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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 109 records · Page 6
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Reimagining full wave rf quasilinear theory in a tokamak

The velocity dependent resonant interaction of particles with applied radio frequency (rf) waves during heating and current drive in the presence of pitch angle scattering collisions gives rise to narrow collisional velocity space boundary layers that dramatically enhance the role of collisions as recently shown by Catto (J. Plasma Phys., vol. 86, 815860302, 2020). The behavior is a generalization of the narrow collisional boundary layer that forms during Landau damping as found by Johnston (Phys. Fluids, vol. 14, 1971, pp. 2719-2726) and Auerbach (Phys. Fluids, vol. 20, 1977, pp. 1836-1844). For a wave of parallel wave number k|| interacting with weakly collisional plasma species of collision frequency ν and thermal speed vth , the effective collision frequency becomes of order ν(k_||v_th /ν)^2/3>> ν . The narrow boundary layers that arise because of the diffusive nature of the collisions allows a physically meaningful wave-particle interaction time to be defined that is the inverse of this effective collision frequency. The collisionality implied by the narrow boundary layer results in changes in the standard quasilinear treatment of applied rf fields in tokamaks while remaining consistent with causality. These changes occur because successive poloidal interactions with the rf are correlated in tokamak geometry and because the resonant velocity space dependent interactions are controlled by the spatial and temporal behavior of the perturbed full wave fields rather than just the spatially local Landau and Doppler shifted cyclotron wave-particle resonance condition associated with unperturbed motion of the particles. The correlation of successive poloidal circuits of the tokamak leads to the appearance in the quasilinear operator of transit averaged resonance conditions localized in velocity space boundary layers that maintain negative definite entropy production.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2024

Overview of Large Helical Device experiments of basic plasma physics for solving crucial issues in reaching burning plasma conditions

Recently, experiments on basic plasma physics issues for solving future problems in fusion energy have been performed on a Large Helical Device. There are several problems to be solved in future devices for fusion energy. Emerging issues in burning plasma are: alpha-channeling (ion heating by alpha particles), turbulence and transport in electron dominant heating helium ash exhaust, reduction of the divertor heat load. To solve these problems, understanding the basic plasma physics of (1) wave–particle interaction through (inverse) Landau damping, (2) characteristics of electron-scale (high-k) turbulence, (3) ion mixing and the isotope effect, and (4) turbulence spreading and detachment, is necessary. This overview discusses the experimental studies on these issues and turbulent transport in multi-ion plasma and other issues in the appendix.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2020

On the light-front wave functions of quarkonia

The light-front wave functions of hadrons allow us to calculate a wide range of physical observables; however, the wave functions themselves cannot be measured. We discuss recent results for quarkonia obtained in basis light-front quantization using an effective Hamiltonian with a confining model in both the transverse and longitudinal directions and with explicit one-gluon exchange. In particular, we focus on the numerical convergence of the basis expansion, as well as the asymptotic behavior of the light-front wave functions. Finally, we also illustrate that, for mesons with unequal quark masses, the maxima of the light-front wave functions depend in a non-trivial way on the valence quark-mass difference.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2022

Physics-informed neural networks for identification of material properties using standing waves

A metallic structure in its initial stage of failure involves plastic deformation or environmental degradation that changes the elastic modulus and density. This work presents the detection of change in wave velocity (a function of elastic modulus and density) as a system identification problem. A physics-informed neural network (PINN) is proposed to solve the system identification problem. The PINN takes the spatial coordinates of scanning locations and time as inputs and provides the displacement and wave velocity as outputs. The governing partial differential equation of standing waves in a rod is incorporated into the neural network as physics in the form of a loss function. The wave velocity vector is randomly initiated. During the training of the network, physics is used to determine and update the wave velocity target vector from the network’s displacement predictions. The measured data, comprising sparse displacement response on the rod structure, are used to train the PINN. The wave velocity at the sparse locations on the rod is learned from the predicted displacements during the training. Using the predictions of the trained network, the response of free vibration or material property variation can be reconstructed at unscanned locations on the structure to obtain high-resolution maps for full-field imaging to detect and localize the changes caused by plastic deformation. The PINN’s sparse scanning and simultaneous prediction capability during training can lead to high scanning and data-processing speeds. This capability yields a nondestructive evaluation system that can predict the presence of degraded material locations as the structural vibrations are scanned and processed in real time.

Rathod, Vivek↗
DOE OSTI2024

Data Imbalance, Uncertainty Quantification, and Transfer Learning in Data‐Driven Parameterizations: Lessons From the Emulation of Gravity Wave Momentum Transport in WACCM

Abstract Neural networks (NNs) are increasingly used for data‐driven subgrid‐scale parameterizations in weather and climate models. While NNs are powerful tools for learning complex non‐linear relationships from data, there are several challenges in using them for parameterizations. Three of these challenges are (a) data imbalance related to learning rare, often large‐amplitude, samples; (b) uncertainty quantification (UQ) of the predictions to provide an accuracy indicator; and (c) generalization to other climates, for example, those with different radiative forcings. Here, we examine the performance of methods for addressing these challenges using NN‐based emulators of the Whole Atmosphere Community Climate Model (WACCM) physics‐based gravity wave (GW) parameterizations as a test case. WACCM has complex, state‐of‐the‐art parameterizations for orography‐, convection‐, and front‐driven GWs. Convection‐ and orography‐driven GWs have significant data imbalance due to the absence of convection or orography in most grid points. We address data imbalance using resampling and/or weighted loss functions, enabling the successful emulation of parameterizations for all three sources. We demonstrate that three UQ methods (Bayesian NNs, variational auto‐encoders, and dropouts) provide ensemble spreads that correspond to accuracy during testing, offering criteria for identifying when an NN gives inaccurate predictions. Finally, we show that the accuracy of these NNs decreases for a warmer climate (4 × CO 2 ). However, their performance is significantly improved by applying transfer learning, for example, re‐training only one layer using ∼1% new data from the warmer climate. The findings of this study offer insights for developing reliable and generalizable data‐driven parameterizations for various processes, including (but not limited to) GWs.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Universality aspects of quantum corrections to transverse momentum broadening in QCD media

We study non-linear quantum corrections to transverse momentum broadening (TMB) of a fast parton propagating in dense QCD matter in the leading logarithmic approximation. These non-local corrections yield an anomalous super-diffusive behavior characterized by a heavy tailed distribution which is associated with Lévy random walks. Using a formal analogy with the physics of traveling waves, we show that at late times the transverse momentum distribution tends to a universal scaling regime. We derive analytic solutions in terms of an asymptotic expansion around the scaling limit for both fixed and running coupling. We note that our analytic approach yields a good agreement with the exact numerical solutions down to realistic values of medium length. Finally, we discuss the interplay between system size and energy dependence of the diffusion coefficient $\hat{q}$ and its connection with the gluon distribution function that is manifest at large transverse momentum transfer.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2026

Emulating 2D Materials with Magnons

Spin waves (magnons) in two-dimensional (2D) materials have received increasing interest due to their unique states and potential for tunability. However, many interesting features of these systems, including Dirac points and topological states, occur at high frequencies, where experimental probes are limited. Here, we study a crystal formed by patterning a hexagonal array of holes in a perpendicularly magnetized thin film. Through simulation, we find that the magnonic band structure imitates that of graphene, but additionally has some kagomelike character and includes a few flat bands. Surprisingly, its nature can be understood using a nine-band tight-binding Hamiltonian. This clear analogy to 2D materials enables band-gap engineering in 2D, topological magnons along 1D phase boundaries, and spectrally isolated modes at 0D point defects. Interestingly, the 1D phase boundaries allow access to the valley degree of freedom through a magnonic analog of the quantum valley Hall insulator. These approaches can be extended to other magnonic systems, but are potentially more general due to the simplicity of the model, which resembles existing results from electron, phonon, photon, and cold-atom systems. This finding brings the physics of spin waves in 2D materials to more experimentally accessible scales, augments it, and outlines a few principles for controlling magnonic states.

Ferrimagnets↗
DOE OSTI2026

Understanding carbon sourcing and transport originating from the helicon antenna surfaces during high-power helicon discharge in DIII-D Tokamak

The high-power helicon wave system in the DIII-D tokamak could potentially introduce new plasma–material interaction (PMI) challenges owing to rectified RF sheath potentials that develop near the antenna and surrounding plasma-facing components. We present the first application of the STRIPE (Simulated Transport of RF Impurity Production and Emission) framework to helicon-induced PMIs, extending previous STRIPE studies of ICRH antennas by incorporating net erosion, local re-deposition, and three-dimensional global impurity transport. The integrated workflow couples SOLPS, COMSOL, RustBCA, GITR, and GITRm to simulate carbon erosion, re-deposition, and impurity transport for two experimentally constrained DIII-D H-mode helicon operating scenarios with different antenna–plasma gaps, coupled RF powers, and edge plasma conditions. COMSOL predicts rectified RF sheath potentials of 1–5 kV localized near the lower portion of the antenna, where the magnetic field intersects the surface at grazing incidence. Carbon self-sputtering dominates the erosion source, whereas RF-accelerated D + ions contribute approximately 1% of the total gross erosion. The smaller-gap operating scenario exhibits substantially stronger gross erosion, enhanced local re-deposition (∼12%), and a larger confined carbon inventory owing to increased plasma accessibility and broader RF sheath coverage. Comparison with available DIII-D measurements shows no distinct change in the global carbon signal that correlates with the helicon RF pulse, consistent with the simulations indicating that the helicon-generated carbon source remains small compared with the existing background carbon inventory under the present graphite-wall operating conditions. These results demonstrate the capability of STRIPE to integrate RF sheath modeling, plasma transport, surface interaction physics, and three-dimensional impurity transport for the interpretation of helicon-induced PMIs. The study further identifies the principal sources of modeling uncertainty, including grazing-angle RF sheath physics, slow-wave resolution, plasma-background extrapolation, and trace-impurity transport assumptions, providing a framework for future validation and model development.

Kumar, Atul [Oak Ridge National Laboratory (ORNL),↗
DOE OSTI2021

Multiphenomenology explosion monitoring (MultiPEM): a general framework for data interpretation and yield estimation

SUMMARY An underground nuclear explosion (UNE) couples mechanical energy into crustal rock, which propagates as seismic and acoustic waves. These different physical phenomena transport, by different pathways, to standoff detectors at varying distances. The transport pathways attenuate the original signal but in different ways. Enabled by correct statistical weighting, signal attenuation models can be used to combine these disparate sensor data to estimate the yield of an UNE. Contemporaneous statistical models, used in yield estimation, can be improved with an advanced partition of error for these physical signal propagation models. We present an advanced multivariate approach to error modelling of multiphenomenology physical signatures. In addition to measurement error, our error model represents physical model biases as random with a physics-based covariance structure. To illustrate this proposed framework, we demonstrate the estimation of explosion yield using openly available seismic and acoustic data from chemical single-point explosions.

Williams, Brian J.↗
DOE OSTI2023

Microscopic calculation of fission product yields for odd-mass nuclei

Fission data are essential inputs to reaction networks involved in nucleosynthesis simulations and nuclear forensics. In such applications as well as in the description of multichance fission, the characteristics of fission for odd-mass nuclei are just as important as those for even-even nuclei. The fission theories that aim at explicitly describing fission dynamics are typically based on some variant of the nuclear mean-field theory. In such cases, the treatment of systems with an odd number of particles is markedly more involved, both formally and computationally. In this article, we use the blocking prescription of the Hartree-Fock-Bogoliubov theory with Skyrme energy functionals to compute the deformation properties of odd-mass uranium isotopes. We show that the resulting fission fragment distributions depend quite significantly on the spin of the odd neutron. By direct calculation of the spin distribution of the fissioning nucleus, we propose a methodology to rigorously predict the charge and mass distributions in odd-mass nuclei.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
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 OSTI2026

FAIR Data and Interpretable AI Framework for Architectured Metamaterials (Final Report)

This research program established a transformative framework for the discovery and design of mechanical metamaterials, which are architected structures engineered to control physical phenomena like sound and vibration in ways natural materials cannot. To overcome the traditional reliance on trial-and-error, the project developed an interpretable Artificial Intelligence (AI) framework that moves beyond "black box" models to reveal the specific geometric patterns—such as "unit-cell templates"—that govern a material’s performance. A major breakthrough was the development of a hierarchical design method, which allows a single material to block vibrations across multiple frequency ranges simultaneously by layering patterns at different scales without them interfering with one another. This was further expanded to include irregular, graph-based designs that use spanning tree algorithms to ensure structural connectivity while allowing for customized, direction-dependent properties like stiffness and acoustic impedance. Beyond design, the project addressed the practicalities of real-world production by developing uncertainty quantification techniques that account for manufacturing defects and material variability, reducing the need for expensive physical testing by orders of magnitude. To speed up the discovery process, the team implemented Gaussian Process Regression and other surrogate models that provide accurate performance predictions at a fraction of the traditional computational cost. The AI-generated designs were successfully validated through fabrication of physical samples and wave propagation experiments, confirming their ability to accurately guide or reflect waves as predicted. By contributing these tools and high-quality FAIR benchmark datasets to the wider scientific community, this work provides a scalable foundation for advancing technologies in aerospace vibration control, medical imaging, and noise reduction.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Remote Fiber Based Velocimeter For Interface Strength Measurements

The understanding of the performance of nuclear reactor plate fuels is essential when developing new fuel systems. The condition of pre and post irradiated plate fuels must be characterized effectively and efficiently prior to irradiation and then post irradiation within a high rad environment. The post characterization work must be performed remotely and in an environment hostile to instrumentation. Laser based characterization methods provide the capability to be remote and robust within a hot-cell environment. Laser based characterization can provide high spatial resolution appropriate for scanning and imaging large areas. The INL is currently developing a laser shock system for Post Irradiation Examination (PIE) station for use in the Hot Fuel Examination Facility (HFEF) at the INL. The laser shock system is designed to characterize fuel to cladding and cladding to cladding interface strength. The laser shock-technique induces large amplitude shock waves to mechanically characterize interfaces such as the fuel-clad interface. The shock wave propagates as a compressional wave through the fuel plate to the free (unconfined) backside surface and is reflected back through the test plate as a rarefaction (tensile) wave. This rarefaction wave is the physical mechanism that produces internal delamination failure. The focus or this paper is on the accurate measurement of surface velocity to quantify the amount of energy traveling through the interfaces to the back surface of the fuel plate. The original fiber based velocimeter design was biased by parasitic back reflections. This paper will discuss the discovery of the back-reflection issue and the design changes to resolve the issue. Supporting data will be presented.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2023

Charge Density Wave Order and Electronic Phase Transitions in a Dilute d ‐Band Semiconductor

Abstract As one of the most fundamental physical phenomena, charge density wave (CDW) order predominantly occurs in metallic systems such as quasi‐1D metals, doped cuprates, and transition metal dichalcogenides, where it is well understood in terms of Fermi surface nesting and electron–phonon coupling mechanisms. On the other hand, CDW phenomena in semiconducting systems, particularly at the low carrier concentration limit, are less common and feature intricate characteristics, which often necessitate the exploration of novel mechanisms, such as electron–hole coupling or Mott physics, to explain. In this study, an approach combining electrical transport, synchrotron X‐ray diffraction, and density‐functional theory calculations is used to investigate CDW order and a series of hysteretic phase transitions in a dilute d ‐band semiconductor, BaTiS 3 . These experimental and theoretical findings suggest that the observed CDW order and phase transitions in BaTiS 3 may be attributed to both electron–phonon coupling and non‐negligible electron–electron interactions in the system. This work highlights BaTiS 3 as a unique platform to explore CDW physics and novel electronic phases in the dilute filling limit and opens new opportunities for developing novel electronic devices.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Multiwavelength Afterglow Analysis of GRB 221009A: Unveiling the Evolution of a Cooling Break in a Wind-like Medium

Gamma-ray bursts (GRBs) are the most energetic explosions in the Universe, and their afterglow emission provides an opportunity to probe the physics of relativistic shock waves in an extreme environment. Several key pieces for completing the picture of GRB afterglow physics are still missing, including jet properties, the emission mechanism, and particle acceleration. Here, we present a study of the afterglow emission of GRB 221009A, the most energetic GRB ever observed. Using optical, X-ray, and gamma-ray data up to approximately 2 days after the trigger, we trace the evolution of the multiwavelength spectrum and the physical parameters behind the emission process. The broadband spectrum is consistent with the synchrotron emission emitted by relativistic electrons with its index of p = 2.29 ± 0.02. We identify a break energy at keV and an exponential cutoff at GeV in the observed multiwavelength spectrum. The break energy increases in time from $16.0^{+7.1}_{-4.9}$ keV at 0.65 days to $46.8^{+25.0}_{-15.5}$ keV at 1.68 days, favoring a stellar-wind-like profile of the circumburst medium with k = 2.4 ± 0.1 as in ρ(r) ∝ r -k . The high-energy attenuation at around 0.4 to 4 GeV is attributed to the maximum of the particle acceleration in the relativistic shock wave. This study confirms that the synchrotron process can explain the multiwavelength afterglow emission and its evolution.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2023

Advancements in High-Q development for novel medium-velocity 644 MHz 5-cell elliptical superconducting RF cavities for continuous-wave operation in heavy-ion linacs

The Facility for Rare Isotope Beams is a world-leading center for experimental nuclear physics research, and relies on a first-in-kind superconducting RF driver linac to supply 400 kW beams of a uniquely large range of particles, from protons through the heaviest uranium ions. The FRIB400 project proposes to double the end energy of the current superconducting FRIB linac for the heaviest uranium ions from 200 MeV per nucleon (MeV/u) to 400 MeV/u, which equates to approximately 1 GeV for protons. The increased rare isotope production from higher-energy drive beams would deliver new and exciting capabilities to FRIB users, which include extending the facility's reach along the neutron drip-line, greatly increasing the facility's rare isotope yield, and significantly improving the precision of parameter measurements for the nuclear matter equation of state, which is a particularly timely complement to the recent advent of multi-messenger astronomy, and the study of neutron star mergers. The unique set of design parameters defined by the 400 MeV/u (uranium) energy goal, the physical size, continuous-wave operation, and cryogenic capacity of the extant facilities at FRIB, requires development of a first-in-kind beta = v/c = 0.65, 644 MHz 5-cell elliptical superconducting RF (SRF) cavity, with field-leading efficiency, measured by the cavity's intrinsic quality factor, Q_0. The field of SRF has recently experienced a very active period of development, which demonstrated the potential to raise the Q_0 of 1.3 GHz cavities by factors of two to three or more. In addition to uncovering fascinating new properties of the fundamental physics of superconducting RF phenomena, these improvements suggest substantial improvements could be made over the minimum Q_0 of the proposed FRIB400 upgrade SRF cavities, if these new techniques can be adapted to the beta = 0.65, 644 MHz regime. This work presents the first validation of the the novel beta = 0.65, 644 MHz 5-cell elliptical superconducting RF cavity design, and the first results of both conventional and advanced RF surface preparation techniques. In particular, this work focuses on two recently-developed high-Q_0 recipes: N-doping, and furnace baking, setting the current world-record Q_0 for this type of resonant cavity at 3.8 x 10^10 at the FRIB400 operating gradient of 17.5 MV/m. We also find evidence that these results may be further improved upon, and make the case for further RF surface preparation recipe optimization. We then pair these high-power RF investigations with techniques borrowed from material science to try to better understand the ways in which specific properties of the niobium material, such as grain structure and superconducting flux pinning force, can be related to its superconducting RF performance. These experiments better elucidate the effects of high-temperature annealing on niobium samples, which suggest methods of further optimizing cavity flux expulsion and thus SRF performance.Read

McGee, Kellen E.↗
DOE OSTI2020

Fate of Internal Waves on a Shallow Shelf

Internal waves strongly influence the physical and chemical environment of coastal ecosystems worldwide. We report novel observations from a distributed temperature sensing (DTS) system that tracked the transformation of internal waves from the shelf break to the surf zone over a narrow shelf slope region in the South China Sea. The spatially continuous view of temperature fields provides a perspective of physical processes commonly available only in laboratory settings or numerical models, including internal wave reflection off a natural slope, shoreward transport of dense fluid within trapped cores, and observations of internal rundown (near-bed, offshore-directed jets of water preceding a breaking internal wave). Analysis shows that the fate of internal waves on this shelf—whether transmitted into shallow waters or reflected back offshore—is mediated by local water column density structure and background currents set by the previous shoaling internal waves, highlighting the importance of wave-wave interactions in nearshore internal wave dynamics.

54 ENVIRONMENTAL SCIENCES↗