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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 199 records · Page 11
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Chiral Instabilities and the Onset of Chiral Turbulence in QED Plasmas

We present a first principles study of chiral plasma instabilities and the onset of chiral turbulence in QED plasmas with strong gauge matter interaction ( e 2 N f = 64 ), far from equilibrium. By performing classical-statistical lattice simulations of the microscopic theory, we show that the generation of strong helical magnetic fields from a helicity imbalance in the fermion sector proceeds via three distinct phases. During the initial linear instability regime the helicity imbalance of the fermion sector causes an exponential growth (damping) of magnetic field modes with right- (left-) handed polarization, for which we extract the characteristic growth (damping) rates. Secondary growth of unstable modes accelerates the helicity transfer from fermions to gauge fields and ultimately leads to the emergence of a self-similar scaling regime characteristic of a decaying turbulence, where magnetic helicity is efficiently transferred to macroscopic length scales. Within this turbulent regime, the evolution of magnetic helicity spectrum can be described by an infrared power spectrum with spectral exponent κ = 10.2 ± 0.5 and dynamical scaling exponents α = 1.14 ± 0.50 and β = 0.37 ± 0.13 .

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2023

Fast Frequency Response using Reinforcement Learning-Controlled Wind Turbines

To fulfill the auxiliary grid services such as load regulation, spin and non-spin reserve, and frequency support during emergencies, power system operators often require certain wind farms to operate in de-loaded modes. By leveraging the fast response capability of wind farms, the reserved power in deloaded modes can significantly enhance the stability and reliability of power grids. This paper presents a novel methodology that incorporates wind turbines into reinforcement learning-based solutions for frequency response. The proposed approach employs the state-of-the-art reinforcement learning algorithm, surrogategradient- based evolution strategy (GSES), for continuous control of the wind farm output. Our methodology is tested on a modified IEEE-39 bus system, and simulation outcomes demonstrate that the proposed approach can reliably support the frequency of the power system and prevent unnecessary load shedding.

Gao, Wei↗
DOE OSTI2026

BIL GRID-READY WIND: Reliable and Economical Grid Services Design, Implementation, and Demonstration at the Great Pathfinder Wind Power Plant

The GRID-READY WIND project aimed to demonstrate the ability of utility-scale wind power plants (WPPs) to deliver traditional and emerging grid services (GSs) in both grid-following (GFL) and grid-forming (GFM) modes through a centralized control framework, supplemented by local control adjustments at the wind turbine generator as needed. Throughout the project, the team offered recommendations on how the grid services demonstrated with WPPs could be applied to other inverter-based resources (IBRs), such as photovoltaic (PV) systems and battery energy storage systems (BESS), while considering the unique dynamics of these resources arising from their energy generation characteristics. The project sought to enhance the confidence of system operators (SOs) and planners in utilizing WPPs, alongside other IBRs such as PV plants and BESS, to provide essential grid services over extended periods under diverse operating conditions, thereby supporting their integration into the bulk power system.

17 WIND ENERGY↗
DOE OSTI2021

Semicomputational calculation of Bragg shift in stratified materials

The fiber Bragg grating (FBG) may be viewed as a one dimensional photonic band-gap crystal by virtue of the periodic spatial perturbation imposed on the fiber core dielectric material. Similar to media supporting Bloch waves, the engraved weak index modulation, presenting a periodic “potential” to an incoming guided mode photon of the fiber, yields useful spectral properties that have been the basis for sensing applications and emerging quantum squeezing and solitons. The response of an FBG sensor to arbitrary external stimuli represents a multiphysics problem without a known analytical solution despite the growing use of FBGs in classical and quantum sensing and metrology. Here, we study this problem by first presenting a solid mechanics model for the thermal and elastic states of a stratified material. Additionally, the model considers an embedded optical material domain that represents the Bragg grating, here in the form of an FBG. Using the output of this model, we then compute the optical modes and their temperature- and stress-induced behavior. The developed model is applicable to media of arbitrary shape and composition, including soft matter and materials with nonlinear elasticity and geometric nonlinearity. Finally, we employ the computed surface stress and temperature distributions along the grating to analytically calculate the Bragg shift, which is found to be in reasonable agreement with our experimental measurements.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2025

Neural network-based classification and regression of magnetohydrodynamic modes in tokamaks

We present a machine learning-based magnetohydrodynamic (MHD) classifier and regressor that utilizes real or complex-valued 3D magnetic sensor array data to determine neoclassical tearing mode (NTM) onset times in tokamaks with millisecond accuracy. The input dataset consists of poloidal profiles of complex Fourier amplitudes with an n = 1 toroidal mode number from 144 human-labeled ITER Baseline Scenario discharges in the DIII-D tokamak, spanning both tearing-dominated and sawtooth-dominated regimes. Since m, n = 2,1 NTMs frequently emerge alongside sawteeth at the same frequency in this scenario, the focus is on isolating the m = 1 and m = 2 components of the n = 1 MHD mode near the tearing onset. To improve model regularization and prediction stability, singular value decomposition was applied to balance the sawtooth and tearing datasets. The enriched datasets facilitated training neural networks that learn the key distinguishing features of sawtooth and tearing modes in the poloidal profiles of their magnetic amplitude and phase. When the modes occur independently, the networks achieve perfect classification due to the modes’ distinct characteristics and low measurement noise. In the more experimentally relevant case where both modes coexist, the networks maintain exceptional performance across key metrics. Tests on synthetic data with known ground truth demonstrate the superior accuracy of the neural network trained on complex-valued input compared to models using real amplitude, phase, or pseudo-complex data, achieving both a mean time delay and standard deviation below 1 ms. Notably, standard linear regression methods fitting the dominant singular modes to the data closely match the neural network’s performance. Applying these methods across a broad range of H-mode scenarios will enable future studies to systematically identify dominant NTM triggers as scenario-specific variables, paving the way for more effective tearing mode avoidance strategies in future fusion reactor designs.

machine learning↗
DOE OSTI2023

Toward a tunable fabrication of multifunctional iron-aluminum spinels via solution combustion synthesis: The effects of fuel, heating mode, and Fe:Al precursor ratio

The solid solutions of iron and aluminum spinels (hercynite, maghemite, magnetite, and γ-alumina) are promising materials for emerging technologies such as solar thermochemical fuel production and clean dehydrogenation of fossil fuels. Solution combustion synthesis (SCS) is an attractive technique for the fabrication of these materials as it has been used for synthesis of many nanoscale oxides. However, the design space of SCS is large with many synthesis parameters affecting the properties of the combustion products. To optimize the SCS of these materials, it is important to determine which parameters yield the best properties. In the present work, FeAlO x nanocomposites were obtained by SCS using iron nitrate and aluminum nitrate as precursors and oxidizers. Two fuels (citric acid and glycine), three Fe:Al molar ratios (1:2, 1:1, and 2:1) in the precursors, and three heating modes (hotplate, muffle furnace, and microwave oven) were compared. The products were characterized by X-ray diffraction analysis, scanning electron microscopy, Brunauer-Emmett-Teller surface area analysis, and laser diffraction particle size analysis. The fuel used had the largest impact on the combustion behavior and hence the material properties. The high combustion temperature of glycine allowed for the formation of an FeAl 2 O 4 /Fe 3 O 4 solid solution, while the lower-temperature combustion of citric acid yielded Al-substituted γ-Fe 2 O 3 with high amorphous content. With citric acid, the specific surface area as high as 200 m 2 /g was achieved. The effect of Fe:Al precursor ratio was clearly seen in variations of the lattice parameter, which demonstrated the ability to tune the phase composition. The heating mode had minor effects on material properties, but some differences were observed. As a result, all products obtained by SCS in a microwave oven had low specific surface areas compared to those obtained with a hotplate and a muffle furnace.

Alumina↗
DOE OSTI2022

The density distribution and physical origins of intermittency in supersonic, highly magnetized turbulence with diverse modes of driving

The probability density function (PDF) of the logarithmic density contrast, s = ln (ρ/ρ 0 ), with gas density ρ and mean density ρ 0 , for hydrodynamical supersonic turbulence is well known to have significant non-Gaussian (intermittent) features that monotonically increase with the turbulent Mach number, M. By studying the mass- and volume-weighted s-PDF for an ensemble of 36 sub-to-trans-Alfvenic mean-field, supersonic, isothermal turbulence simulations with different modes of driving, relevant to molecular gas in the cool interstellar medium, we show that a more intricate picture emerges for the non-Gaussian nature of s. Using four independent measures of the non-Gaussian components, we find hydrodynamical-like structure in the highly magnetized plasma for M≲4. However, for M≳4, the non-Gaussian signatures disappear, leaving approximately Gaussian s-statistics – exactly the opposite of hydrodynamical turbulence in the high-M limit. We also find that the non-Gaussian components of the PDF increase monotonically with more compressive driving modes. To understand the M≲4 non-Gaussian features, we use one-dimensional pencil beams to explore the dynamics along and across the large-scale magnetic field, B 0 . Here we discuss kinetic, density, and magnetic field fluctuations from the pencil beams, and identify physical sources of non-Gaussian components to the PDF as single, strong shocks coupled to fast magnetosonic compressions that form along B 0 . We discuss the Gaussianization of the M≳4 s-fields through the lens of two phenomenologies: the self-similarity of the s-field and homogenization of the dynamical time-scales between the over- and underdense regions in the compressible gas.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2023

Fundamental aspects of Spacetime and Quantum Fields

The proposal contained two goals: firstly, placing fundamental bound on thermalization in Quantum Field Theories (QFTs) and, secondly, developing our understanding of emergent spacetime from matrices through concrete models. Since the previous reporting period, in collaboration with Sean Hartnoll we have continued our study of entanglement edge modes in matrix quantum mechanics (MQM). This has resulted in two papers. The first applies our construction for the Matrix Quantum Hall system first to fuzzy sphere states known to correspond to stringy M2-branes in MQM. Entanglement in these states using machine learning methods have also been studied by Sean Hartnoll and Xizhi Han in previous work done under this grant. Our construction builds on this work, and further demonstrates how area laws on fuzzy geometries emerge from strongly coupled systems. The second paper generalizes this construction to all noncommutative geometries with curvature much larger than the noncommutativity parameter. We demonstrate that despite UV/IR mixing effects, the structure of entanglement edge mode irreducible representations is determined by the boundary area of subsystems. On manifolds without global symmetries, we have demonstrated that nonlocal effects inherent to noncommutative geometries resum into a change of frame of the metric structure, similar to the change from string frame to Einstein frame for entanglement entropies calculated in string theory. These advancements lay the groundwork for future progress in the understanding of emergent geometry from large-N theories. Using these techniques, we are currently working on applying our methods to noncom mutative geometries whose construction is not so well understood, such as the fuzzy 5-sphere. Despite their opacity these objects are quite important, as string physics in the bulk of holographic systems bears many features of noncommutative geometry. We have also laid the groundwork of applying our methods to tensor networks, one of the most powerful models for understanding how geometry emerges from entanglement.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2024

Coherent Transfer of Lattice Entropy via Extreme Nonlinear Phononics in Metal Halide Perovskites

Entropy transfer in metal halide perovskites, characterized by significant lattice anharmonicity and low stiffness, underlies the remarkable properties observed in their optoelectronic applications, ranging from solar cells to lasers. The conventional view of this transfer involves stochastic processes occurring within a thermal bath of phonons, where the lattice arrangement and energy flow from higher- to lower-frequency modes. Here, we unveil a comprehensive chronological sequence detailing a conceptually distinct coherent transfer of entropy in a prototypical perovskite CH 3 NH 3 Pbl 3 . The terahertz periodic modulation imposes vibrational coherence into electronic states, leading to the emergence of mixed (vibronic) quantum beat between approximately 3 and 0.3 THz. We highlight a well-structured bidirectional time-frequency transfer of these diverse phonon modes, each developing at different times and transitioning from high to low frequencies from 3 to 0.3 THz, before reversing direction and ascending to around 0.8 THz. First-principles molecular dynamics simulations disentangle a complex web of coherent-phononic coupling pathways and identify the salient roles of the initial modes in shaping entropy evolution at later stages. Capitalizing on coherent entropy transfer and dynamic anharmonicity presents a compelling opportunity to exceed the fundamental thermodynamic (Shockley-Queisser) limit of photoconversion efficiency and to pioneer novel optoelectronic functionalities. Published by the American Physical Society 2024

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2022

VERDI: VERsatile DIffractometer with wide-angle polarization analysis for magnetic structure studies in powders and single crystals

The VERsatile DIffractometer will set a new standard for a world-class magnetic diffractometer with versatility for both powder and single crystal samples and capability for wide-angle polarization analysis. The instrument will utilize a large single-frame bandwidth and will offer high-resolution at low momentum transfers and excellent signal-to-noise ratio. A horizontal elliptical mirror concept with interchangeable guide pieces will provide high flexibility in beam divergence to allow for a high-resolution powder mode, a high-intensity single crystal mode, and a polarized beam option. A major science focus will be quantum materials that exhibit emergent properties arising from collective effects in condensed matter. The unique use of polarized neutrons to isolate the magnetic signature will provide optimal experimental input to state-of-the-art modeling approaches to access detailed insight into local magnetic ordering.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Hydrodynamics of quantum spin liquids

Quantum spin liquids are topological states of matter that arise in frustrated quantum magnets at low temperatures. At low energies, such states exhibit emergent gauge fields and fractionalized quasiparticles and can also possess enhanced global symmetries compared to their parent microscopic Hamiltonians. Here we study the consequences of this emergent gauge and symmetry structure for the hydrodynamics of quantum spin liquids. Specifically, we analyze two cases, the U(1) spin liquid with a Fermi surface and the SU(4)-symmetric “algebraic” spin liquid. We show that the emergent degrees of freedom in the spin liquid phase lead to a variety of additional hydrodynamic modes compared to the high-temperature paramagnetic phase. We identify a hydrodynamic regime for the internal U(1) gauge field common to both states, characterized by slow diffusion of the internal transverse photon.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Deep Reinforcement Learning Based Control of Wind Turbines for Fast Frequency Response

In order to fulfill vital auxiliary grid services, such as load regulation, spin and non-spin reserve provision, and frequency support during emergencies, there is often a requirement for certain wind farms to operate in de-loaded modes. Leveraging the swift response capabilities of wind farms, this study demonstrates that reserving power in de-loaded modes can significantly enhance power grid stability and reliability during system contingencies. Controlling wind farms optimally for frequency support is intricate due to the nonlinearity of models and controllers and the complexity of wind farm interactions with power systems. Here, to address this challenge, this paper introduces a novel approach that integrates wind turbines into reinforcement learning-based solutions for frequency response. This innovative methodology utilizes the state-of-the-art reinforcement learning algorithm known as the surrogate-gradient-based evolutionary strategy. The proposed learning-based algorithm provides continuous control of wind farm output to rapidly stabilize system frequency and prevent unnecessary trips of under-frequency load shedding relays. To facilitate efficient training, parallel computing techniques are employed. The proposed methodology is evaluated on a modified IEEE-39 bus system, and simulation results reveal its efficacy in reliably supporting power system frequency and preventing the need for unnecessary load shedding.

Gao, Wei [Argonne National Laboratory (ANL), Argon↗
DOE OSTI2021

Achieving extreme light confinement in low-index dielectric resonators through quasi-bound states in the continuum

Obtaining large field enhancement in low-refractive-index dielectric materials is highly relevant to many photonic and quantum optics applications. However, confining light in these materials is challenging, owing to light leakage through coupling to continuum modes in the surrounding environment. Here, we investigate the possibility of achieving high quality factors in low-index dielectric resonators through the bound states in the continuum (BIC). Our simulations demonstrate that destructive interference between leaky modes can be achieved by tuning the geometrical parameters of the resonator arrays, leading to the emergence of quasi-BIC in resonators that have a small index contrast to the underlying substrates. The resultant large field enhancement gives rise to giant quality factors and Purcell effects. By introducing vertical mirror symmetry, the quasi-BIC can be tuned into an ideal BIC. In addition, the quasi-BIC can modify the emission patterns of the coupled emitters, rendering highly directional and focused far-field emission. These findings may provide a path for the practical implementation of photonic and quantum devices based on low-index dielectric materials.

42 ENGINEERING↗
DOE OSTI2026

Enhancing Coherence Limits in Superconducting Quantum Systems for Computing and Sensing

This talk will highlight recent efforts at the SQMS Center to develop qudit-based quantum computing architectures using superconducting three-dimensional (3D) cavities, as well as the use of these ultra-coherent cavities for quantum sensing. I will present systematic studies of materials and devices aimed at identifying and mitigating the dominant sources of decoherence—including two-level systems (TLS), quasiparticles, and other noise mechanisms—in both transmons and 3D cavities. These investigations include microwave loss characterization of niobium, tantalum, aluminum, their native oxides, and substrate materials such as silicon and sapphire. By combining measurements on qubits and cavities, we disentangle subsystem-specific loss mechanisms and establish a hierarchy of mitigation strategies, leading to transmon coherence times exceeding one millisecond. I will also discuss studies of quasiparticle dynamics, including quasiparticle bursts observed in qubits operated both above ground and at the Gran Sasso underground laboratory, and the observation that applied magnetic fields can suppress temporal T₁ fluctuations. Building on these advances, we demonstrate a record-coherence two-cell cavity-qudit system with coherence times exceeding 20 milliseconds. Leveraging tunable sideband interactions together with error-resilient protocols, including measurement-based error correction and post-selection, we achieve high-fidelity quantum state control, including the preparation of Fock states up to N=20 with fidelities above 95% and the generation of high-fidelity two-mode entangled states. Finally, I will discuss how these ultra-coherent quantum systems are enabling emerging quantum sensing applications, including searches for dark matter and gravitational waves.

Roy, Tanay [Fermilab] (ORCID:000000019442862X)↗
DOE OSTI2021

RouteE: A Vehicle Energy Consumption Prediction Engine

The emergence of connected and automated vehicles and smart cities technologies create the opportunity for new mobility modes and routing decision tools, among many others. To achieve maximum mobility and minimum energy consumption, it is critical to understand the energy cost of decisions and optimize accordingly. The Route Energy prediction model (RouteE) enables accurate estimation of energy consumption for a variety of vehicle types over trips or sub-trips where detailed drive cycle data are unavailable. Applications include vehicle route selection, energy accounting and optimization in transportation simulation, and corridor energy analyses, among others. The software is a Python package that includes a variety of pre-trained models from the National Renewable Energy Laboratory (NREL). However, RouteE also enables users to train custom models using their own data sets, making it a robust and valuable tool for both fast calculations and rigorous, data-rich research efforts. The pre-trained RouteE models are established using NREL's Future Automotive Systems Technology Simulator paired with approximately 1 million miles of drive cycle data from the Transportation Secure Data Center, resulting in energy consumption behavior estimates over a representative sample of driving conditions for the United States. Validations have been performed using on-road fuel consumption data for conventional and electrified vehicle powertrains. Transferring the results of the on-road validation to a larger set of real-world origin-destination pairs, it is estimated that implementing the present methodology in a green-routing application would accurately select the route that consumes the least fuel 90% of the time. The novel machine learning techniques used in RouteE make it a flexible and robust tool for a variety of transportation applications.

47 OTHER INSTRUMENTATION↗
DOE OSTI2023

Observation of long-radial-range-correlation in turbulence in high-collisionality high-confinement plasmas on DIII-D

Abstract We report on the observation of spatially asymmetric turbulent structures with a long radial correlation length in the core of high-collisionality H -mode plasmas on DIII-D tokamak. These turbulent structures develop from shorter wavelength turbulence and have a radially elongated structure. The envelope of turbulence spans a broad radial range in the mid-radius region, leading to streamer-like transport events. The underlying turbulence is featured by intermittency, long-term memory effect, and the characteristic spectrum of self-organized criticality. The amplitude and the radial scale increase substantially when the shearing rate of the mean flow is reduced below the turbulent scattering rate. The enhanced long-radial-range-correlated (LRRC) transport events are accompanied by apparent degradation of normalized energy confinement time. The emergence of such LRRC transport events may serve as a candidate explanation for the degrading nature of H-mode core plasma confinement at high collisionality on DIII-D tokamak.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2025

Defect-induced phonon-resonant scattering and its influence on thermal transport of irradiated thorium-dioxide

Thermal transport in proton irradiated thorium-dioxide (ThO 2 ) is investigated. Using a combination of experiments and first-principles computational framework, the role of lattice defects on thermal conductivity is analyzed. A resonant-phonon scattering mechanism beyond the traditionally considered Rayleigh scattering is found to significantly influence low-temperature thermal transport in the presence of irradiation-induced point defects. The existence of localized phonon modes associated with irradiation-induced defects is suggested by the inability of the first-principles based thermal conductivity model—which considers only three-phonon interactions and phonon-defects scattering using the Tamura formalism—to predict the experimental results, unless a resonant scattering mechanism is included. The emergence of additional peaks in the Raman spectra in the proximity of phonon-resonant frequency provides further evidence for the existence of localized modes. Coupled with a microstructure evolution model, this analysis enables more accurate analysis for contrasting the contributions of different phonon scattering mechanisms across all irradiation doses and temperatures.

11 - NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2023

Direct observation of the collective modes of the charge density wave in the kagome metal CsV 3 Sb 5

A recently discovered group of kagome metals AV 3 Sb 5 (A = K, Rb, Cs) exhibit a variety of intertwined unconventional electronic phases, which emerge from a puzzling charge density wave phase. Understanding of this charge-ordered parent phase is crucial for deciphering the entire phase diagram. However, the mechanism of the charge density wave is still controversial, and its primary source of fluctuations—the collective modes—has not been experimentally observed. Here, we use ultrashort laser pulses to melt the charge order in CsV 3 Sb 5 and record the resulting dynamics using femtosecond angle-resolved photoemission. We resolve the melting time of the charge order and directly observe its amplitude mode, imposing a fundamental limit for the fastest possible lattice rearrangement time. These observations together with ab initio calculations provide clear evidence for a structural rather than electronic mechanism of the charge density wave. Our findings pave the way for a better understanding of the unconventional phases hosted on the kagome lattice.

36 MATERIALS SCIENCE↗