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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 sources NASA NTRS DOE OSTI

DOE OSTI2025

Block segmentation in feature space for realtime object detection in high granularity images

Computer vision has applications in object detection, image recognition and classification, and object tracking. One of the challenges of computer vision is the presence of useful information at multiple distance scales. Filtering techniques may sacrifice details at small scales in order to prioritize the analysis of large-scale features of the image. We present a strategy for coarse-graining multidimensional data while maintaining fine-grained detail for subsequent analysis. The algorithm is based on fixed-size block segmentation in the feature space. We apply this strategy to solve the long-standing challenge of detecting particle trajectories at the Large Hadron Collider in real time.

Computer vision↗

DOE OSTI2025

TR-XPS Realtime Analysis Tool (ArroyoXPS) v0.1

The ALS has developed a Time-Resolved X-ray Photoelectron Spectroscopy (TR-XPS) technique, which involves applying a specific pattern of voltage curves to a sample while measuring XPS peaks. This pattern is repeated over multiple cycles, and changes in the material's response provide valuable scientific insights. Traditionally, file-based analysis workflows have been used: scans are run for a predetermined time, and after one or more scans are complete, calculations are made. ArroyoXPS changes this by offering in-experiment scan and analysis, allowing researchers to gain insights before a scan is finished. This enables them to adjust experimental parameters quickly, potentially saving valuable beamtime. ArroyoXPS includes tools for integrating with beamline control systems, performing analysis, and visualizing scan data in a web browser.

McReynolds, Dylan [Lawrence Berkeley National Labo↗

DOE OSTI2025

Passive Wireless Sensors for Realtime Temperature and Corrosion Monitoring of Coal Boiler Components Under Flexible Operation (Final Technical Report)

Researchers at West Virginia University (WVU) propose to demonstrate inexpensive wireless, high-temperature sensors for real-time monitoring of the temperature and corrosion of metal components, which are commonly used in coal-fired boilers. This study presents the development of cost-effective wireless high-temperature sensors for real-time temperature and corrosion monitoring in coal-fired boilers' metal components. The focus is on fabricating and evaluating chipless radio-frequency identification (RFID) sensors capable of operating between 25-1300 ºC. Efforts were directed towards designing passive RFID sensor and interrogator antenna with a broad frequency range, optimizing a microstrip patch antenna sensor integrated into a "peel-and-stick" format for efficient application to various metal specimens without altering their geometry. Additionally, this research aimed to assess the sensor responses under accelerated high-temperature conditions, correlating corrosion and cracking mechanisms with sensor data. An investigation of through-wall data acquisition techniques was also planned, facilitating unobtrusive monitoring of sensor responses housed within metal enclosures. Ultimately, this work sought to establish a robust passive wireless sensor system for the continuous health monitoring of metal components in operational settings, thereby contributing to enhanced safety and efficiency in coal-fired power plants.

20 FOSSIL-FUELED POWER PLANTS↗

DOE OSTI2024

Fermi -LAT follow-up observations in seven years of real-time high-energy neutrino alerts

The realtime program for high-energy neutrino track events detected by the IceCube South Pole Neutrino Observatory releases alerts to the astronomical community with the goal of identifying electromagnetic counterparts to astrophysical neutrinos. Gamma-ray observations from theFermi-Large Area Telescope (LAT) enabled the identification of the flaring gamma-ray blazar TXS 0506+056 as a likely counterpart to the neutrino event IC-170922A. By continuously monitoring the gamma-ray sky,Fermi-LAT plays a key role in the identification of candidate counterparts to realtime neutrino alerts. In this paper, we present theFermi-LAT strategy for following up high-energy neutrino alerts applied to seven years of IceCube data. Right after receiving an alert, a search is performed in order to identify gamma-ray activity from known and newly detected sources that are positionally consistent with the neutrino localization. In this work, we study the population of blazars found in coincidence with high-energy neutrinos and compare them to the full population of gamma-ray blazars detected byFermi-LAT. We also evaluate the relationship between the neutrino and gamma-ray luminosities, finding different trends between the two blazar classes BL Lacs and flat-spectrum radio quasars.

Astronomy & Astrophysics↗

DOE OSTI2024

The data acquisition system of the LZ dark matter detector: FADR

The Data Acquisition System (DAQ) for the LUX-ZEPLIN (LZ) dark matter detector is described. The signals from 745 PMTs, distributed across three subsystems, are sampled with 100-MHz 32-channel digitizers (DDC-32s). A basic waveform analysis is carried out on the on-board Field Programmable Gate Arrays (FPGAs) to extract information about the observed scintillation and electroluminescence signals. This information is used to determine if the digitized waveforms should be preserved for offline analysis. The system is designed around the Kintex-7 FPGA. In addition to digitizing the PMT signals and providing basic event selection in real time, the flexibility provided by the use of FPGAs allows us to monitor the performance of the detector and the DAQ in parallel to normal data acquisition. Furthermore, the hardware and software/firmware of this FPGA-based Architecture for Data acquisition and Realtime monitoring (FADR) are discussed and performance measurements are described.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

DOE OSTI2026

Current-Driven Symmetry Breaking and Spin–Orbit Polarization in Chiral Wires

The spin dynamics of electrons in chiral molecular systems remains a topic of intense interest, particularly regarding whether geometric chirality inherently induces spin polarization in current-carrying electrons. In this work, we employ ab initio real-time time-dependent density functional theory (rt-TDDFT) to directly simulate the interplay among charge current, spin, and orbital. This realtime tracking extends beyond perturbative treatments, and we analyze how nonequilibrium currents effectively lift the symmetry constraints of screw rotation and time-reversal symmetry. We find that the emergence of spin and orbital angular momenta is dynamically correlated with a concomitant loss of translational (linear) momentum, which we interpret as an intrinsic consequence of current-driven symmetry lowering. The implications of this mechanism for chirality-induced spin selectivity and spintronics device design are discussed.

chiral wire↗

DOE OSTI2026

Divertor detachment and heat exhaust mitigation control in KSTAR with tungsten divertor

KSTAR has recently undergone an upgrade to use a new tungsten divertor to run experiments in ITER-relevant scenarios. Even with a high melting point of tungsten, it is important to control the heat flux impinging on tungsten divertor targets to minimize sputtering and contamination of the core plasma. Heat flux on the divertor is often controlled by increasing the degree of detachment of scrape-off layer plasma from the target plates. In this work, we have demonstrated successful divertor detachment and heat exhaust dissipation control experiments using two different methods. The first method uses attachment fraction as a control variable which is estimated using ion saturation current measurements from embedded Langmuir probes in the divertor. The second method uses a novel machine-learning-based surrogate model of 2D UEDGE simulation database, DivControlNN. We demonstrated running inference operation of DivControlNN in realtime to estimate heat flux at the divertor and use it as the control variable in a feedback loop with impurity gas flow. We present interesting insights from these experiments including a systematic approach to tuning controllers and discuss future improvements in the control infrastructure and control variables for future burning plasma experiments.

KSTAR tungsten divertor operations↗

DOE OSTI2025

Real-time Simulation Model of a Vanadium Redox Flow Battery Energy Storage System with Power Electronics Integration

This paper presents a real-time (RT) model of a 20 kW multi-stack vanadium redox flow battery (VRFB) system including power electronics converters in a single real-time framework. Implemented on a Typhoon HIL 604 device, this model is designed to simulate the operation of the flow battery with system-level controls. To provide a realistic overview of a VRFB system, the model incorporates a closed-loop control system for managing the speeds of two centrifugal pumps and an electro-thermal model of the battery stacks, enabling realtime temperature estimation. Furthermore, a two-stage power electronics topology and associated control solution are presented, operating with distinct strategies for interfacing the battery model with the main grid and a local load. Results from simulations demonstrate that the VRFB model is well suited for RT environment, and system can manage power during both charging and discharging cycles, while a SCADA monitor is used to display critical variables for safe, efficient, and reliable operation of the VRFB systems.

Rezende Da Costa Reis Kimpara, Renata [ORNL] (ORCI↗

DOE OSTI2024

Conditional Experts for Improved Building Damage Assessment Across Satellite Imagery View Angles

Rapid building damage assessment (BDA) is vital in guiding disaster response missions and estimating population distribution across impacted areas. While commercial satellite imagery providers have enabled near-daily monitoring of the Earth, near-realtime assessment of disaster scenarios frequently requires analysis of off-nadir imagery, as satellites are often far from impacted areas for at-nadir post-event imaging to occur Such scenarios are, however, underrepresented in existing BDA datasets and methodologies. With this motivation, we investigate generalization capabilities of current BDA practices across overhead view-angles and strategies for their improvement. Using a labeled dataset of images capturing conflict-related damages, we first train a baseline BDA architecture using imbalanced and balanced datasets with respect to view-angle. Then, we explore conditional convolutions parameterized on image features, image nadir, and their combination as a mechanism for conditioning on view-angles. Experiments demonstrate the limitations of current practice and the potential of conditional mechanisms to increase model robustness to view-angle variations.

Ambrozio Dias, Philipe↗

DOE OSTI2025

The Second Skin: A Wearable Sensor Suite that Enables Real-Time Human Biomechanics Tracking Through Deep Learning

Objective: Real-time determination of human kinematics and kinetics could advance biomechanics research and enable valuable applications of biofeedback and generalizable exoskeleton control. Here, this work aims to investigate a taskindependent, user-independent method for obtaining precise realtime joint state estimation across lower-body joints during a wide variety of tasks. Methods: We developed a generalizable sensing approach using a suit comprised of inertial measurement units (IMUs) and pressure insoles. With the suit, we collected a dataset of 33 tasks commonly performed during construction and hazardous waste cleanup (N = 10). We then trained deep learning user-independent, task-agnostic models to estimate joint lowerbody kinematics and dynamics using only worn sensor data. We likewise computed joint kinematics and dynamics analytically from sensor data to serve as a comparison tool for model results. Results: Our models achieved overall angle estimation root-meansquared-errors (RMSE) of 6.56±.92°, 8.60±1.01°, 7.58±.89°, and 6.00±.73° compared to 13.9±.1.3°, 15.31±1.0°, 10.76±.70°, and 7.56±.48° via analytical methods at the lower back, hip, knee, and ankle, respectively. Likewise, our models achieved overall normalized moment estimation RMSEs of .207±.069 Nm/kg, .242±.044 Nm/kg, .202±.038 Nm/kg, and .193±.034 Nm/kg compared to .306±.036 Nm/kg, .407±.021 Nm/kg, 1.18 ±.022 Nm/kg, and 1.73±.071 Nm/kg via analytical methods at the lower back, hip, knee, and ankle, respectively. Conclusion: These results are comparable to other state-of-the-art wearable sensing systems, establishing deep learning as a viable sensing approach that generalizes to new users and tasks. Significance: This work shows promise for enabling accurate real-world biomechanical data collection and enhancement of biofeedback systems and wearable robot control.

Casey, Ryan T. F. [Georgia Institute of Technology↗

DOE OSTI2026

Operational Simulation of Multi-Functional Charging Station for Sustainable Transportation

As transportation systems move toward electrification and decarbonization, multifunctional charging stations (MFCS) are emerging as key infrastructure for electric vehicles (EVs) and hydrogen fuel cell vehicles (HFCVs). This paper presents a simulation model of an MFCS that integrates solar photovoltaic (PV), wind power, battery storage, hydrogen (\mathrm{H}_{2}) production, dual-pressure \mathrm{H}_{2} storage, fuel cells, and dynamic grid interactions. The model simulates daily operations using 5 -minute resolution data to capture realtime variability in renewable energy (RE), demand, and electricity prices. A flexible dispatch algorithm dynamically allocates energy for EV charging, \mathrm{H}_{2} production, storage, and grid transactions while respecting system constraints. Results show that the MFCS effectively prioritizes RE usage, minimizes waste, meets diverse energy demands, and achieves net operational profit. The model serves as a valuable decision-support tool for designing and optimizing integrated clean energy hubs for zero-emission transportation.

Gbadegoye, Jeremiah [University of Tennessee, Knox↗

DOE OSTI2026

Ride Pingo to Transit

In this project, we developed an on-demand microtransit first- and last-mile service. To integrate the service with fixed-route transit, we developed a feature called Transit Connect that prioritized riders’ on-time arrival at the transit station over other service requirements. We first prototyped service and related algorithms in a simulated environment, and then piloted the service in the city of Kent, Washington. Our algorithm incorporates request-specific hard drop-off deadlines to ensure timely arrivals for transit transfers. In the pilot, these constraints were obtained from GTFS Realtime data to accurately determine the schedule of the transit and the location of the stations. This approach introduced the ability to accept or decline new requests based on the timing of transit connections for these new requests and connection status of onboarding customers. The pilot (called “Ride Pingo to Transit”) deployed a fleet of three 14-person vans, ran from September 2021 to March 2023, and served a total of 21,329 trips. Transit Connect was offered for drop-offs at both Kent Station and the Kent Valley hub. In total, 2,844 such trips were completed. This dataset was collected from our pilot, which includes the following: - Requests: List of all trip requests, including those that were actually served and those not materialized. - Fleet: Daily vehicle service logs. - Service details: Daily vehicle stop logs (boarding and alighting). - Trip types: First mile, last mile, or point-to-point. ![pingo to transit](pingo-to-transit.jpg)

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2024

Complementing the CCS Class VI Well Permit Process with DOE-NETL's SMART Initiative Tools and Workflows

This is a presentation on model explorer developed under SMART initiative Task 2. Our team will present the current progress of the model explorer in using machine learning models to accelerate CCS project at GWPC meeting. Model explorer bring new capabilities, (fast, Realtime, and accurate) that can help CCS stakeholders including regulatory agencies, public and site operators make faster decisions and process information and data.

Hosseini, Seyyed↗

DOE OSTI2024

Multi-Scale Integrated Monitoring System for Enhancing Methane Emission Detection, Quantification & Prediction

This report details the progress and findings of a comprehensive study on reviewing existing solutions, identifying technology gaps, and formulating an “all-in-one” integrated strategy for developing the next-generation multiscale methane monitoring and modeling platform, conducted under grant number DE-FE0032292. Co-led by Dr. David Ebert, Dr. Binbin Weng, and Dr. Chenghao Wang at the University of Oklahoma, the project’s goal was to develop an integrated approach for building this engineering platform to detect, quantify, and mitigate methane emissions across various temporal scale, spatial scales, and sectors. The planning grant study began with an extensive review of various methane sensing and monitoring technologies and systems, surveying over 100 technology providers globally. This review revealed the prevalence of optical methods over chemical methods in commercially available sensors, with Non-Dispersive Infrared (NDIR), Tunable Diode Laser Absorption Spectroscopy (TDLAS), and Optical Gas Imaging (OGI) cameras being the most prevalent options. A trend towards more advanced optical techniques was observed, driven by increased regulatory focus and technological advancements. The technical evaluation of these sensing technologies provided crucial insights into their capabilities and limitations. The study examined emerging technologies such as Differential Absorption LiDAR (DIAL), which show promise for high-precision and long-range detection. The team then investigated the features and application bandwidth of various sensing platforms, including handheld, fixed/stationary, mobile, aerials, and spaceborne monitors. Pilot field studies were conducted to assess the capabilities of solutions for different emission scenarios. Field work with sensor deployments was conducted at three distinct site types: an oil & gas industry site, a cattle ranching operation, and a waste processing facility. The team also conducted a thorough review of methane flux inverse modeling approaches, focused on physically based methods. These approaches were categorized into simple, intermediate, and advanced methods. A realtime WRF-GHG (Weather Research and Forecasting-Greenhouse Gas) modeling system was developed and applied, incorporating multiple data sources to guide field experiments and inform methane plume detection. The project identified and analyzed numerous categories of methane data sources, including satellite measurements, ground-based sensors, and inventory databases. Key platforms examined include EDGAR, EPA GHGI, NASA TROPOMI, Carbon Mapper, and Climate TRACE, among others. The team proposed an architecture for a comprehensive methane monitoring platform. This system incorporates multi-source data acquisition, advanced data processing and assimilation, interactive visualization tools, and analytical capabilities for emissions forecasting and scenario analysis. The proposed platform aims to provide a user-friendly interface catering to various stakeholders, from researchers to policymakers. The architecture includes sophisticated data ingestion methods, a centralized data warehouse, and advanced analytical tools for data fusion and interpretation. To ensure the relevance and effectiveness of the proposed system, a comprehensive survey was conducted to gather stakeholder input on system requirements. Key findings include a strong need for integrating various data types and formats, a preference for real-time data updates and advanced visualization tools, and a demand for user-friendly interfaces catering to different expertise levels.

03 NATURAL GAS↗

DOE OSTI2025

Report on the "Symposium on Advancements in Simulating Neutron Scattering Instruments and Experiments"

An international group of researchers gathered for the “Symposium on Advancements in Simulating Neutron Scattering Instruments and Experiments” at Oak Ridge National Laboratory (ORNL) on October 29- 30, 2024. The symposium highlighted state-of-the-art advancements in neutron scattering instrument simulations and the development of digital twins for scattering instruments. This document summarizes the recurring themes from the 19 talks and discussions. A key focus was on the role of neutronics simulations in the design, analysis, and performance optimization of neutron instruments. Presentations featured the most widely used software packages, such as McStas, McVine, Vitess, and MCNP. Another major theme addressed the analysis and interpretation of experimental neutron data, where materials models and simulations play a critical role. Talks on relevant software tools, including NCrystal, OCLimax, and Paradyse, were delivered by their respective developers. During the symposium, both talks and breakout sessions explored strategies to enhance the interplay between neutronics simulations and materials models. A key question emerged: How can we develop a RealTime Explainable Digital Twin of a neutron scattering experiment? Discussions include the idea that the Digital Twin concept has implications for instrument control and testing. Through this symposium, we gained valuable insights into the challenges and requirements for developing a Real-Time Explainable Digital Twin of a neutron scattering experiment. The discussions and presentations paved the way for future innovations in this rapidly advancing field.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

DOE OSTI2026

Virtual Inspection of Advanced Manufacturing via Process-Scale Digital Twins (Abbreviated Report)

Inspection and certification comprise the most significant bottlenecks in advanced manufacturing for NNSA applications, often requiring far more time and resources than the fabrication of the parts themselves. Traditional methods, such as manual review and X-ray computed tomography, are not only slow and costly, but also struggle to provide a clear connection between manufacturing instructions and the final performance of critical components. This gap limits both the agility and assurance needed to support the modernization and safety of the United States nuclear stockpile. In response, our Strategic Initiative established a digital twin framework that integrates realtime process monitoring, automated data analysis, and immersive virtual reality collaboration into a unified inspection pipeline. By leveraging data from sensors, machine instructions, and imaging, we created high-fidelity virtual models of manufactured parts that could be rapidly analyzed and certified. This approach was first demonstrated with Direct Ink Write, and then extended to other manufacturing settings, including conventional (or “subtractive”) manufacturing and to predict the end of life performance of parts per the aging and lifetimes programs. The result is a transformational capability: inspection times have been reduced by a factor of 120,000 without loss of accuracy and while simultaneously improving traceability and confidence in part quality. This framework not only streamlines certification for critical applications, but also positions the national security enterprise to respond more flexibly to emerging challenges, supporting agile manufacturing and digital engineering practices across a broad range of mission-relevant domains.

42 ENGINEERING↗

DOE OSTI2026

Automation of Nanoparticle Synthesis Processes in a Plasma Environment Using LabVIEW

This work presents an automated control system for the synthesis of nanomaterials by plasma-enhanced chemical vapor deposition (PECVD), implemented using the LabVIEW software environment. The main objective of the study is to develop an integrated hardware-software platform that enables sequential control of the key stages of the PECVD process, including vacuum chamber preparation, pressure monitoring, working gas supply, plasma ignition, power matching, cyclic nanomaterial growth, and optical monitoring of nanoparticles in the plasma environment. The use of LabVIEW made it possible to integrate actuator control, experimental parameter acquisition, and realtime process visualization within a single automated system. The automated cycle begins with evacuation of the reaction chamber to a predefined base pressure. Transition to the next stage is permitted only after the specified pressure threshold has been reached, ensuring reproducible initial conditions for each experiment. The program then controls the supply of the working gas through mass flow controllers (MFCs). In this work, two gas-flow control modes were considered: analog control using a 0-5 V voltage signal and digital communication via RS-232 interface. It was shown that the analog approach requires accurate scaling of the control voltage, since applying 5 V corresponds to full-scale opening of the controller and results in the maximum gas flow. In contrast, the RS232 interface enables the gas flow rate to be specified directly in sccm, improving the accuracy, flexibility, and convenience of gas-environment control. After pressure stabilization, LabVIEW initiates RF plasma ignition and executes the RF matching algorithm aimed at minimizing reflected power and improving the stability of the plasma process. A separate software module implements the cyclic nanomaterial growth mode, in which the plasma-on time, plasma duration, and total number of synthesis cycles are predefined. This approach makes it possible to control material accumulation on the substrate and to correlate the process parameters with the morphological characteristics of the resulting nanostructures. The final module of the system is designed for optical monitoring of the nanoparticle cloud density in dusty plasma. For this purpose, the change in the intensity of laser radiation passing through the plasma region is recorded using a photodetector and a Keithley 2401 measuring unit connected to LabVIEW via RS-232 interface. The difference between the initial and modified optical signal intensity is used as a diagnostic parameter characterizing the formation and temporal evolution of nanoparticles. The developed system demonstrates that LabVIEW can be effectively applied not only for the automation of individual instruments, but also for the implementation of a complete digital control cycle for PECVD-based nanomaterial synthesis.

PECVD↗

DOE OSTI2026

Evaluating Protection System Performance for a Real-World Weak Grid Area With High Inverter-Based Resources

This paper evaluates an existing protection scheme implemented in a real-world weak grid area with a high penetration of inverter-based resources (IBRs). The study aims to assess the reliability and adequacy of protection schemes originally designed for traditional synchronous machine systems and determine whether they can continue to operate reliably in systems with high levels of IBRs. Hardware relays are tested using a controller-hardware-in-the-loop setup. PSCAD electromagnetic transient simulation with an IBR original equipment manufacturer black-box model is used to perform fault studies and generate COMTRADE data, which are replayed by a realtime digital simulator (RTDS) to feed input to the hardware relays. Three scenarios are analyzed: normal operation, an N-1 contingency, and an IBR-only scenario. The evaluation results reveal the following: 1) the protection scheme remains reliable under normal conditions and N-1 contingencies and 2) in IBRonly scenarios, differential protection (87L) continues to operate reliably, whereas local protection elements, such as distance and directional elements, fail because of the lack of regulated negative sequence current contributed by IBRs. These findings provide utilities with valuable insights for improving their protection systems in high-IBRs.

24 POWER TRANSMISSION AND DISTRIBUTION↗