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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 163 records · Page 9
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Real-time wall conditioning and recycling modification utilizing boron and boron nitride powder injections into the Large Helical Device

Controlled particulate injections from the PPPL impurity powder dropper (IPD) into the Large Helical Device have demonstrated positive effects on the wall conditions on both an intra and inter-shot basis. Here injections over a range of densities, input powers, pulse lengths, heating schemes, injection quantities and main ion species show conclusive evidence of improvement to plasma wall conditions. Successful injections are confirmed by both spectroscopic measurements as well as real-time visible camera signals. In 7 s long plasmas the responses include a reduction in wall recycling as well as a reduction in native impurity content as observed over the course of several discharges. For plasmas longer than 40 s, improvements to the recycling rate and increased impurity control are observed in real time as a consequence of the extended particulate injections. These experiments demonstrate the extended applicability of this solid particulate conditioning technique to the control and maintenance of the plasma wall conditions. In addition they are an important initial step in the development of the real-time boronization technique as a supplement to standard conditioning scenarios.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2025

Ocelot: An Interactive, Efficient Distributed Compression-As-a-Service Platform With Optimized Data Compression Techniques

Large volumes of data generated by scientific simulations, genome sequencing, and other applications need to be moved among clusters for data collection/analysis. Data compression techniques have effectively reduced data storage and transfer costs. However, users' requirements on interactively controlling both data quality and compression ratios are non-trivial to fulfill. Here, we propose a novel Compression-as-a-Service (CaaS) platform called Ocelot with four important contributions: (1) It offers real-time visualization, interactive compression, and transfer of scientific datasets. (2) It incorporates new strategies for compressing diverse types of datasets more effectively than traditional methods. (3) It provides an effective method for estimating the compression ratio and execution time of compression tasks. (4) Experiments on multiple real-world datasets on geographically distributed computers show that Ocelot can significantly improve data transfer efficiency with a performance gain of more than 10x in computing clusters with relatively slow networks.

compression as a service (CaaS)↗
DOE OSTI2023

Evaluation of the economic implications of varied pressure drawdown strategies generated using a real-time, rapid predictive, multi-fidelity model for unconventional oil and gas wells

Experience has suggested that pressure maintenance in hydraulically fractured reservoirs via lower, more sustained production drawdowns may offer improved cumulative recovery and overall resource extraction efficiency compared to more rapid drawdown approaches aimed at generating high initial production. However, given the inherent variability of oil and natural gas markets, operators pursue production strategies that maximize profitability over resource extraction efficiency. This study focuses on evaluating the implications of contrasting pressure drawdown strategies on the long-term production and resulting economics for a real, producing unconventional gas well in the Marcellus Shale of the Appalachian Basin using a techno-economic analysis approach. Our research combines elements of well-specific horizontal well design, production forecasting, equipment sizing and capital cost estimation, operating cost estimation, and revenue and tax calculations. Gas production forecast outlook scenarios were generated under varying pressure drawdowns using two approaches: 1) a novel physics-informed machine learning workflow and 2) traditional reservoir simulation. A discounted cash flow model was used to evaluate the resulting economic implications for each drawdown scenario—generating output for exploring the coupled effect of factors like the timing and volume of gas production, prevailing economic and market conditions for natural gas, and overall estimated ultimate recovery on profitability metrics such as internal rate of return and net present value. Results show that there is potential to maximize the cumulative gas produced in the specific case study well by employing a lower pressure drawdown. Conversely, the greatest profitability is achieved using rapid drawdown as signified by a small, specific subset of our outlook scenarios. On an averaging basis, we find that the combinations of highest cumulative producing and most profitable scenarios occur under lower drawdowns with long (>40 years) producing timeframes, but require higher relative gas price and lower discounting considerations. Further, the machine learning predictive outlooking capability proved effective for enabling rapid generation of a multitude of scenario forecasts. As a result, a variety of prominent example cases could be generated to strike the balance of greater productivity and economic return given their associated producing features and economic conditions when compared to similar producing scenarios—critical insight that offers improved decision support for unconventional oil and gas operations.

42 ENGINEERING↗
DOE OSTI2021

A Framework for International Collaboration on ITER Using Large-Scale Data Transfer to Enable Near-Real-Time Analysis

The global nature of the ITER project along with its projected ~ petabyte per day data generation presents a unique challenge, but also an opportunity for the fusion community to rethink, optimize, and enhance our scientific discovery process. Recognizing this, collaborative research with computational scientists was undertaken over the past several years to create a framework for large-scale data movement across wide-area networks (WANs), to enable global near-real time analysis of fusion data. This would broaden the available computational resources for analysis/simulation, and increase the number of researchers actively participating in experiments. An official demonstration of this framework for fast, large data transfer and real-time analysis was carried out between the KSTAR tokamak in Daejeon, Korea and PPPL in Princeton, USA. Streaming large data transfer, with near real-time movie creation and analysis of the KSTAR Electron Cyclotron Emission imaging (ECEI) data, was performed using the I/O framework ADIOS, and comparisons made at PPPL with simulation results from the XGC1 code. These demonstrations were made possible utilizing an optimized network configuration at PPPL, which achieved over 8.8 Gbps (88% utilization) in throughput tests from NFRI to PPPL. This demonstration showed the feasibility for large-scale data analysis of KSTAR data, and provides a nascent framework to enable use of globally distributed computational and personnel resources in pursuit of scientific knowledge from the ITER experiment.

43 PARTICLE ACCELERATORS↗
DOE OSTI2021

Report on High Energy Arcing Fault Experiments

This report documents an experimental program designed to investigate High Energy Arcing Fault (HEAF) phenomena for medium voltage electrical switchgear containing aluminum conductors. This report covers full-scale laboratory experiments using representative nuclear power plant (NPP) three-phase electrical equipment. Electrical, thermal, and pressure data were recorded for each experiment and documented in this report. This report covers four of the fourteen planned medium voltage electrical enclosure experiments. Subsequent reports will document the additional experiments performed in the future. The experiments were performed at KEMA Labs located in Chalfont, Pennsylvania. The experimental design, setup, and execution were completed by staff from the United States Nuclear Regulatory Commission (NRC), the National Institute of Standards and Technology (NIST), Sandia National Laboratories (SNL) and KEMA. In addition, representatives from the Electric Power Research Institute (EPRI) observed some of the experimental setups and execution. The HEAF experiments were performed on four near-identical units of General Electric metal-clad medium voltage switchgear. The three-phase arcing fault was initiated on the primary cable connection bus. All four experiments used the same system voltage (6.9 kV) but varied the current and duration. Real-time electrical operating conditions, including voltage, current and frequency, were measured during the experiments. Heat fluxes and incident energies were measured with plate thermometers and slug calorimeters at various locations around the electrical enclosures. Internal enclosure pressures were measured during the experiments. The experiments were documented with normal and high-speed videography, infrared imaging, and photography. Insights from the experimental series included timing information related to enclosure breach, event progression, mass loss measurements for electrodes and steel enclosures, peak pressure rise, particle analysis, along with visual and thermal imaging data to better understand and characterize the hazard. These results will be used in subsequent efforts to advance the state of knowledge related to HEAF.

42 ENGINEERING↗
DOE OSTI2020

X-ray Phase Contrast Imaging of the Impact of Multiple HMX Particles in a Polymeric Matrix

The initiation of high explosives (HEs) under shock loading lacks a comprehensive understanding: particularly at the particle scale. One common explanation is the hot spot theory, which suggests that energy in the material resulting from the impact event is localized in a small area causing an increase in temperature that can lead to ignition. This study focuses on the response of HMX particles (a common HE) within a polymer matrix (Sylgard-184 (R)), a simplified example of a polymer-bound explosive (PBX). These PBXs consist of multiple HMX particles in a single polymer-bound sample. A light gas gun was used to load the samples at impact velocities above 400 m/s. The impact events were visualized using X-ray phase-contrast imaging (PCI) allowing real-time observation of the impact event. The experiment used two different types of samples (multi-particle and two crystals) and found evidence of cracking and debonding in both sample types. Additionally, it was found that the multiple particle samples showed similar evidence of damage at lower velocities than that of single particle samples. This is an expected result as the multiple particles add additional interfaces for stress concentration and frictional heating.

42 ENGINEERING↗
DOE OSTI2024

A hardware-in-the-loop (HIL) testbed for cyber-physical energy systems in smart commercial buildings

In recent years, there has been a growing trend toward the development of smart buildings that rely on cyber-physical systems (CPS) to optimize occupant comfort, safety, and energy efficiency. To ensure the reliable and efficient operation of CPS with designed control strategies, it is important to evaluate their performance under various scenarios before deploying them in the real world. This is where a Hardware-in-the-loop (HIL) testbed designed for studying sensor and control-related studies in smart buildings can be highly valuable. With the growing threat of cyber-attacks and physical faults targeting smart buildings, it is essential to ensure the security of building operations. A HIL testbed can emulate cyber-attack and physical fault scenarios, allowing researchers to develop and test threat detection and mitigation algorithms. This enables researchers to identify potential issues and optimize the algorithms in a safe and controlled environment before they are deployed in real-world settings, reducing the risk of failures that can negatively impact occupant comfort, safety, and energy efficiency. Therefore, this paper developed a HIL testbed designed for cyber-physical energy systems (e.g. buildings automation system (BAS)) in smart commercial buildings. The HIL testbed is comprised of a real-time building and Heating, Ventilation, and Air-Conditioning (HVAC) emulator using Modelica-based dynamic models, a set of BAS controllers, and a BAS computer server. The data generation capability of the HIL testbed is demonstrated by tracking normal and faulty operating data in the BAS, as well as monitoring detailed network traffic in the local BAS network. Here, this study further demonstrates the HIL testbed’s capability by conducting case studies on real-time physical fault and cyber-attack experiments using a Department of Energy (DOE) prototype commercial building. It is anticipated that the fully functional HIL testbed will be utilized for a variety of sensor and control-related studies, including but not limited to testing, developing, validating of different HVAC control strategies, fault detection & diagnosis, energy monitoring and analysis, cyber security study, etc.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2024

Real-time charged track reconstruction for CLAS12

Abstract This paper presents the results of charged particle track reconstruction in CLAS12 using artificial intelligence. In our approach, we use machine learning algorithms to reconstruct tracks, including their momentum and direction, with high accuracy from raw hits of the CLAS12 drift chambers. The reconstruction is performed in real-time, with the rate of data acquisition, and allows for the identification of event topologies in real-time. This approach revolutionizes the Nuclear Physics experiments' data processing, allowing us to identify and categorize the experimental data on the fly, and will lead to a significant reduction in experiment data processing. It can also be used in streaming readout applications leading to more efficient data acquisition and post-processing.

Instruments & Instrumentation↗
DOE OSTI2025

AI-Optimized Polarization at Jefferson Lab

The AI-Optimized Polarization project seeks to develop experimental control applications for polarized targets and beams at Jefferson Lab using AI/ML. This paper will focus on two ongoing efforts involving a cryogenic polarized target and a linearly-polarized photon beam. Firstly, cryogenic targets, such as those used in Halls B and C (and approved for Hall D), are complex systems that are sensitive to a number of factors, including the temperature, beam currents, and the microwave and NMR apparatus. Secondly, the Hall D photon beam polarization depends on the optimal orientation of a diamond radiator, which produces coherent bremsstrahlung radiation from the electron beam incident upon it. Manual operation of both systems is tedious and error prone; implementing well-designed, interpretable control systems that incorporate AI is expected to lead to improved real-time polarization. AI optimization of nuclear physics experiments will lead, not just to cost-savings, but also to more efficient and higher-quality data, and this project will help to lay the foundation for future autonomous experiments.

Moran, Patrick [College of William and Mary, Willi↗
DOE OSTI2023

Photodissociation of H 2 on Ag and Au Nanoparticles: Effect of Size and Plasmon versus Interband Transitions on Threshold Intensities for Dissociation

This Article provides new insights concerning the simulation of plasmon-driven chemical reactions using real-time TDDFT based on the tight-binding electronic structure code DFTB+, with applications to the dissociation of H 2 on octahedral silver and gold nanoparticles with 19–489 atoms. A new component of these calculations involves sampling a 300 K canonical ensemble to determine the distribution of possible outcomes of the calculations, and with this approach we are able to determine the threshold for dissociation as a function of laser intensity, wavelength, and nanocluster size. We show that the threshold intensity varies as an inverse power of nanocluster size, which makes it possible to extrapolate the results to sizes that are more typical of experimental studies. The intensities obtained from this extrapolation are around a factor of 100 above powers used in the pulsed experiments. This is a closer comparison of theory and experiment than has been obtained in previous real-time simulations, and the remaining discrepancy can be understood in terms of electromagnetic hot spots that are associated with cluster formation. Here, we also compare the influence of plasmon excitation versus interband excitation on reaction thresholds, revealing that for silver clusters plasmon excitation leads to lower thresholds, but for gold clusters interband excitation is more effective. Our study also includes an analysis of charge transfer to and from the H 2 molecule, and a determination of orbital populations during and after the pulse, showing the correlation between metal excitations and the location of the antibonding level of H 2 .

77 NANOSCIENCE AND NANOTECHNOLOGY↗
DOE OSTI2025

Modular Autonomous Experimentation for Biological Applications (Full Report)

The Modular Autonomous Research System (MARS) was developed to address the pressing need for faster, more reliable, and more adaptable scientific discovery. Traditional experimentation is limited by manual labor, long cycle times, and fragmented data streams, which constrain the ability to explore complex chemical and materials design spaces. To overcome these limitations, we created an integrated, modular platform that combines laboratory robotics, diverse measurement instruments, and a central data infrastructure with artificial intelligence–driven decision-making. The system links liquid handling robots, robotic arms, and optical plate readers into a closed loop where experiments are executed automatically, data is analyzed in real time, and subsequent experimental conditions are adaptively chosen to maximize information gain. Over the course of the project, MARS was validated on two primary test cases—spectroscopic metal–ligand binding assays and peptide-directed mineralization—which highlighted the system’s ability to handle uncertainty and variability in experimental measurements. To further demonstrate modularity and extensibility, we also established additional testbeds in electrochemistry for catalyst discovery and electrolyte formulation for advanced batteries. The results show that MARS can reliably conduct autonomous campaigns with minimal human intervention, adapt to distinct scientific domains, and provide a scalable model for future self-driving laboratories. This work establishes new capabilities for modular, uncertainty-aware automation and directly supports the need for advanced, data-driven research platforms capable of accelerating discovery across a wide range of scientific and national security missions.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2026

Exploring AI/ML-based Real-time Anomaly Detection in DUNE for Supernova Burst Neutrinos

The Deep Underground Neutrino Experiment (DUNE) is currently under construction with far detectors consisting of 4 liquid argon time projection chamber (LArTPC) modules at SURF (South Dakota Underground Research Facility) and a near detector complex with neutrino beam production at Fermilab to unambiguously determine neutrino mass ordering, to discover and precisely measure Charge-Parity (CP) violation phase in leptonic sector, to search for Beyond Stand Model (BSM) physics, and to study solar and supernova burst neutrinos. Anomalies in this project are classified in three categories: new physics signals, supernova burst neutrinos, and detector malfunction. We report here on promising early studies toward an Artificial Intelligence/Machine Learning-based real-time anomaly detection system, using a prototype autoencoder model currently under development. Additionally, the current status of an improved model and its performance will be presented. The model will be evaluated not only for its sensitivity to supernova neutrinos, but also to BSM physics signals and detector malfunctions. We will also consider how such a real-time algorithm might be used in DUNE.

de Jonge, Anselm [Kirchhoff Inst. Phys.] (ORCID:00↗
DOE OSTI2024

Temperature-induced degradation of GaN HEMT: An in situ heating study

High-power electronics, such as GaN high electron mobility transistors (HEMTs), are expected to perform reliably in high-temperature conditions. This study aims to gain an understanding of the microscopic origin of both material and device vulnerabilities to high temperatures by real-time monitoring of the onset of structural degradation under varying temperature conditions. This is achieved by operating GaN HEMT devices in situ inside a transmission electron microscope (TEM). Electron-transparent specimens are prepared from a bulk device and heated up to 800 °C. High-resolution TEM (HRTEM), scanning TEM (STEM), energy-dispersive x-ray spectroscopy (EDS), and geometric phase analysis (GPA) are performed to evaluate crystal quality, material diffusion, and strain propagation in the sample before and after heating. Gate contact area reduction is visible from 470 °C accompanied by Ni/Au intermixing near the gate/AlGaN interface. Elevated temperatures induce significant out-of-plane lattice expansion at the SiNx/GaN/AlGaN interface, as revealed by geometry-phase GPA strain maps, while in-plane strains remain relatively consistent. Exposure to temperatures exceeding 500 °C leads to almost two orders of magnitude increase in leakage current in bulk devices in this study, which complements the results from our TEM experiment. The findings of this study offer real-time visual insights into identifying the initial location of degradation and highlight the impact of temperature on the bulk device’s structure, electrical properties, and material degradation.

36 MATERIALS SCIENCE↗
DOE OSTI2020

High Temperature Irradiation Resistant Thermocouples

The High Temperature Irradiation Resistant Thermocouple (HTIR-TC) is the world’s leading temperature sensor for reactor fuel experiments and material test reactors (MTR), in general (e.g. Advanced Test Reactor, Massachusetts Institute of Technology Nuclear Reactor, etc.). Recently commercialized, the HTIR-TC provides real-time centerline and fuel cladding temperature measurements of reactor fuel experiments during irradiations. This provides direct information about fuel temperatures during Gen IV reactor, small modular reactor (SMR), and microreactor vetting processes. Further, the HTIR-TC recently won the 2019 R&D100 award under category: Analytical/Test.

46 - INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AN↗
DOE OSTI2023

Workflows Community Summit 2022: A Roadmap Revolution

Scientific workflows have become integral tools in broad scientific computing use cases. Science discovery is increasingly dependent on workflows to orchestrate large and complex scientific experiments that range from the execution of a cloud-based data preprocessing pipeline to multi-facility instrument-to-edge-to-HPC computational workflows. Given the changing landscape of scientific computing (often referred to as a computing continuum) and the evolving needs of emerging scientific applications, it is paramount that the development of novel scientific workflows and system functionalities seek to increase the efficiency, resilience, and pervasiveness of existing systems and applications. Specifically, the proliferation of machine learning/artificial intelligence (ML/AI) workflows, need for processing large-scale datasets produced by instruments at the edge, intensification of near real-time data processing, support for long-term experiment campaigns, and emergence of quantum computing as an adjunct to HPC, have significantly changed the functional and operational requirements of workflow systems. Workflow systems now need to, for example, support data streams from the edge-to-cloud-to-HPC, enable the management of many small-sized files, allow data reduction while ensuring high accuracy, orchestrate distributed services (workflows, instruments, data movement, provenance, publication, etc.) across computing and user facilities, among others. Further, to accelerate science, it is also necessary that these systems implement specifications/standards and APIs for seamless (horizontal and vertical) integration between systems and applications, as well as enable the publication of workflows and their associated products according to the FAIR principles.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

MSASGCN : Multi-Head Self-Attention Spatiotemporal Graph Convolutional Network for Traffic Flow Forecasting

Traffic flow forecasting is an essential task of an intelligent transportation system (ITS), closely related to intelligent transportation management and resource scheduling. Dynamic spatial-temporal dependencies in traffic data make traffic flow forecasting to be a challenging task. Most existing research cannot model dynamic spatial and temporal correlations to achieve well-forecasting performance. The multi-head self-attention mechanism is a valuable method to capture dynamic spatial-temporal correlations, and combining it with graph convolutional networks is a promising solution. Therefore, we propose a multi-head self-attention spatiotemporal graph convolutional network (MSASGCN) model. It can effectively capture local correlations and potential global correlations of spatial structures, can handle dynamic evolution of the road network, and, in the time dimension, can effectively capture dynamic temporal correlations. Experiments on two real datasets verify the stability of our proposed model, obtaining a better prediction performance than the baseline algorithms. The correlation metrics get significantly reduced compared with traditional time series prediction methods and deep learning methods without using graph neural networks, according to MAE and RMSE results. Compared with advanced traffic flow forecasting methods, our model also has a performance improvement and a more stable prediction performance. We also discuss some problems and challenges in traffic forecasting.

Cao, Yang↗
DOE OSTI2025

Real-time steerable frequency-stepped Doppler backscattering (DBS) system for local helicon wave electric field measurements on the DIII-D tokamak

A new frequency-stepped Doppler backscattering (DBS) system has been integrated into a real-time steerable electron cyclotron heating launcher system to simultaneously probe local background turbulence (f < 10 MHz) and high-frequency (20–550 MHz) density fluctuations in the DIII-D tokamak. The launcher allows for 2D steering (horizontally and vertically) over wide angular ranges to optimize probe location and wavenumber response. The vertical steering can be optimized during a discharge in real time. The new DBS system employs a programmable frequency synthesizer with adjustable dwell time as a source to launch either O or X-mode polarized millimeter waves. This system can step in real-time over the entire E-band frequency range (60–90 GHz). This combination of capabilities allows for the diagnosis of the complex internal spatial structure of high power (>200 kW) helicon waves (476 MHz) injected from an external antenna during helicon current drive experiments in DIII-D. Broadband density fluctuations around the helicon frequency are observed during real-time scans of measurement location and wavenumber during these experiments. Analysis indicates that these broadband high-frequency fluctuations are a result of backscattering of the DBS millimeter-wave probe beam from plasma turbulence modulated by the helicon wave. It is observed that background turbulence is effectively locally “tagged” with the helicon wave electric field, forming images of the turbulent spectrum in the overall density fluctuation spectrum that appear as high-frequency sidebands of the turbulence. These observations of background turbulence and high-frequency fluctuations open up the possibility of monitoring local helicon wave amplitude by comparing the high-frequency signal amplitude to the simultaneously measured background turbulence. In combination with the real-time measurement location and wavenumber scanning capabilities (offered by real-time frequency-stepping and steering), this allows rapid determination of the spatial distribution of the helicon wave power during steady-state plasma operation.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗