. The L-0 wave-mode is easily excited by L-mode transducers but they have surface normal wave motion that will be sensitive to and more attenuative because of fluid interaction with the tube surface (Rose J.L., 1994). Shear Horizontal (SH) wave modes have material motion parallel to the tube surface therefore are virtually unaffected by fluid interaction. For SH-0 to be nondispersive in steel, the frequency-thickness product (MHz-mm) must be less than approximately 1.5. SH mode transducers must be coupled by a material that can support shear stress; a thin layer of honey works well for temporary sensors and an adhesive or a brazed metallurgical bond for permanent sensors. If sensors are incorporated into the fabrication process, the same kinds of SH-0 waves can be generated from the stagnant flow area of the tube OD without compromising the heat-exchange tube function. Piezoelectric materials are also available that can withstand harsh temperatures of molten salt heat exchangers (Stevenson T., 2015) (Machura et al., 2008). Though they may not have as high-performance characteristics, their performance only needs to be good enough to detect flaws of interest. This project attempts to demonstrate feasibility to develop a sensor that can be sensitive to flaws of interest and perform long-term monitoring of a molten salt reactor heat exchanger. Benefits to the advanced reactor system include: • Minimizing the expense of a reactor shut down for periodic inspection—only shutdown for cause. • Reduce risk of unexpected tube rupture by detecting flaws before they grow to through-wall leaks. • Cost reduction by minimizing need to design for manual/robotic inspectability. Transducer design, characterization of the beam profile, wedges vs. direct application of piezoelectric sensors, and ultrasonic pulse-echo responses from a representative heat exchange tube with fabricated flaw types are discussed.

On-Line Tube Monitor, Guided Wave Ultrasound, SH-0↗
DOE OSTI2020

CysLT1R receptor complex with Zafirlukast (P21 space group) structure (SFX@LCLS)

This dataset has overall quality of 2.4 Å (CC*=0.5), P1 space group and cell parameters 41.58, 68.56, 87.37, 76.33, 76.69, 81.19. Structure obtained from this data have been deposited under PDB ID 6RZ5. Data processing scripts are described in detail [here](https://gist.github.com/marinegor/96102c9b7ce87509a0832649d11ba927)

CXI↗
DOE OSTI2020

Mechanism and dynamics of fatty acid photodecarboxylase

The deposition contains diffraction patterns after hit-finding of time-resolved serial femtosecond crystallography measurements on fatty acid photodecarboxylase performed at LCLS XFEL (proposal ID: LT59). Five datasets are available. One dataset without pump laser excitation, labeled as "dark" and four time-resolved datasets with pump laser excitation at four different time-points labeled as "20 ps", "900 ps", "300 ns" and "2 μs". Please check the publication for more information about the datasets.

CXI↗
DOE OSTI2021

Three-dimensional coherent Bragg imaging of spontaneously rotating nanoparticles

This dataset contains spontaneous rocking curves of individual 60 nm gold particles, measured as CDI patterns in the Bragg geometry. The dataset provides a higher-quality alternative to ID 151, and is collected at lower energy, shorter detector distance (~120 mm) and using a He-filled flight tube. Additionally, this data includes measurements along the (111), (200), and (220) directions, providing access to multiple strain directions.

60 nm truncated-octrahedral gold nanoparticles↗
DOE OSTI2026

Integrase-On-Demand-Pipeline Data Set

Files needed to run the Integrase-On-Demand-Pipeline, a program designed to provide users with a list of putative attachment site and integrase pairs for a prokaryotic genome of interest. isles.pkl: Serialized python-object file, containing a dictionary of attachment site sequences and reference genomic island information extracted from the Genomic island database ints.gff: Gene format file containing annotations for all integrases referenced in isles.pkl. The source genome, gene coordinates, integrase name, protein IDs and amino acid sequence included. reps.msh: Binary file containing 1000 128-bit MurmurHash3 hashes for >80,000 genomes

McClain, Hannah Marie [Sandia National Laboratorie↗
DOE OSTI2021

AGU/AMS Abstract Search and Display Software

The AGU/AMS Abstract Search and Display Software is a standalone web application which enables the searching, storing, and displaying of abstracts featured at the annual American Geophysical Union (AGU) and American Meteorological Society (AMS) meetings. This application is designed for those who wish to host a standalone web application and feature a select subset of posters and talks scheduled for the AGU/AMS meetings. Please read the entirety of this README.md file before attempting to download and use the application. There are three views available via the UI: Lookup - enables searching and submitting posters for displaying on the summary view Manual Submission - allows individual manual submission of posters given a poster ID Summary - displays all posters submitted by users from the lookup view

Darnell, Wade↗
DOE OSTI2020

Interactive Mobility Data Landscape

This is the companion repository to NREL's Interactive Mobility Landscape App (SEE: DOE Code ID 61291. NREL SWR-21-10). The Interactive Mobility Data Landscape is a project intended as a map through the previously uncharted, growing mobility data science field. This attempts to categorize most of the data sources, specifications, and tools in the field of mobility. It has been built by the National Renewable Energy Laboratory's (NREL) Mobility, Behavior, and Advanced Powertrains (MBAP) research group. The software for the interactive landscape is located at https://github.com/NREL/mobility_landscapeapp This repo includes all of the data and images specific to the Interactive Mobility Data Landscape.

Shankari, Kalyanaraman↗
DOE OSTI2020

Interactive Mobility Landscape App

This is the companion repository to NREL's Interactive Mobility Data Landscape (SEE: DOE Code ID 61284. NREL SWR-21-09). The landscapeapp is an upstream NPM module that supports building interactive landscape websites such as the CNCF Cloud Native Landscape (source) and the LF Artificial Intelligence Landscape (source). The application is managed by Dan Kohn of CNCF and is under active development by Andrey Kozlov (who did most of the development to date, 9/13/2021) and Jordi Noguera.

Shankari, Kalyanaraman↗
DOE OSTI2021

Network Architecture Verification & Validation Tool

The NAVV Tool is an automation of Linux commands run Zeek IDS software on a packet capture to create a Microsoft Excel spreadsheet table breaking down network traffic observed. The tool automates the Zeek software analysis, the collation of logs, and then the dissection of the Conn.log and DNS.logs to create a summary table within a Excel. This spreadsheet can then be updated with network segments using CIDR formatting and labels along with inventory information including name and IP address. Using the tool again will integrate these label and color coding into the existing analysis table to aid in conducting an evaluation of the network traffic.

Nichols, DonovanW↗