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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 73 records · Page 4
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Initial Development of Fusion Magnet Simulation Capabilities for Performance and Safety Evaluation Using the MOOSE Framework

Fusion energy holds the promise of being a transformative technology as a carbon-neutral, sustainable source of energy. Whole device modeling and the development of fusion digital twins will be increasingly important for emerging fusion device concepts at both national laboratories and within the commercial fusion industry. However, meeting the challenge of whole device modeling of fusion energy devices requires robust, multiphysics, multiscale modeling and simulation technologies capable of running on large-scale supercomputers. Detailed analysis of individual systems at-scale is also required to ensure safe and efficient operation as well as provide the safety basis for future device designs and licensing activities. In a tokamak, toroidal and poloidal magnets confine and shape the fusion plasma to promote the fusion reaction. High plasma temperatures and high magnetic field requirements in modern design concepts (leading to high amounts of energy stored within each magnet) impose electrical, thermal, and mechanical loads on the magnet components, which in turn impacts the safety considerations of the magnet and their supporting systems. Idaho National Laboratory (INL) has a history of working in this space, including development and benchmarking of the Magnetic System Circuitry Analysis Program (MSCAP) and Magnet Arcing (MAGARC) codes to study magnet quench events; notably, MAGARC was used to study quenching during the ITER Engineering Design Activity. However, these legacy codes and capabilities are not parallel and scalable, and new tools are required for future advances in this area, which leads to the INL-developed Multiphysics Object-Oriented Simulation Environment (MOOSE) framework. Developed originally for fission reactor systems under United States Department of Energy, Office of Nuclear Energy modeling and simulation programs, the MOOSE framework has been well-suited to multiscale, multiphysics modeling and simulation needs for nuclear systems. The framework is open-source, well-tested, under continuous development and deployment, and developed to a Nuclear Quality Assurance, Level 1 software quality standard. MOOSE has also been used in the fusion space previously in several projects: INL’s Tritium Migration Analysis Program, Version 8 (TMAP8) for tritium migration, UK Atomic Energy Authority’s A Unified Resource for OpenMC (fusion) Reactor Applications (AURORA) code for fusion thermo-mechanical and neutronics analysis, and Argonne National Laboratory’s Cardinal for high-fidelity computational fluid dynamics and neutronics. However, to model superconducting magnets, several MOOSE enhancements are required: additions to the current MOOSE electromagnetic capabilities, new material libraries for superconductors of interest (such as YBCO), as well as fusion-specific models for thermo-mechanics. This talk will discuss initial development activities to build these capabilities in MOOSE, focusing on initial validation and benchmarking activities. Proposed coupling workflows and future work to support the simulation of fusion magnets and magnet structural assemblies for performance and safety evaluation in MOOSE will also be discussed.

70 - PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2021

Data-Driven Strategies for Accelerated Materials Design

The ongoing revolution of the natural sciences by the advent of machine learning and artificial intelligence sparked significant interest in the material science community in recent years. The intrinsically high dimensionality of the space of realizable materials makes traditional approaches ineffective for large-scale explorations. Modern data science and machine learning tools developed for increasingly complicated problems are an attractive alternative. An imminent climate catastrophe calls for a clean energy transformation by overhauling current technologies within only several years of possible action available. Tackling this crisis requires the development of new materials at an unprecedented pace and scale. For example, organic photovoltaics have the potential to replace existing silicon-based materials to a large extent and open up new fields of application. In recent years, organic light-emitting diodes have emerged as state-of-the-art technology for digital screens and portable devices and are enabling new applications with flexible displays. Reticular frameworks allow the atom-precise synthesis of nanomaterials and promise to revolutionize the field by the potential to realize multifunctional nanoparticles with applications from gas storage, gas separation, and electrochemical energy storage to nanomedicine. In the recent decade, significant advances in all these fields have been facilitated by the comprehensive application of simulation and machine learning for property prediction, property optimization, and chemical space exploration enabled by considerable advances in computing power and algorithmic efficiency. In this Account, we review the most recent contributions of our group in this thriving field of machine learning for material science. We start with a summary of the most important material classes our group has been involved in, focusing on small molecules as organic electronic materials and crystalline materials. Specifically, we highlight the data-driven approaches we employed to speed up discovery and derive material design strategies. Subsequently, our focus lies on the data-driven methodologies our group has developed and employed, elaborating on high-throughput virtual screening, inverse molecular design, Bayesian optimization, and supervised learning. We discuss the general ideas, their working principles, and their use cases with examples of successful implementations in data-driven material discovery and design efforts. Furthermore, we elaborate on potential pitfalls and remaining challenges of these methods. Finally, we provide a brief outlook for the field as we foresee increasing adaptation and implementation of large scale data-driven approaches in material discovery and design campaigns.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Can Cobalt Be Eliminated from Lithium-Ion Batteries?

Following the discovery of LiCoO 2 (LCO) as a cathode in the 1980s, layered oxides have enabled lithium-ion batteries (LIBs) to power portable electronic devices that sparked the digital revolution of the 21st century. Since then, LiNi x Mn y Co z O 2 (NMC) and LiNi x Co y Al z O 2 (NCA) have emerged as the leading cathodes for LIBs in electric vehicle (EV) application and have become crucial components in the fight against global warming. However, the surging demand for LIBs has led to an extremely tight supply. The EV sector has already dominated the LIB market, even though EV sales were only 2–3% of total passenger vehicle sales in 2020. EV sales are expected to grow 10-fold by the end of this decade, and close to 90% of the total LIB demand will come from the EV sector. As a result, LIB manufacturers must aggressively ramp up cell production to keep pace with the immense growth of the EV market, a quest that brings scrutiny to the cost and sustainability of current LIB manufacturing practices. Here, cathodes are a critical component that largely determines the energy density and 40–50% of the total cell cost in LIBs. Rigorous consideration of the cathode performance and material cost is crucial in sustaining EV adoption. In this Viewpoint, we discuss why using cobalt in cathodes is unsustainable in the long run and highlight the features of cobalt-free cathodes.

25 ENERGY STORAGE↗
DOE OSTI2020

Direct-write orientation of charge-transfer liquid crystals enables polarization-based coding and encryption

Abstract Optical polarizers encompass a class of anisotropic materials that pass-through discrete orientations of light and are found in wide-ranging technologies, from windows and glasses to cameras, digital displays and photonic devices. The wire-grids, ordered surfaces, and aligned nanomaterials used to make polarized films cannot be easily reconfigured once aligned, limiting their use to stationary cross-polarizers in, for example, liquid crystal displays. Here we describe a supramolecular material set and patterning approach where the polarization angle in stand-alone films can be precisely defined at the single pixel level and reconfigured following initial alignment. This capability enables new routes for non-binary information storage, retrieval, and intrinsic encryption, and it suggests future technologies such as photonic chips that can be reconfigured using non-contact patterning.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

Eigenvalue-Based Micromagnetic Analysis of Switching in Spin-Torque-Driven Structures

We present an eigenvalue-based approach for studying the magnetization dynamics in magnetic nanostructures driven by spintronic excitations, such as spin-transfer torque and spin-orbit torque. The approach represents the system dynamics in terms of normal oscillation modes (eigenstates) with corresponding complex eigenfrequencies. The dynamics is driven by a small number of active eigenstates and often considering just a single eigenstate is sufficient. We develop a perturbation theory that provides semianalytical dynamic solutions by using eigenstates for the case in the absence of damping and spintronic excitations as a basis. The approach provides useful insights into dynamics in such systems and allows solving several difficulties in their modeling, such as extracting the switching current in magnetic random-access memories and understanding switching mechanisms. We show that the presented approach directly predicts the critical switching current, i.e., switching current for an infinite time. The approach also provides solutions for the switching dynamics allowing the switching current to be obtained for a finite switching time, provided that the system symmetry is broken, e.g., by tilting the polarizer, so that switching by a finite pulse is possible.

42 ENGINEERING↗
DOE OSTI2022

Implementation of a Binary Neural Network on a Passive Array of Magnetic Tunnel Junctions

The increasing scale of neural networks and their growing application space have produced demand for more energy- and memory-efficient artificial-intelligence-specific hardware. Avenues to mitigate the main issue, the von Neumann bottleneck, include in-memory and near-memory architectures, as well as algorithmic approaches. In this report we leverage the low-power and the inherently binary operation of magnetic tunnel junctions (MTJs) to demonstrate neural network hardware inference based on passive arrays of MTJs. In general, transferring a trained network model to hardware for inference is confronted by degradation in performance due to device-to-device variations, write errors, parasitic resistance, and nonidealities in the substrate. To quantify the effect of these hardware realities, we benchmark 300 unique weight matrix solutions of a two-layer perceptron to classify the Wine dataset for both classification accuracy and write fidelity. Despite device imperfections, we achieve software-equivalent accuracy of up to 95.3% with proper tuning of network parameters in 15 x 15 MTJ arrays having a range of device sizes. The success of this tuning process shows that new metrics are needed to characterize the performance and quality of networks reproduced in mixed signal hardware.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2025

Harnessing ionic complexity: A modeling approach for hierarchical ionic circuit design

Since the 1950s, soft ionic devices have evolved from individual components to an expanding library of sensors, actuators, signal transmitters, and processors. However, integrating these components into complex, multifunctional systems remains challenging due to the nonintuitive and nonlinear interactions between ionic elements. In this work, we address these fundamental challenges by developing a lumped element model that enables interrogation of the physics that governs ionic circuits, as well as rapid design and optimization. Our model captures features specific to ionic charge carriers, while preserving the hierarchical design flexibility and computational efficiency of traditional circuit modeling. We demonstrate that our model can not only fit individual device behavior but also accurately predict the behavior of larger circuits formed by combining those devices. Additionally, we show how our tool utilizes the intrinsic nonlinearities of ionic systems to enable extended functionality, revealing how factors such as ion enrichment, ion leakage, and polymer charge density influence performance. Lastly, we present a fully ionic power supply, sensor, control system, and actuator for a soft robot that adapts its motion in response to environmental salt, illustrating the tool’s potential to accelerate advancements in chemical sensing, biointerfacing, biomimetic systems, and adaptive materials.

42 ENGINEERING↗
DOE OSTI2021

Illuminating Galaxy Evolution at Cosmic Noon with ISCEA: the Infrared Satellite for Cosmic Evolution Astrophysics

ISCEA (Infrared Satellite for Cosmic Evolution Astrophysics) is a small astrophysics mission whose Science Goal is to discover how galaxies evolved in the cosmic web of dark matter at cosmic noon. Its Science Objective is to determine the history of star formation and its quenching in galaxies as a function of local density and stellar mass when the Universe was 3-5 Gyrs old (1.2 10 Mpc in each of 50 protocluster (cluster and cosmic web) fields at 1.2 < z < 2.1. ISCEA measures the star formation quenching factor in those fields, and galaxy kinematics with a precision < 50 km/s to deduce the 3D spatial distribution in each field. ISCEA will transform our understanding of galaxy evolution at cosmic noon. ISCEA is a small satellite observatory with a 30cm equivalent diameter aperture telescope with a FoV of 0.32 deg^2, and a multi-object spectrograph with a digital micro-mirror device (DMD) as its programmable slit mask. ISCEA will obtain spectra of 1000 galaxies simultaneously at an effective resolving power of R=1000, with 2.8"x2.8" slits, over the NIR wavelength range of 1.1 to 2.0 microns, a regime not accessible from the ground without large gaps in coverage. ISCEA will achieve a pointing accuracy of <= 2" FWHM over 200s. ISCEA will be launched into a Low Earth Orbit, with a prime mission of 2.5 years. ISCEA's space-qualification of DMDs opens a new window for spectroscopy from space, enabling revolutionary advances in astrophysics.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2025

Real-Time Testbed for Studying Cyberattacks and Defense in DER-integrated Smart Inverter Systems

In this paper, we propose a Hardware-in-the-Loop (HIL) simulation testbed suitable for the implementation and testing of realistic cyberattacks on grid-tied smart inverter systems integrated with Distributed Energy Resources (DER) that use the Distributed Network Protocol-3 (DNP3) protocol for communications between grid components. Specifically, our testbed combines a Real-Time Digital Simulator (RTDS) NovaCor device, outfitted with GNETx2 network interface cards, a gridtied DER topology implemented via the RTDS software package RSCAD, and a custom virtual network that emulates a man in the middle attacker. The Man-in-the-Middle (MITM) attacker captures DNP3 traffic and falsifies telemetry data in DNP3 packets to trigger unwarranted commands from a DNP3 controller that exploit smart inverter grid support functions. We choose DNP3 and implement grid support functions according to the IEEE Std. 1547-2018 mandated for the interconnection and interoperability of DER power systems with associated power components. Furthermore, we develop a protocol payload agnostic attack detection framework that leverages the round-trip time (RTT) anomalies between DNP3 requests and responses and can detect the presence of attacks without having to analyze the payload’s contents, while balancing trade-offs between false alarm counts, missed detections, and time to detection. To facilitate further research, we publicly release benign and attack network traffic exchanged between various sensors, controllers, and actuators in our grid-tied inverter testbed.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2024

Best Practices for Measuring the Modulation Transfer Function of Video Endoscopes

Endoscopes are crucial for assisting in surgery and disease diagnosis, including the early detection of cancer. The effective use of endoscopes relies on their optical performance, which can be characterized with a series of metrics such as resolution, vital for revealing anatomical details. The modulation transfer function (MTF) is a key metric for evaluating endoscope resolution. However, the 2020 version of the ISO 8600-5 standard, while introducing an endoscope MTF measurement method, lacks empirical validation and excludes opto-electronic video endoscopes, the largest family of endoscopes. Measuring the MTF of video endoscopes requires tailored standards that address their unique characteristics. This paper aims to expand the scope of ISO 8600-5:2020 to include video endoscopes, by optimizing the MTF test method and addressing parameters affecting measurement accuracy. We studied the effects of intensity and uniformity of image luminance, chart modulation compensation, linearity of image digital values, auto gain control, image enhancement, image compression and the region of interest dimensions on images of slanted-edge test charts, and thus the MTF based on these images. By analyzing these effects, we provided recommendations for setting and controlling these factors to obtain accurate MTF curves. Our goal is to enhance the standard’s relevance and effectiveness for measuring the MTF of a broader range of endoscopic devices, with potential applications in the MTF measurement of other digital imaging devices.

47 OTHER INSTRUMENTATION↗
DOE OSTI2024

Machine learning at the edge to improve in-field safeguards inspections

Artificial intelligence (AI) and machine learning (ML) are near-ubiquitous in day-to-day life; from cars with automated driver-assistance, recommender systems, generative content platforms, and large language chatbots. Implementing AI as a tool for international safeguards could significantly decrease the burden on safeguards inspectors and nuclear facility operators. The use of AI would allow inspectors to complete their in-field activities quicker, while identifying patterns and anomalies and freeing inspectors to focus on the uniquely human component of inspections. Sandia National Laboratories has spent the past two and a half years developing on-device machine learning to develop both a digital and robotic assistant. This combined platform, which we term inspecta, has numerous on-device machine learning capabilities that have been demonstrated at the laboratory scale. Here this work describes early successes implementing AI/ML capabilities to reduce the burden of tedious inspector tasks such as seal examination, information recall, note taking, and more.

98 NUCLEAR DISARMAMENT, SAFEGUARDS, AND PHYSICAL P↗
DOE OSTI2025

Probing the Kitaev honeycomb model on a neutral-atom quantum computer

Quantum simulations of many-body systems are among the most promising applications of quantum computers. In particular, models based on strongly correlated fermions are central to our understanding of quantum chemistry and materials problems, and can lead to exotic, topological phases of matter. However, owing to the non-local nature of fermions, such models are challenging to simulate with qubit devices. Here we realize a digital quantum simulation architecture for two-dimensional fermionic systems based on reconfigurable atom arrays. We utilize a fermion-to-qubit mapping based on Kitaev’s model on a honeycomb lattice, in which fermionic statistics are encoded using long-range entangled states. We prepare these states efficiently using measurement and feedforward, realize subsequent fermionic evolution through Floquet engineering with tunable entangling gates interspersed with atom rearrangement, and improve results with built-in error detection. Leveraging this fermion description of the Kitaev spin model, we efficiently prepare topological states across its complex phase diagram and verify the non-Abelian spin-liquid phase by evaluating an odd Chern number. We further explore this two-dimensional fermion system by realizing tunable dynamics and directly probing fermion exchange statistics. Finally, we simulate strong interactions and study the dynamics of the Fermi–Hubbard model on a square lattice. These results pave the way for digital quantum simulations of complex fermionic systems for materials science, chemistry and high-energy physics.

atomic and molecular physics↗
DOE OSTI2023

Next-Generation Modular Flexible Low-Cost SiC-based High-Frequency-Link Transformer (Final Technical Report)

This project, led by the small business NextWatt LLC, led to the design and development of a novel high-frequency-link (HFL) transformer that yields reduce number of semiconductor devices compared to conventional solid-state transformers (SSTs). The HFL transformer is based on SiC device technology and nanocrystalline-core-based magnetics techniology. The control actuation of the SiC devices are algorithmically implemented in digital signal processor (DSP) platform. The overall HFL transformer technology is conceived and developed using a modular approach. During the course of the project the novel technology and its experimental performances at the module, multi-phase, and cascaded multi-phase levels have been demonstrated to the sponsor U.S. Department of Energy (DOE).

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2024

IBR Digital Supply Chain Gap Analysis and Recommendations

The adoption of clean energy technologies, including solar photovoltaics, continues to introduce non-traditional stakeholders to the operations and planning of the electric system. Stakeholders such as manufacturers, vendors, owners, aggregators, and others are enabling the adoption, integration, and optimum operations of solar technologies at accelerated rates. Inverters form the foundation of many digitally controlled energy sources for clean energy technologies, including Solar, Battery Energy Storage Systems, Hybrid Systems, and Hydrogen Fuel Cells. Their supply chain is complex, a series of microchips, electronic switches and other components making up its primary functions. The complexity of this space and the growing digitization associated with these components can create supply chain cyber risks. One measure to mitigate cybersecurity attacks is proper digital supply chain security. The U.S. Department of Energy (DOE) Solar Energy Technologies Office (SETO), in partnership with the Cybersecurity, Energy, Security, and Emergency Response (CESER) office, is hosting a workshop to bring together solar vendors and services providers to discuss digital supply chain security for solar systems and challenges and opportunities in the transitioning to a fully domestic supply chain for solar energy in the U.S. This workshop will support the Securing Solar for the Grid (S2G) and Energy Cyber Sense program activities. During the workshop, industry experts and researchers from DOE National Laboratories will discuss the current solar supply chain landscape and the transition to domestic manufacturing of solar components in the U.S. Tools and techniques to better manage and secure the digital supply chain of solar devices and systems will be discussed.

cybersecurity↗
DOE OSTI2023

Improved noise performance from the next-generation buried-channel p-MOSFET SiSeROs

The Single electron Sensitive Read Out (SiSeRO) is a novel on-chip charge detector output stage for charge-coupled device (CCD) image sensors. Developed at MIT Lincoln Laboratory, this technology uses a p-MOSFET transistor with a depleted internal gate beneath the transistor channel. The transistor source-drain current is modulated by the transfer of charge into the internal gate. At Stanford, we have developed a readout module based on the drain current of the on-chip transistor to characterize the device. In our earlier work, we characterized a number of first prototype SiSeROs with the MOSFET transistor channels at the surface layer. An equivalent noise charge (ENC) of around 15 electrons root mean square (RMS) was obtained. In this work, we examine the first buried-channel SiSeRO. Here we have achieved substantially improved noise performance of around 4.5 electrons root mean square (RMS) and a full width half maximum (FWHM) energy resolution of 132 eV at 5.9 keV, for a readout speed of 625 kpixel/s. We also discuss how digital filtering techniques can be used to further improve the SiSeRO noise performance. Additional measurements and device simulations will be essential to further mature the SiSeRO technology. This new device class presents an exciting new technology for the next-generation astronomical X-ray telescopes requiring fast, low-noise, radiation-hard megapixel imagers with moderate spectroscopic resolution.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2026

Machine-Protection System for the PIP-II Linear Accelerator: Architecture and Data Processing Schemes

PIP-II at Fermilab features a brand-new, 800 MeV leading-edge Linear Accelerator (Linac) that will enable the Fermilab complex to deliver more than a megawatt of beam power to the Long Base-line Neutrino Facility (LBNF), requiring a robust Machine Protection System (MPS) to prevent beam-induced damage to delicate cryomodules and vacuum components. Signals in various accelerator components, in both analog and digital format, are collected and processed to assest the operating conditions of the accelerator. In the situation a potential damage is foreseen, the beam permit signal will be drop. A critical element of this system is the fast Analog Machine Protection System, a high-bandwidth platform designed for real-time beam loss monitoring. The system utilizes a modular, FPGA-based architecture to digitize signals from beam-sensing devices, such as AC Current Transformers (ACCTs), non-invasive Ring Pickups (RPUs) and beam scrapers. To eliminate both high frequency as well as 60 Hz noise that might be picked up in the industrial environment, data processing schemes beyond regular digital filtering are planned and implemented.

Wu, Jinyuan [Fermilab] (ORCID:0000000344329521)↗
DOE OSTI2021

Managing Cyber Supply Chain Risk for Renewable Energy Technologies

On July 1, 2021, the U.S. Department of Energy (DOE) Office of Cybersecurity, Energy Security, and Emergency Response (CESER) hosted a virtual workshop facilitated by the National Renewable Energy Laboratory (NREL). Cybersecurity supply chain experts, researchers, and leaders in government and industry came together to share information on current and future challenges in securing emerging technologies and technical architecture. From a cybersecurity perspective, we need to move from a cybersecurity approach that focuses principally on legacy asset owners to one that incorporates more emphasis on end-point device manufacturers and third-party integrators. Cybersecurity for the global digital supply chain for manufacturers of consumer end-point devices—such as smart solar inverters and smart electric vehicle (EV) chargers—will be critical to the future cyber health of the grid.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2024

ASIC Scoping Study Final Report

This report documents the results and findings of a one-year scoping study investigating multichannel readout application specific integrated circuits (ASICs) for interfacing to, and processing data from, silicon photomultiplier (SiPM) arrays. We document ASIC desired and required specifications for four applications supporting national security mission areas: neutron radiography, associated particle imaging, and two versions of kinematic neutron imaging cameras. While each application has a few unique requirements that stress capability, there is generally good agreement among most. Two recently developed ASIC devices were evaluated in a system-like configuration by interfacing these to scintillator crystals exposed to gamma and neutron sources. The 64-channel ORNL device delivered functional capability while meeting most mission requirements for neutron radiography. The Nalu Scientific device, a 32-channel full waveform digitizer, did not demonstrate reliable neutron / gamma separation but it is unclear if this was an ASIC issue or problems with test setup or firmware. A literature survey of other commercial and academic ASICs was undertaken to with the conclusion that existing devices do not meet all requirements.

42 ENGINEERING↗