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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 307 records · Page 17
All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

Geometric optics model of Thomson scattering enhanced by parametric coupling

We formulate a theory of Thomson scattering (TS) from inhomogeneous plasmas using a ray-tracing approach. The scattered radiation is described within the eikonal approximation, including convective gain arising from scattering instabilities driven by the TS probe laser. In the limit of a weak probe and vanishing gain, the theory recovers the standard TS cross section and scattered power due to volumetric particle noise. The ray-tracing formalism is implemented in a TS fitting framework, enabling accurate analysis of inhomogeneous plasmas whose background profiles can be provided by large-scale hydrodynamic simulations or experimental diagnostics. Here, several examples of synthetic TS spectra are presented, and further extensions of the theory are discussed.

Carleton, D. E. [University of Alberta, Edmonton, ↗
DOE OSTI2021

Parametric Study of Used Nuclear Oxide Fuel Constituent Dissolution in Molten LiCl-KCl-UCl 3

Prior work identified dissolution of used nuclear oxide fuel constituents from a uranium oxide matrix into molten LiCl-KCl-UCl 3 at 500°C, prompting a subsequent series of three progressive studies (including an initial scoping study, an electrolytic dissolution study, and a chemical-seeded dissolution study) to further investigate associated parameters and mechanisms. Thermodynamic calculations were performed to identify possible reaction mechanisms and their propensities in used oxide fuel constituent dissolution. Used nuclear oxide fuels with varying preconditions from fast and thermal test reactors were separately immersed in the subject salt system to assess fuel constituent migration from the bulk fuel matrix to the salt phase in an initial scoping study. Dissolution of expected fuel constituents, including alkali, alkaline earth, lanthanide, and transuranium oxides, into the chloride salt phase varied widely, ranging from 12% to 99% in the initial study. Uranium isotope blending between the salt phase and bulk fuel matrix was also observed, which was attributed to reducing conditions in the fuel matrix. Electrolytic and chemical-seeded dissolution studies were subsequently performed to effect reducing conditions in the fuel. Other parameters, including temperature (at 500°C, 650°C, 725°C, and 800°C) and uranium trichloride concentrations (at 6, 9, and 19 wt% uranium), were investigated in the latter two studies, resulting in fuel constituent dissolution above 90%. Extents of dissolution were based on initial and final fuel constituent concentrations in the oxide fuels following operations in the salt and subsequent removal of the salt via distillation. Finally, in this series of progressive studies, oxide fuel preconditioning and in situ reducing conditions, along with elevated temperature and uranium trichloride concentrations, were the primary parameters promoting used nuclear oxide fuel constituent dissolution in accordance with identified reaction mechanisms.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2022

Parametric studies of stream instability-induced higher harmonics in plasma ionization breakdown near an emissive dielectric surface

Here, in this work, we comprehensively investigate the generation of higher harmonic (HH) electric fields normal to the applied rf electric field in multipactor-coexisting plasma breakdown by fully kinetic particle-in-cell (PIC) simulations and a theoretical model. Firstly, a base case at driving frequency 1 GHz, transverse rf electric field amplitude 3 MV m –1 , and background gas pressure 0.2 Torr, is studied in detail. The enhanced harmonic frequency observed is around ten times the fundamental rf frequency, significantly lower than the Langmuir frequency. A theoretical model reveals that the fundamental mechanism of HHs generation is stream–plasma instability, which originates from stream-like secondary electron emission interacting with plasma. The resulting HH frequency and the growth rate of its oscillating amplitude from the theoretical model, agree well with the PIC simulations. With increasing pressure, the HH oscillations are found to be significantly reduced. This is because at higher pressure the gas ionization rate is higher, which causes a more rapidly increasing plasma density, leaving less time for the growth of instability. Furthermore, the parameter space in terms of background gas pressure and rf field amplitude within which the HHs appear is revealed. Finally, the effect of the driving rf frequency on HHs is also investigated, and it is found that the instability-induced oscillating HHs field remains when the driving frequency is increased to 2 GHz, however, it is highly reduced at higher driving frequency of 5 GHz, as oscillations at the fundamental frequency start playing a more important role.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2023

Non-parametric Lagrangian biasing from the insights of neural nets

We present a Lagrangian model of galaxy clustering bias in which we train a neural net using the local properties of the smoothed initial density field to predict the late-time mass-weighted halo field.By fitting the mass-weighted halo field in the AbacusSummit simulations at z = 0.5, we find that including three coarsely spaced smoothing scales gives the best recovery of the halo power spectrum. Adding more smoothing scales may lead to 2–5% underestimation of the large-scale power and can cause the neural net to overfit.We find that the fitted halo-to-mass ratio can be well described by two directions in the original high-dimension feature space.Projecting the original features into these two principal components and re-training the neural net either reproduces the original training result, or outperforms it with a better match of the halo power spectrum. The elements of the principal components are unlikely to be assigned physical meanings, partly owing to the features being highly correlated between different smoothing scales.Our work illustrates a potential need to include multiple smoothing scales when studying galaxy bias, and this can be done easily with machine-learning methods that can take in high dimensional input feature space.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2021

Parametric dependencies of locked mode thresholds in KSTAR L-mode plasmas

Resonant error fields with toroidal harmonics n = 1 and n = 2 in otherwise axisymmetric tokamak plasmas can drive disruptive locked modes when their strength exceeds a threshold value. This paper presents the sensitivity of error field threshold in global parameters such as line-averaged density and toroidal magnetic field as well as toroidal harmonics dependence in KSTAR L-mode plasmas. The error field threshold for mode-locking increases along with density up to a roll-over density, which coincides well with the linear ohmic confinement (LOC) to saturated ohmic confinement (SOC) density for the studied KSTAR plasmas. As a result, this non-monotonic density correlation of the error field threshold in LOC–SOC can be explained by a modification of existing theoretical scaling in SOC, while plasma rotation can also play an important role in setting the resonant error field threshold.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2023

Parametric study of Alfvénic instabilities driven by runaway electrons during the current quench in DIII-D

Abstract To avoid or mitigate runaway electron (RE) beams in ITER, RE-driven instabilities are actively studied as a complimentary technique to massive material injection. In this work we report experimental dependencies of Alfvénic instabilities driven by REs during the current quench in DIII-D on plasma and material injection parameters. These instabilities, observed in the frequency range of 0.1–3 MHz, correlate with increased RE loss and thus may play a role in non-sustained RE beams. It was found that as the toroidal magnetic field ( B T ) decreases, the RE population becomes more energetic, the energy of instabilities increases, and no RE beam is observed when the maximum energy of REs exceeds 15 MeV (or when B T is below 1.8 T). Analysis of disruptions at plasma core temperature ( T e ) of 1 keV and 8 keV shows that the RE population is much less energetic (with the maximum energy of only about 3 MeV) when T e is high, and no instabilities are observed in this case. Besides disruptions above caused by Ar injection, cases with Ne and D 2 injections were also studied. Both Ne and D 2 injections cause no sustained RE beams, however, for different reasons. Measurements of the instability polarization indicate that it is of predominantly compressional nature at the edge, which is consistent with modeling suggesting excitation of compressional Alfvén eigenmodes. However, drive of global Alfvén eigenmodes is also possible at low frequencies.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2020

Parametrizing the detector response with neural networks

In high energy physics, characterizing the response of a detector to radiation is one of the most important and fundamental experimental tasks. In many cases, this task is accomplished by parameterizing summary statistics of the full detector response probability density. The parameterized detector response can then be used for calibration as well as for directly improving physics analysis sensitivity. This paper reports on how to parameterize summary statistics of the detector response using neural networks. Specifically, neural networks are powerful tools for incorporating multidimensional data and the loss function used during training determines which summary statistic is learned. One common summary statistic that has not been combined with deep learning (as far as the authors are aware) is the mode. A neural network-based approach to mode learning is proposed and empirically demonstrated in the context of high energy jet calibrations. Altogether, the neural network-based toolkit for detector response parameterization can enhance the utility of data collected at high energy physics experiments and beyond.

47 OTHER INSTRUMENTATION↗
DOE OSTI2020

Automated detector simulation and reconstruction parametrization using machine learning

Rapidly applying the effects of detector response to physics objects (e.g. electrons, muons, showers of particles) is essential in high energy physics. Presently available tools for the transformation from truth-level physics objects to reconstructed detector-level physics objects involve manually defining resolution functions. These resolution functions are typically derived in bins of variables that are correlated with the resolution (e.g. pseudorapidity and transverse momentum). This process is time consuming, requires manual updates when detector conditions change, and can miss important correlations. Machine learning offers a way to automate the process of building these truth-to-reconstructed object transformations and can capture complex correlation for any given set of input variables. Such machine learning algorithms, with sufficient optimization, could have a wide range of applications: improving phenomenological studies by using a better detector representation, allowing for more efficient production of Geant4 simulation by only simulating events within an interesting part of phase space, and studies on future experimental sensitivity to new physics.

47 OTHER INSTRUMENTATION↗
DOE OSTI2023

Non-parametric projections of national income distribution consistent with the Shared Socioeconomic Pathways

Understanding and projecting income distributions within countries and regions is important to understanding consumption trends and the distributional consequences of climate impacts and responses. Several global, country-level projections of income distribution are available but most project only the Gini coefficient (a summary statistic of the distribution) or utilize the Gini along with the assumption of a lognormal distribution. We test the lognormal assumption and find that it typically underestimates income in the highest deciles and over-estimates it in others. We find that a new model based on two principal components of national time series data for income distribution provides a better fit to the data for all deciles, especially for the highest and lowest. We also construct a projection model in which the first principal component is driven by the Gini coefficient and the second captures deviations from this relationship. We use the model to project income distribution by decile for all countries for the five shared socioeconomic pathways. We find that inequality is consistently higher than projections based on the Gini and the lognormal functional form, with some countries reaching ratios of the highest to lowest income deciles that are almost three times their value using the lognormal assumption.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2025

Small-𝑥 gluon GPD constrained from deeply virtual 𝐽/𝜓 production and gluon PDF through universal-moment parametrization

We phenomenologically constrain the small-𝑥 and small-𝜉 gluon generalized parton distributions (GPDs) with the deeply virtual 𝐽/𝜓 production (DV⁢𝐽/𝜓⁢P) in the framework of GPDs through universal moment parameterization (GUMP). We use a hybrid cross-section formula combining collinear factorization to the next-to-leading order (NLO) accuracy of the strong coupling 𝛼 𝑠 , with corrections from nonrelativistic QCD to account for the power corrections due to the heavy 𝐽/𝜓 mass. We reach reasonable fit to the measured differential cross sections of DV⁢𝐽/𝜓⁢P by H1 at the Hadron-Electron Ring Accelerator as well as forward gluon parton distribution functions (PDFs) from JAM22 global analysis. We find that both NLO and nonrelativistic corrections are significant for heavy vector meson productions. Of course, the gluon GPD we obtain still contains considerable freedom in need of inputs from other constraints, particularly in the distribution-amplitude-like region.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
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 OSTI2022

Parametrized process characterization with reduced resource requirements

Quantum process tomography (QPT) is a powerful tool to characterize quantum operations, but it requires considerable resources, making it impractical for more than two-qubit systems. This study proposes an alternative approach that requires significantly fewer resources for unitary process characterization with a built-in method for state preparation and measurement error mitigation. By measuring the quantum process as rotated through the X and Y axes on the Bloch sphere, we can acquire enough information to reconstruct the quantum process matrix χ and measure its fidelity. We test the algorithm's performance against standard QPT using simulated and physical experiments on several IBM quantum processors and compare the resulting process matrices. We demonstrate with numerical experiments that the method can improve gate fidelity via a noise reduction in the imaginary part of the process matrix, along with a stark decrease in the number of experiments needed to perform the characterization.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2020

Parametrization, characterization, and optimization of double-bend achromat cell

We continue the study of single-particle dynamics in a more realistic cell, namely the double-bend achromat with three families of quadrupoles. The cell is parameterized with the optical parameters including the phase advances and the horizontal beta functions at its entry and center. At zero chromaticity, we find that the landscape of the dynamic aperture in the tune plane can be captured by a simple formula constructed from the effective Hamiltonian of 4th order. Furthermore, the optimal dynamic aperture can be found by simply minimizing the third-order driving terms with equal weight in the effective Hamiltonian.

43 PARTICLE ACCELERATORS↗