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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 OSTI2025

Harnessing the Power of AI: Status and Expansion of Current Domestic Transport Security Through Flexible Embedded Hardware

As applications of Artificial Intelligence (AI) continue to expand, there are increasing opportunities to leverage applied AI methodologies with mobile transportation focused embedded systems. Current applications of AI in transportation focus on a variety of areas, including fuel efficiency, safety, security, and other broad fields of optimization or detection. To leverage these AI workflows and methodologies in the field, teams must utilize complex embedded systems capable of implementing these AI-enabled algorithms in real-time. In this paper, we will investigate how these algorithms can be integrated into existing technologies leveraging vehicle data - such as the Controller Area Network Transport Security Tracking and Reporting Unit (C-STAR). The C-STAR technology is an embedded platform with onboard computation capable of running next generation algorithms in vehicle systems AI, such as preventative maintenance, driver authentication, and transport security. As deployed in the field, the C-STAR has a limited AI functionality –this paper will directly discuss how a device like C-STAR can be utilized and the advantages of integrating these new technologies. We will open with relevant background information and transportation projects that leverage AI, focusing specifically on those around transport security such as vehicle identification, anomaly detection, and deterrence. We will then extend this into potential opportunities and scaling for AI methodologies using platforms like the C-STAR. Finally, we will speak directly to the challenges of deploying AI-powered workflows, such as computing power needs, bandwidth, hallucinations, and other regulatory considerations.

Cook, Adian [ORNL] (ORCID:0000000160825395)↗
DOE OSTI2022

Multiarea Distribution System State Estimation via Distributed Tensor Completion

Here, this paper proposes a model-free distribution system state estimation method based on tensor completion using canonical polyadic decomposition. In particular, we consider a setting where the network is divided into multiple areas. The measured physical quantities at buses located in the same area are processed by an area controller. A three-way tensor is constructed to collect these measured quantities. The measurements are analyzed locally to recover the full state information of the network. A distributed closed-form iterative algorithm based on the alternating direction method of multipliers is developed to obtain the low-rank factors of the whole network state tensor where information exchange happens only between neighboring areas. The convergence properties of the distributed algorithm and the sufficient conditions on the number of samples for each smaller network that guarantee the identifiability of the factors of the state tensor are presented. To demonstrate the efficacy of the proposed algorithm and to check the identifiability conditions, numerical simulations are carried out using the IEEE 123-bus system and a large-scale real utility feeder.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2021

A Multi-Stage Stochastic Risk Assessment With Markovian Representation of Renewable Power

Probabilistic forecasts provide a distribution of possible outputs and so can capture the uncertainty and variability of Variable Renewable Energy (VRE). However, taking advantage of uncertainty information has practical challenges that make it difficult to integrate probabilistic forecasting into control room decision-making. This paper proposes a novel use-case for probabilistic forecasts by incorporating them into the hour-ahead operations for situational awareness via a risk-averse multi-stage stochastic program. We employ a Markovian representation of the probabilistic forecasts that enables the formulation of the multi-stage problem and avoids a scenario generation phase. We test the model on a realistically sized system to assess risk and showcase the capability of using probabilistic renewable forecast as input to produce probabilistic output forecasts of future system states. The results show that the model can capture time consistency in the reserves and Area Control Error (ACE) forecast. The solution times are adequate for risk profiling in hour-ahead timescales.

forecasting↗
DOE OSTI2022

Strym: A Python Package for Real-time CAN Data Logging, Analysis and Visualization to Work with USB-CAN Interface

In this report, we describe a data analysis tool developed for decoding and analyzing vehicle data obtained from a passenger vehicle’s onboard controller area network (CAN) bus. The tool developed in this paper provides a timeseries framework to perform domain-specific analysis at scale when interpreting data from a vehicle or a collection of vehicles in light of how to design intelligent vehicle applications. The tool, called Strym, exploits the CAN bus mechanism of modern vehicles to capture data using commercially available CAN-to-USB hardware Comma.ai Panda devices, managed through open-source software Libpanda. Strym permits the decoding of vendor-specific CAN messages in a vehicle-agnostic manner. Through this, a researcher can characterize data throughput, assess data quality, and perform analyses. Such analyses are useful in a number of research such as studying human driving behavior in mixed-autonomy, new driver models, rare-event detection, traffic flow estimation, and custom control of vehicles.

Performance evaluation, Smart cities, Intelligent ↗
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 OSTI2020

Large Volume Airborne Contamination Monitoring To Support Nuclear Processes' Deactivation and Decommissioning

Current D and D operations at Hanford have demonstrated a flaw in the current state of the art capability of defining airborne contamination boundaries - Airborne particulate emissions of Pu-239 from CM2H operations on the Hanford Pu Finishing Plant were detected well beyond areas controlled for airborne Pu, putting numerous workers at risk for radiological assimilations - Current air samplers and CAM systems were surveying insufficient volumes of air to accurately predict where respiratory protection was required - Hanford is located in a high radon environment, making air monitoring for alpha emitting actinides challenging in high alpha radon induced backgrounds. - Inexpensive HEPA based home and industrial air purifiers filter significantly higher volumes of air than commercially available continuous-air monitoring (CAM) systems. - Inexpensive models capable of filtered air volumes exceeding 1000 times that of a CAM. - Detector system can be built to detect x-rays generated from actinide decay. - Branching ratios of x-rays are 4 orders of magnitude more intense from Plutonium decay than its gamma emissions. - Detector system can be a low resolution systems, as x-ray region of interest is not terribly congested. - Detection systems evaluated will be designed based on slabs of NaI or pixelated NaI or CsI panels. - Simple graphical user interface software will be developed to operate the detection system. - Concept is to deploy some of these air-purifying systems in nonradiological areas around SRNL to confirm radon can be rejected with confidence. - Generate some filters contaminated with plutonium via electroplated Pu or lab generated simulated particles. - then deploy units in known airborne radiological environments to establish systems ability to accurately measure actinide based hot particles Lab analyses will follow up the system analyses to ensure hot particles were correctly identified. Analysis by Scintillation: - Application of scintillation media to the surface of the filters is being explored. - Evaluating slabs of ZnS(Ag), application of powdered ZnS(Cu) and spray on Perkin Elmer Enhance. - Scintillation events would then be digitized with a digital camera and quantified. - Comparing against sensitivity of a PMT or SiPD readout. - Currently evaluating a Thorlabs 8 Megapixel Monochrome Scientific CCD Camera, hermetically sealed cooled package with a wide angle lens. - Wide angle lens allows complete view of HEPA filter from 7 inches away. - Images taken of glow in the dark paint, as well as plutonium-induced fluorescence. - Plutonium was flamed mounted on a 1 inch diameter stainless steel planchet, covered with a layer of mylar and a section of scintillating ZnS obtained from Eljon. - Currently working on reducing signal-to-noise levels to boost sensitivity. - Dark box to hold contaminated or electroplated source covered filters under fabrication. Analysis by x-ray Spectroscopy: - Branching ratios for x-ray emissions from Plutonium isotopes are orders of magnitude more intense than gamma-ray emissions. - The nuclear databases are incomplete on the branching ratios of some of the isotopes. - Evaluating some custom built SrI2 x-ray spectrometers vs a conventional windowed NaI detector. - SrI2 detectors are carbon-composite-windowed 51 mm x 51 mm. - MCNP calculations to establish self absorbance of filter media on 17 keV x-ray. Radon Rejection: - Activated charcoal pre-filter acts as a Radon trap. - Adding a time lapse feature to camera to aid in radon rejection. SRS Plutonium Fuel Form D and D Operations: Currently deploying air sampler to D and D operation of Pu-238 facility at SRS to generate some field samples to analyze.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2020

One–Step Fabrication of Nanocrystalline Nanonetwork SnO 2 Gas Sensors by Integrated Multilaser Processing

An integrated multilaser process is developed to fabricate nanocrystalline nanonetwork SnO 2 gas sensors in one integrated procedure, which combines electrodes fabrication, nanomaterials deposition, and postannealing. Interdigit electrodes are fabricated on an Au-coated fused silica substrate using a picosecond (ps) laser, which ablates the Au coating from the back of the substrate to pattern the electrodes. A novel transmitted Ps laser deposition (TPLD) process is designed to deposit SnO 2 nanonetwork on the interdigit electrodes with precise deposition area control under a close target-to-substrate distance. The obtained SnO 2 nanonetwork is in situ postannealed by a CO 2 laser to improve the crystallinity, while the nano morphology and grain size keep intact. To investigate the morphology and formation process of the nanonetwork, the microstructure of the laser-deposited SnO 2 layer is characterized. As a result, the crystallization control of CO 2 laser annealing is investigated through analyzing the Raman spectrum, X-ray diffraction (XRD) patterns, and lattice structures of the samples. By exposed to H 2 atmosphere, the fabricated gas sensor is demonstrated for H 2 monitoring.

08 HYDROGEN↗
DOE OSTI2024

A methodology to develop multi-physics dynamic fuel cell system models validated with vehicle realistic drive cycle data

Fuel cell (FC) technology has been identified as a technically attractive solution to decarbonize the transportation sector, especially for heavy-duty vehicles. In this context, the industry and the scientific community are in need of advanced fuel cell systems (FCS) models that are able to replicate real -world operating conditions. Due to the scarcity of said models in the open literature, this study aimed to develop a comprehensive methodology to calibrate and validate multi -physics dynamic FCS models. Therefore, the key contribution of this paper is the detailed description of the calibration process for each component and the calibration order. The specific focus here was to accurately describe the behavior of the FC stack as well as the cathode, anode, and cooling circuits of the balance of plant. The model was calibrated with the aid of experimental data from a Toyota Mirai FC electric vehicle, which was predominantly retrieved from the vehicle's Controller Area Network (CAN) bus system thereby negating the need for major intrusion into the powertrain system. The validation process was deemed successful with the model being able to truthfully replicate the characteristics of the FC vehicle operated on the World-wide harmonized Light duty Test Cycle (WLTC) 3b and US06 driving cycle. The time -resolved physical parameters such as the cathode pressure, mass flow, or the FC stack temperature were captured with high fidelity, while the overall performance parameters such as the H2 consumption in the stack and the system, and the compressor energy consumption were predicted accurately with a deviation lower than 0.47%, 1.75% and 1.89% with respect to the experimental data, respectively.

Lopez-Juarez, Marcos↗
DOE OSTI2026

Cellulose-MOFs hybrid materials: Chemistry and mechanism of applications in biomedical - A review

Rising costs and performance limits of modern biomedical materials motivate the search for advanced, biocompatible alternatives. Cellulose-based metal-organic frameworks (cellulose-MOFs) emerge as distinctive hybrids combining renewable polymer chemistry with tunable porous architectures, enabling uncommon structure–function relationships. Their large surface area, controllable pore size, adaptable functional groups, and efficient host–guest interactions underpin diverse biomedical functions. Till now, no comprehensive, application-focused review has systematically summarized cellulose-MOFs synthesis for biomedical applications. This review critically analyzes cellulose-MOFs, emphasizing mechanistic links between chemistry, synthesis routes, interfacial interactions, and biomedical performance, rather than cataloging applications alone. Antibacterial action, targeted drug delivery, and sensing/biosensing are discussed through comparative insights. The article identifies unresolved challenges and proposes future research pathways to rationally design next-generation cellulose-MOFs systems, guiding researchers and clinicians alike.

Biomedical↗
DOE OSTI2021

Skyshine Calculations for a Large Spent Nuclear Fuel Storage Facility with SCALE 6.2.3

The SCALE code system developed at Oak Ridge National Laboratory includes state-of-the-art capabilities for radiation source term and radiation transport simulations that can be used in numerous applications, including dose rate analyses of complex consolidated interim storage facilities (CISFs). A licensed CISF could be used to store tens of thousands of tonnes of spent nuclear fuel discharged from commercial power reactors using various cask and storage pad designs. A CISF design must comply with the regulatory requirements provided in 10 CFR Part 72, including requirements related to annual dose limits applicable to real individuals located beyond the area controlled by the licensee. Therefore, calculating a dose to the public is a necessary part of the licensing process for the construction of a CISF. These calculations are very challenging because of the complexity of the CISF design and the low magnitude of dose rate at large distances from the facility. This paper describes detailed far-field dose rate calculations performed for a proposed CISF using MAVRIC, the Monte Carlo radiation shielding sequence in SCALE 6.2.3, with automated variance reduction based on discrete ordinates calculations. The method presented in this paper uses a detailed Monte Carlo radiation transport simulation in one step from source to dose rate. A series of independent simulations was made using the complete site geometry (all casks present), but with only one cask containing radiation sources to obtain the dose rate maps produced by each storage cask. The CISF dose rate map was obtained by adding the dose rate maps produced by the independent individual cask simulations. Ample volumes of air and soil extending beyond the location of interest for dose rate calculation were included in the calculation model to properly simulate important radiation attenuation and scattering events that affect far-field dose rates. Furthermore, a comprehensive sensitivity study is included in this paper to illustrate the importance of selecting appropriate air volume, mass density, and composition for CISF skyshine dose rate calculations. Dry soil and soil containing water were analyzed to determine their effects on groundshine radiation.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2024

Little prairie under the panel: testing native pollinator habitat seed mix establishment at three utility-scale solar sites in Minnesota

Abstract As more land is being utilized for large-scale solar energy projects, there are increasing discussions from stakeholders on how to utilize land under solar panels to promote biodiversity. One path is to plant habitat beneficial to pollinators and other insects, but there have been few long-term studies that examine how different vegetation and seed mixes establish underneath solar panels. This study addresses a scientific gap to determine whether native pollinator seed mixes successfully establish over time under solar arrays using a systematic assessment of eight seed mixes planted at three utility-scale solar sites in Minnesota. We assess establishment with a percent native coverage metric, which is an assessment of native species observations compared to total observations during percent cover analyses in our vegetative test plots. The percent native coverage metric allows for a measurement of how the seed mix established and how the seed mix persists over time. The percent native coverage under and in between the solar photovoltaic (PV) arrays rose from 10% after one year of planting to 58% after three years across all sites, while the native coverage of the full sun control area rose from 9.6% to 70% under the same period, showing that native prairie and pollinator plants successfully established under the array, although to a lesser extent than in full sun conditions. Percent native coverage under the PV arrays rose 5- to 8-fold for each of the three sites from over the course of the study, while the coverage of weeds decreased for all three sites over the same period. Percent native coverage varied by seed mix over the project years, but every seed mix experienced a higher percent native coverage year after year under the PV arrays. Our results did not indicate a difference in establishment across placement within the array; the center, west, and east portions of the areas in between panels had similar establishment rates at two out of three sites, indicating that the same seed mix can be applied throughout the array. Out of 101 plant species seeded, we observed the establishment of 68 species in our vegetative test plots, and we detailed the top 20 observed species to inform future seed mix development. Based on these findings, native pollinator vegetation can establish over time at solar arrays, and it can be suitable for creating habitat at utility-scale solar sites.

14 SOLAR ENERGY↗
DOE OSTI2020

ACTT: Automotive CAN Tokenization and Translation

Modern vehicles contain scores of Electrical Control Units (ECUs) that broadcast messages over a Controller Area Network (CAN). Vehicle manufacturers rely on security through obscurity by concealing their unique mapping of CAN messages to vehicle functions which differs for each make, model, year, and even trim. This poses a major obstacle for after-market modifications notably performance tuning and in-vehicle network security measures. We present ACTT: Automotive CAN Tokenization and Translation, a novel, vehicle-agnostic, algorithm that leverages available diagnostic information to parse CAN data into meaningful messages, simultaneously cutting binary messages into tokens, and learning the translation to map these contiguous bits to the value of the vehicle function communicated.

Verma, Miki↗
DOE OSTI2024

Online Detection of Power Grid Anomalies via Federated Learning

Data from sensors is critical for advanced applica- tions that support efficient, reliable, and resilient electric grid operations. Historically, data from phasor measurement units (PMU) has been utilized to develop a wide variety of wide area control and protection applications suitable for power grid control centers. However, until now, most of these could not be deployed for automated operations due to a set of data corruption challenges and uncertainty in the incoming data pipeline. In this paper, we address the problem of detecting different variety of anomalies that are evident in different high- speed power grid measurements. The paper discusses a workflow for handling problems with data acquisition and highlights some of the key findings suitable for anomaly detection in a centralized and distributed environment. The effectiveness of the proposed method was demonstrated with results utilizing realistic PMU datasets

Shinkle, Matthew W.↗
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↗
DOE OSTI2024

Cy-Phy ADS: Cyber Physical Anomaly Detection Framework for EV Charging Systems

Today’s large-scale Electric Vehicle (EV) infrastructures are heavily dependent on information communication technologies to maintain their operation and to support communication within sub-system components as well as the outside world. These technologies are vulnerable to various cyber and physical threats. Timely identification and mitigation of these threats are critical for improving human safety, avoiding economic losses, and preventing catastrophic system failures. By addressing this, our work presents a ResNet Autoencoder (AE) based Cyber-Physical Anomaly Detection System (Cy-Phy ADS) for detecting anomalies in EV Controller Area Network (CAN) protocol communication. It consists of four main components: Cyber-Physical Feature Extractor, ResNet AE-based Anomaly Detection Framework, Cyber-Physical Health Metric (CPHM), and Visualization Dashboard. The presented framework was trained and tested using CAN data collected from the EV charging system testbed at the Idaho National Laboratory. The presented Cy-Phy ADS compared against six widely used unsupervised anomaly detection algorithms: One Class Support Vector Machine (OCSVM), Variational Autoencoder (VAE), LSTM Autoencoder (LSTM AE), Isolation Forest (IForest), Principle Component Analysis (PCA) and Local Outlier Factor (LOF). Here the presented approach showed the highest accuracy among the compared methods. Further, the proposed approach showed comparable performance in terms of precision, F1, and False positive rate. It also showed the lowest training and inference time compared to the neural network-based baseline algorithms compared against with. Additionally, the Cy-Phy ADS has advantages such as unsupervised training, the ability to provide a holistic metric for system health characterization, and non-linear feature extraction.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2020

In-Cu alloy substrates for low-temperature chemical vapor deposition of Mo2C

Two-dimensional transition metal carbides are promising materials because of their potential for combining the favorable properties of transition metal carbides with the high aspect ratio of two-dimensional materials. Though commonly produced by top-down wet-chemical synthesis methods, synthesis by chemical vapor deposition is being considered because of its ability to achieve large areas, controlled layer thickness, and reduced defect density. Typically, liquid Cu is used as a synthesis substrate, though the high melting temperature of Cu (1085 °C) requires high synthesis temperatures. Thus, alternative substrates have been studied in order to reduce the necessary melting temperature. This work systematically studies the impact of synthesis parameters and substrate composition on the growth of ultrathin Mo2C (∼4–120 nm thick) by chemical vapor deposition on an In-Cu alloy. Mo2C flake size increases, and graphene/Mo2C heterostructures form with an increase in the methane flow rates. Increasing the In composition slightly decreases surface coverage and coalescence but does not appreciably impact the Mo2C flake size. Increasing In content also decreases the alloy substrate melting temperature so that a lower temperature synthesis (800 °C) can be performed. However, the necessary high temperatures for pyrolysis of methane lead to a lower limit for the synthesis temperature, similar to graphene.

Young, Katherine T. (ORCID:0000000285845576)↗
DOE OSTI2023

Analysis of a Runtime Data Sharing Architecture over LTE for a Heterogeneous CAV Fleet

This paper describes a lightweight runtime architecture for telemetry, communication, and control of cars deployed with advanced driver assistance systems where a human is in the loop with the car, via an LTE connection. The system architecture supports both local control decisions based on car sensors and safety algorithms as well as high-level input from external systems that may provide insight into traffic state ahead of sensor data. Implementation of the architecture is done in ROS and depends on open-source software packages for runtime decoding of information from the vehicle’s controller area network (CAN) and integration of GPS data from accompanying sensors. The contribution of the paper is to describe the overall architecture, the data it can communicate to other systems, performance of the system at runtime, and challenges faced when deploying the architecture across a heterogeneous fleet. Preliminary results from analysis of test data will provide insights into whether the use of high-latency communication can be effective for societal-scale intelligent transportation systems when applied in future scenarios

Richardson, Alex↗
DOE OSTI2022

Time-Based CAN IDS Paper Results Code

Modern vehicles are complex cyber-physical systems made of hundreds of electronic control units (ECUs) that communicate over controller area networks (CANs). This inherited complexity has expanded the CAN attack surface which is vulnerable to message injection attacks. These injections change the overall timing characteristics of messages on the bus, and thus, to detect these malicious messages, time-based intrusion detection systems (IDSs) have been proposed. However, time-based IDSs are usually trained and tested on low-fidelity datasets with unrealistic, labeled attacks. This makes difficult the task of evaluating, comparing, and validating IDSs. Here we detail and benchmark four time-based IDSs against the newly published ROAD dataset, the first open CAN IDS dataset with real (non-simulated) stealthy attacks with physically verified effects. We found that methods that perform hypothesis testing by explicitly estimating message timing distributions have lower performance than methods that seek anomalies in a distribution related statistic. In particular, these “distribution-agnostic” based methods outperform “distribution-based” methods by at least 55% in area under the precision-recall curve (AUC-PR). Our results expand the body of knowledge of CAN time-based IDSs by providing details of these methods and reporting their results when tested on datasets with real advanced attacks. Finally, we develop an after-market plug-in detector using lightweight hardware, which can be used to deploy the best performing IDS method on nearly any vehicle.

Moriano, Pablo [Oak Ridge National Lab. (ORNL), Oa↗