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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 127 records · Page 7
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Visible Light Communications: A Survey on Recent High-Capacity Demonstrations and Digital Modulation Techniques

In order to deal with the increasing number of mobile devices and with their demand for Internet services, particularly social media platforms, streaming video, and online gaming, Radio-Frequency (RF) wireless networks have been pushed to their capacity limits. In addition to this, 80% of the total data traffic is carried out by users inside buildings. Therefore, new technologies have started to be considered for indoor wireless communications. Visible Light Communications (VLC) can provide both illumination and communications, appearing as an alternative or complement to RF wireless networks. VLC offers high bandwidth and immunity to interference from electromagnetic sources. This manuscript reviews recent high-capacity VLC demonstrations. The main focus of this work is to present digital-signal-processing techniques used in VLC systems. Different modulation formats are analyzed, which can be divided into two large groups, namely single-carrier and multi-carrier modulation schemes. Finally, some recently proposed capacity-achieving strategies are presented. We discuss how to implement these techniques and how they will be useful for the continued development of VLC systems.

Loureiro, Pedro A. (ORCID:0000000283546185)↗
DOE OSTI2026

Data Management for Digital Twin Implementation: Enhancing Existing SCADA Architecture for INL's CITRC Test Distribution System

The United States power grid will continue to experience stress from aging infrastructure, extreme weather, and increasing loads. In order to strengthen resiliency, researchers need to run tests and simulations that model real-life infrastructure. Idaho National Laboratory (INL) is creating a digital twin of its Critical Infrastructure Test Range Complex (CITRC). CITRC is an at-scale grid testbed which can be configured in utility-realistic distribution scenarios for a variety of tests, such as advanced grid protection and control. A digital twin of this infrastructure would support in-depth tests/simulations without risking physical consequences, before trialing devices under test at-scale. This project focuses on the data acquisition part of constructing a digital twin. It investigates the Supervisory Data Acquisition and Control (SCADA) system of CITRC and explores ways to utilize this system for the twin. Future “ideal” at-scale implementations such as upgraded equipment, modern communication protocols, and automation applications are also explored.

24 - POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2021

Parametrized Hamiltonian simulation using quantum optimal control

Analog quantum simulation offers a hardware-specific approach to studying quantum dynamics, but mapping a model Hamiltonian onto the available device parameters requires matching the hardware dynamics. In this work, we introduce a paradigm for quantum Hamiltonian simulation that leverages digital decomposition techniques and optimal control to perform analog simulation. We validate this approach by constructing the optimal analog controls for a superconducting transmon device to emulate the dynamics of an extended Bose-Hubbard model. We demonstrate the role of control time, digital error, and pulse complexity, and we explore the accuracy and robustness of these controls. We conclude by discussing the opportunity for implementing this paradigm in near-term quantum devices.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2024

Deep Koopman Neural Network for Analyzing High-Energy-Density Simulations of Electrical Wire Explosions

Megaampere-scale electrical wire experiments (EWEs) provide a platform for studying magnetohydrodynamic (MHD) instability growth in magneto-inertial fusion (MIF) devices. Even when nonlinear simulations of these experiments can digitally reproduce much of the experimentally observed instability growth, interpreting the results and understanding mode growth and evolution can be non-trivial. As a first step toward providing better interpretation of these simulation features, this work investigates the use of a deep neural network that uses Koopman operator theory to analyze the dynamics of pulsed-power-driven explosions of EWEs. This deep neural network is trained on 1-D resistive MHD simulations of EWEs. This neural network learns to transform the nonlinear data into a lower-dimensional representation where the time dynamics are linear. Layers of this neural network are shown to learn features of the simulations, including the locations of shock waves and different physical regimes of the simulation. Using the learned features, the network can compress a time state of the simulation consisting of 5120 data point into a 36-parameter lower-dimensional latent space embedding. Furthermore, these embeddings are shown to be clustered in the latent space by initial radius and time state.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2020

Enterprise Credentialing Service Statement of Work (SOW)

The scope of this project includes providing professional services in support of the development and deployment of an Enterprise Credentialing Service for the Department of Energy (DOE) National Nuclear Security Administration (NNSA), which will be used for issuing and managing credentials used for authentication, digital signature, and encryption functions by users, applications, and devices in a closed computing environment. The Seller shall provide subject matter expertise for the design and deployment of the technologies and processes that comprise the Enterprise Credentialing Service, which will include a public key infrastructure (PKI) and a credential management system (CMS) that issues and manages PKI-based smartcard credentials. The Enterprise Credentialing Service will support approximately 17,000 users at 15 DOE NNSA sites and laboratories located across the continental United States.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Dipolar molecule emulator of lattice gauge theories (Final Report)

The quantum many-body problem is a great unsolved, cross-cutting challenge in physics that is of fundamental importance. Our understanding of phenomena related to dense quark matter, in particular, is challenged by this practical intractability of classical simulations. Because of the sheer cost and challenge of performing experiments that probe the length and energy scales relevant to such physics, there are practical motivations for finding theoretical methods to address the many-body problem. One promising approach is based on the use of a programmable and controllable analog quantum systems to emulate the physics of many-body problems of interest. While there are currently broad efforts to develop mid- to large-scale quantum computers, we are still likely many years away from such devices outperforming classical supercomputers for useful calculations. Even though digital quantum computers are still at a stage too premature for such tasks, approaches based on analog quantum simulators have advanced rapidly over the past two decades and can now treat many-body problems of interest on small- to medium-scale systems. Tackling the challenging many-body problems of relevance to high energy physics represents one of the great new opportunities and challenges for the field of analog quantum simulation. To address this challenge, this award brought together an interdisciplinary team of physicists with expertise in high energy theory (El-Khadra and Draper), computational and condensed matter physics (Clark), and experimental quantum and atomic, molecular, optical (AMO) science (DeMarco and Gadway), with the goal of developing realistic and optimized strategies for the analog quantum simulation of lattice gauge theories using cold atoms and molecules. This track 1 project included both theoretical and experimental goals, stated as follows. The theory goals were to "develop and verify a novel approach to quantum emulation of (1+1)d quantum link models, based on arrays of trapped quantum spins with long-range interactions." This final project report details the progress made under this award, as well as new research directions developed under this award.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2021

Control-Theory-Informed Feature Selection for Detecting Malicious Tampering in Additive Layer Manufacturing Processes

Additive layer manufacturing (ALM) is rapidly becoming an appealing solution to the low-volume manufacturing of metal, polymer, or composite parts. However, ALM’s reliance on digital part specifications, microcontrollers, and modern networking makes these devices vulnerable to malicious tampering by cyber attackers, which can negatively affect part performance and even result in catastrophic failure. We present a hybrid analytic approach to feature discovery using control theoretic techniques and linear modelling on input-output data collected from a representative controller system. Employing this approach, we design, train, and test an anomaly detection system. The preliminary results show that the proposed approach effectively discovers useful input-output relationships for anomaly detection in a simulated ALM process. Application to larger and more complex systems are discussed.

Dawson, Joel↗
DOE OSTI2026

Maximizing dynamic range and performance of anatase TiO 2 ECRAM through structure and programming

Here, in this study, we investigate the structure-dependent modulation characteristics of all-solid-state three-terminal electrochemical random-access memory (ECRAM) based on an anatase Li x TiO 2 channel. By directly comparing “asymmetric” and “symmetric” ECRAM device architectures, we reveal significant insight into the impact of a non-zero gate-drain open-circuit voltage and its influence on voltage vs. current-controlled gating. We also explore the impact of potentiation/depression write parameters on the symmetry, linearity, and dynamic range of the device response. Together, initial results from optimizing structure and programming approaches yielded unprecedented G max /G min ratios of >1,000 for ECRAM and hundreds of tunable memory states with excellent linearity and symmetry. Simulations based on these ECRAM devices further illustrate the promise of this analog memory technology, achieving near 2% classification error in the MNIST digit recognition benchmark for a range of training parameters compared to a theoretical best of 1.66% and outperforming other device models extracted from the literature.

AIHWKit↗
DOE OSTI2024

Advanced Data Science Model for Detecting Intelligent Malware

This study focused on developing a robust artificial intelligence (AI) model capable of detecting and characterizing advanced malware in Internet of Things (IoT) devices using network data. By analyzing network traffic with various machine learning (ML) models, our AI model can identify and characterize malicious activities to significantly improve malware detection accuracy and reliability as compared to traditional methods. The developed AI/ML model was trained using network data from IoT devices, leveraging classifiers such as Random Forest, Gradient Boosting, AdaBoost, and others to optimize detection performance. This project demonstrates a scalable framework for real-time malware detection and characterization in IoT networks, capable of identifying infected devices and facilitating the necessary steps to remove or isolate them, thereby preventing further infections. Although digital twin (DT) integration is not yet implemented in the current model, it represents a promising future enhancement. By creating a virtual replica of physical IoT devices, DT technology would allow for real-time monitoring and analysis without directly accessing operational technology, thus reducing the risk of compromising or reducing the performance of actual devices. This integration would further enhance the security of IoT ecosystems, combining AI technology to better flag and detect indications of malware-infected devices within a nuclear system environment.

42 ENGINEERING↗
DOE OSTI2025

A Digital Twin of Scalable Quantum Clouds

Quantum computing has emerged as a transformative technology capable of solving complex problems beyond the limit of classical systems. The rapid development of quantum processors has led to the proliferation of cloud-based quantum computing services offered by platforms such as IBM, Google, and Amazon. These platforms introduce unique challenges in resource allocation, job scheduling, and multi-device orchestration as quantum workloads become increasingly complex. In this work, we present a digital twin of quantum cloud infrastructures: a framework designed to model and simulate the behavior of real quantum cloud systems. Developed in Python using the SimPy discrete-event simulation library, the framework replicates key aspects of quantum cloud environments, including detailed quantum device modeling, job lifecycle management, and job fidelity. It incorporates noise-aware fidelity estimation, making it the first of its kind to simulate superconducting gate-based quantum cloud systems at an administrative level with job fidelity. We present use cases as proof of concept, demonstrating that our quantum cloud simulation framework can act as a digital twin of a quantum cloud and support the modeling and implementation of practical systems.

Luo, Waylon [Kent State University]↗
DOE OSTI2025

MPEX AI Digital Twins

All magnetically confined plasma fusion power plant concepts (Tokamak, Spherical Tokamak, Stellarator, Mirror, ...) must exhaust the heat and plasma from the core confinement region to the material walls. The primary channel for this exhaust is through a plasma divertor which directs plasma along open magnetic field lines to a material target. The Material Plasma Exposure eXperiment (MPEX) illustrated in Figure 1, is a high-power, steady-state linear plasma device designed to produce the plasma material interaction (PMI) conditions of the divertor of future magnetic confinement fusion power plants: energy flux 20MW/m 2 , ion fluence 1031/m 2 , pulse duration 106 sec. These goals of plasma exposure in MPEX are well beyond those achieved in magnetic fusion experimental devices. Successfully achieving these high power steady state conditions for long pulses requires operational control of the heating and particle sources and the plasma flux to the walls and target. The MPEX AI Hot Spot Controller, proposed in this project, will help achieve the operational milestones of MPEX. The MPEX device will begin commissioning at the end of FY26. A smaller proto-MPEX was operated for 14,666 plasma discharges and will resume operation in September of 2025 as proto-MPEX-lite, with reduced capability, to test a new window for the Helicon plasma source. The proto-MPEX data has undergone surrogate modeling with machine learning methods (R. Archibald, 2022 IEEE International Conference on Big Data). This proto-MPEX data will be used to begin development of the AI digital twins described in this white paper. The scientific mission of MPEX is to qualify materials of different composition for use in the high energy and plasma flux conditions of a fusion power plant. The materials exposed in MPEX will in some cases be exposed to high neutron fluxes at other ORNL facilities to measure the changes to their PMI properties. The targets exposed in MPEX will be transported under vacuum to a Surface Analysis Station (SAS). The SAS will be equipped with the following diagnostics: Focused Ion Beam (FIB) for trench milling, 100-400 angstrom resolution scanning electron microscope (SEM), surface mapping x-ray spectrometer, high resolution camera, and a future upgrade to a laser induced breakdown spectroscopy quadruple mass spectrometer (LIBS-QMS). The MPEX experiments will generate diverse pre- and post-exposure measurement data of detailed material properties down to the crystal grain level in 3D for post-exposure assessment of PMI damage (e.g. cracking, melting, erosion and redeposition of the material). Physics models for the PMI, and how the material composition and manufacturing impact its performance under high energy plasma exposure, need to be validated with MPEX data to guide the selection of new candidate materials. Our vision for the MPEX AI Digital Twins project is to supply experimental and physics model simulation data to train Artificial Intelligence (AI) models for data processing, analysis, operational control, PMI and materials simulation to maximize the scientific output of the MPEX device. Ultimately, an AI digital twin of MPEX material assessment metrics for tested and synthetic material types with simulated PMI will be trained by the AI Modeling Teams on the experimental and physics simulation data submitted to the American Science Cloud by this project. A purely empirical search for the best material is inefficient given the finite number of samples that can be tested on MPEX. In order to expand the material properties database for training the MPEX Material Assessment AI Digital Twin, and to gain physics understanding of the PMI processes, physics models of the material properties and PMI processes are required. The physics simulations provide detailed simulation data, like impact angles for plasma ions, sputtering yields, transport of the ionized sputtered target material in the plasma, and redeposition locations. This simulation data expands the measurement data for deeper physics understanding. The experimental data is essential to validate the PMI and material structure simulation models. The validated models can then be used to generate new simulation data of MPEX material assessments for synthetic material compositions that have not been exposed in MPEX. These predictive simulations, plus the whole experimental dataset, will be used to train the MPEX Material Assessment AI Digital Twin allowing a rapid generative AI search for new materials with reduced PMI damage by interpolating the domain of the training set. These new optimum materials can be simulated with the physics codes and/or tested in MPEX. The ability of AI neural networks to interpolate multi-dimensional parameter spaces and generate virtual data is exploited for a more efficient search for optimum materials. The advent of the Transformational AI Models Consortium (TAIMC) is an opportunity to engage with state of the art private and public AI developers to achieve the goals of the AI digital twins and AI accelerated physics models proposed in this project. Our partners at ORNL from the Advance Scientific Computing Research (ASCR) organization will collaborate in accelerating the integrated plasma material interaction simulation framework. This simulation framework will provide a platform for generating simulation data across a range of physical fidelities, including hybrid methods that produce multi-fidelity results. This data will be leveraged for AI model development, both for generation of surrogates and the automation of simulation campaigns. A part of the research below will include collaborative efforts with the TAIMC to (i) adapt data storage approaches to ensure AI-readiness, (ii) provide a protypical exemplar to inform and exercise constructed workflows, and (iii) generate and share data, using the TAIMC unified AI data standard, for foundational models that will be trained from multiple sources across the DOE complex. We will also collaborate with the TAIMC, as well as the planned AI modeling teams, to develop approaches for reducing the cost of data generation. These include tailored multi-fidelity approaches as well as fine-tuning strategies to augment general, large-scale foundational models.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2024

Time-resolved synchrotron light source X-ray detection with Low-Gain Avalanche Diodes

Low Gain Avalanche Diodes (LGADs) represent the state-of-the-art in timing measurements and will instrument future timing detectors at the High Luminosity Large Hadron Collider (HL-LHC) experiments. While conceived as a sensor for charged particles, the intrinsic gain of LGADs makes it possible to detect low energy X-rays with good energy resolution and excellent timing (tens of picoseconds). Here, using the Stanford Synchrotron Radiation Lightsource (SSRL) at SLAC, several LGADs designs were characterized with energies from 5 to 35 keV. The SSRL provides 10 ps pulsed X-ray bunches separated by 2.1 ns intervals, and with an energy dispersion (ΔE/E) of 1 X 10 -4 . LGADs fabricated by Hamamatsu Photonics (HPK) and Brookhaven National Laboratory (BNL) with different thicknesses ranging from 20 µm to 50 µm and different gain layer designs were read out a two stage fast amplification circuit and digitized with a high bandwidth, high sampling rate oscilloscope. PIN devices from HPK were characterized as well. A systematic and detailed characterization of the devices’ energy linearity, resolution and timing resolution as a function of X-ray energy was performed for different biasing voltages at room temperature

Charge multiplication↗
DOE OSTI2024

Multichannel meta-imagers for accelerating machine vision

Rapid developments in machine vision technology have impacted a variety of applications, such as medical devices and autonomous driving systems. These achievements, however, typically necessitate digital neural networks with the downside of heavy computational requirements and consequent high energy consumption. As a result, real-time decision-making is hindered when computational resources are not readily accessible. Here we report a meta-imager designed to work together with a digital back end to offload computationally expensive convolution operations into high-speed, low-power optics. Further, in this architecture, metasurfaces enable both angle and polarization multiplexing to create multiple information channels that perform positively and negatively valued convolution operations in a single shot. We use our meta-imager for object classification, achieving 98.6% accuracy in handwritten digits and 88.8% accuracy in fashion images. Owing to its compactness, high speed and low power consumption, our approach could find a wide range of applications in artificial intelligence and machine vision applications.

47 OTHER INSTRUMENTATION↗
DOE OSTI2023

A superconducting nanowire binary shift register

We present a design for a superconducting nanowire binary shift register, which stores digital states in the form of circulating supercurrents in high-kinetic-inductance loops. Adjacent superconducting loops are connected with nanocryotrons, three-terminal electrothermal switches, and fed with an alternating two-phase clock to synchronously transfer the digital state between the loops. A two-loop serial-input shift register was fabricated with thin-film NbN and a bit error rate of less than 10 –4 was achieved, when operated at a maximum clock frequency of 83 MHz and in an out-of-plane magnetic field of up to 6 mT. A shift register based on this technology offers an integrated solution for low-power readout of superconducting nanowire single photon detector arrays and is capable of interfacing directly with room-temperature electronics and operating unshielded in high magnetic field environments.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2026

Virtual Power Plant Architecture and Resilient Design

Virtual Power Plants (VPPs) represent a fundamental shift in electric grid operations, aggregating distributed energy resources (DERs) such as solar panels and battery storage to deliver utility-scale grid services traditionally provided by centralized power plants. This report examines the unique architectural, operational, and digital assurance considerations that distinguish VPPs from conventional utility infrastructure as they scale from pilot projects to mainstream deployment across the United States. While VPPs offer significant opportunities for grid modernization and enhanced flexibility, their distributed, multi-stakeholder architecture introduces distinct security challenges that differ fundamentally from traditional generation facilities. The analysis identifies risks in VPP operations, including device-level security gaps, platform vulnerabilities, and communication protocol weaknesses that create expanded attack surfaces compared to centralized power plants. Through examination of real-world incidents and emerging threat patterns, the report demonstrates how some VPPs' reliance on consumer-owned devices, public internet infrastructure, and complex vendor ecosystems require new approaches to digital assurance and operational security. The findings provide practical guidance for utilities, regulators, and aggregators to implement robust security frameworks and operational best practices essential for maintaining grid reliability as VPP deployment accelerates under the Federal Energy Regulatory Commission (FERC) Order 2222 and related regulatory initiatives.

24 - POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2024

Digital risk analysis in nuclear engineering projects: Designing for safety, performance, reliability, and security

Cyber-informed engineering and security-by-design frameworks are important in promoting the need to identify cybersecurity concerns early in the systems engineering lifecycle so risks from adversarial cyber-attacks can be eliminated or reduced through engineering design practices. In addition to adversarial risk, risk in operational technology systems also includes non-adversarial and unintentional risk from other factors such as human performance errors, environmental conditions, design flaws, and device degradation or failure. This paper introduces a new concept for characterizing digital risk, both adversarial and non-adversarial, and provides the basis for initial research into a novel digital risk analysis approach focused on incorporating attack difficulty into a multi-attribute analysis technique using robust decision-making. This digital risk characterization is also used to frame a discussion on the challenges of competing objectives and competing stakeholder requirements in an integrated energy system project that incorporates a small modular reactor and industrial facility.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2022

Radiation-Hardened GaN-Based Wireless Communications Architectures for Terrestrial Nuclear Reactor Sensing and Instrumentation

Wireless technologies have become increasingly common in applications, ranging from close proximity inductive communication links in medical devices to short-range Bluetooth and WiFi communications and longer-range cellular communications. Nonetheless, these technologies are unsuitable for use in or around nuclear reactors due to the extreme radiation and temperatures inherent in these environments and their associated significant degradative effects on electronics hardware. However, recent work in wide bandgap– based electronics has shown promise for gallium nitride (GaN) as an emerging technology for the realization of practical wireless communications systems for use in harsh environments. This report presents initial progress on the development of wireless communications architectures designed specifically for nuclear reactor application, based on inherently radiation-hardened (rad-hard) GaN technology. Prior work on rad-hard analog communications topologies is reviewed, and several digital modulation and encoding schemes that utilize GaN-based electronics devices are newly proposed for application in reactor environments. Continuous-time and discrete-time system simulations were performed, and results are presented for the preliminary transmitter and receiver designs, respectively. In addition, an overview of a software-defined radio testbed, designed for communications protocol development, is provided. Future work will focus on implementing candidate radiation-resistant wireless architectures using a research AlGaN/GaN high electron mobility transistor (HEMT) integrated circuit process, which is available at the Ohio State University, and irradiation studies will be carried out to assess the true potential of this technology for reactor application.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2023

Feedback lock-in: A versatile multi-terminal measurement system for electrical transport devices

Here, we present the design and implementation of a measurement system that enables parallel drive and detection of small currents and voltages at numerous electrical contacts to a multi-terminal electrical device. This system, which we term a feedback lock-in, combines digital control-loop feedback with software-defined lock-in measurements to dynamically source currents and measure small, pre-amplified potentials. The effective input impedance of each current/voltage probe can be set via software, permitting any given contact to behave as an open-circuit voltage lead or as a virtually grounded current source/sink. This enables programmatic switching of measurement configurations and permits measurement of currents at multiple drain contacts without the use of current preamplifiers. Our 32-channel implementation relies on commercially available digital input/output boards, home-built voltage preamplifiers, and custom open-source software. With our feedback lock-in, we demonstrate differential measurement sensitivity comparable to a widely used commercially available lock-in amplifier and perform efficient multi-terminal electrical transport measurements on twisted bilayer graphene and SrTiO 3 quantum point contacts. The feedback lock-in also enables a new style of measurement using multiple current probes, which we demonstrate on a ballistic graphene device.

47 OTHER INSTRUMENTATION↗