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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 523 records · Page 29
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Linear beam stability in periodic focusing systems: Krein signature and band structure

The general question of how a beam becomes unstable has been one of the fundamental research topics among beam and accelerator physicists for several decades. In this study, we revisited the general problem of linear beam stability in periodic focusing systems by applying the concepts of Krein signature and band structure. We numerically calculated the eigenvalues and other associated characteristics of one-period maps, and discussed the stability properties of single-particle motions with skew quadrupoles and envelope perturbations in high-intensity beams on an equal footing. In particular, an application of the Krein theory to envelope instability analysis was newly attempted in this study. Finally, the appearance of instabilities is interpreted as the result of the collision between eigenmodes of opposite Krein signatures and the formation of a band gap.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2024

Ultrafast laser triggered electron emission from ultrananocrystalline diamond pyramid tip cathode

Nitrogen-incorporated ultrananocrystalline diamond [(N)UNCD] pyramid tip cathode has been considered as a next-generation high peak current electron source for dielectric laser accelerators as well as other high peak current particle accelerator applications. In this work, we study non-linear photoemission from an (N)UNCD pyramid tip cathode using an ultrafast laser with the pulse length of 150 fs with the central wavelength of 800 nm in the peak intensity range of 10 9 –10 10 W/cm 2 . We demonstrated that as the incident laser intensity increases, the current emitted from the nano-tip first increases as a power function with an exponent of about 5 and then starts to roll over to an exponent of 3. This roll over is attributed to the Coulomb interaction between electrons emitted from the tip also known as the space charge. We also measured the photoemission electron energy spectra that show electrons with energies as high as ~10 eV. Based on the shape of the electron energy spectra, we conclude that the high-energy electrons are thermally emitted electrons due to ultrafast laser heating at the tip of the (N)UNCD pyramid tip cathode.

43 PARTICLE ACCELERATORS↗
DOE OSTI2025

Using "AI Poincare" to analyze non-linear integrable optics

This study dives into the applicability of using automated discovery of conserved quantities in dynamical systems relevant to accelerator physics. Specifically, we explore the performance of AI Poincaré in analyzing numerical trajectory data obtained using the McMillan system of non-linear integrable optics. A comprehensive evaluation of the algorithm's performance is conducted through diverse methodologies. These include the analysis of the estimated number of conserved quantities embedded in a dataset and the deviation of interpolated points on the inferred manifold with respect to points in actually in the dataset. the investigation identifies an optimal range of perturbation distances where the underlying manifold extraction algorithm inside AI Poincaré exhibits optimal performance. Additionally, an improved neural network architecture is proposed based on the observed results. Finally, we apply the algorithm to preliminary experimental data from the Integrable Optics Test Accelerator at Fermilab to successfully infer the number of conserved quantities even in the presence of fast decoherence of the measured signal.

Osmanov, Lazare [Free U. Tbilisi]↗
DOE OSTI2024

Wakefield generation in hydrogen and lithium plasmas at FACET-II: Diagnostics and first beam-plasma interaction results

Plasma wakefield acceleration provides ultrahigh acceleration gradients of tens of GeV/m, providing a novel path toward efficient, compact, TeV-scale linear colliders, and high brightness free electron lasers. Critical to the success of these applications is demonstrating simultaneously high gradient acceleration, high energy transfer efficiency, and preservation of emittance, charge, and energy spread. Experiments at the FACET-II National User Facility at SLAC National Accelerator Laboratory aim to achieve all of these milestones in a single-stage plasma wakefield accelerator, providing a 10 GeV energy gain in a < 1 m plasma with high energy transfer efficiency. Such a demonstration depends critically on diagnostics able to measure emittance with mm mrad accuracy, energy spectra to determine both percent level energy spread, and broadband energy gain and loss, incoming longitudinal phase space, and matching dynamics. This paper discusses the experimental setup at FACET-II, including the incoming beam parameters from the FACET-II linac, plasma sources, and diagnostics developed to meet this challenge. Initial progress on the generation of beam ionized wakes in meter-scale hydrogen gas is discussed as well as commissioning of the plasma sources and diagnostics. Published by the American Physical Society 2024

43 PARTICLE ACCELERATORS↗
DOE OSTI2024

Interpretable, extensible linear and symbolic regression models for charge density prediction using a hierarchy of many-body correlation descriptors

Here, density functional theory (DFT) is routinely used to make electronic structure predictions for high-throughput screening of materials and molecules for technologically relevant areas, like the identification of better catalysts, electronic materials, and drug discovery. However, the DFT formalism is limited by (a) its poor (quadratic-to-quartic) scaling, and (b) the need to perform repeated eigenvalue computations of the electronic Hamiltonian as part of its self-consistent field (SCF) iteration procedure to obtain the converged ground state electron density, ρ (r). Approaches that directly predict ρ (r) of a structure with high accuracy can accelerate conventional SCF calculations and can also be used in linearly scaling methods such as orbital-free DFT. To this end, we present a procedure to predict the ground state electron density of molecular and periodic three-dimensional systems directly from the atomic structure with a particular emphasis on physical interpretability. In our framework, ρ (r) is modeled using many-body correlation descriptors that accurately capture the effects of local atomic arrangements in the neighborhood of a grid point. Our use of a linear regression scheme to fit to charge density data enables transparent analysis of the relative contributions of various types of local atomic correlations. By systematically including increasingly complex correlations, our model is shown to accurately predict ρ (r) for a variety of chemically and electronically diverse systems — amorphous Ge, Al(001) slab, crystalline Ga 2 O 3 , molecular benzene, and polyethylene. We then demonstrate a symbolic regression-based protocol to construct easily computable, interpretable features from lower-order correlations that significantly improves our electron density predictions with effectively no increase in the computational cost.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2025

Extended State Observer-Based Robust Model Predictive Velocity Control for Permanent Magnet Synchronous Motor

This article proposes an extended state observer based robust model predictive velocity control to decrease system prediction error under parameter uncertainties for permanent magnet synchronous motor (PMSM). We develop a new PMSM model that consists of velocity and acceleration to lump the system information and an external disturbance into a disturbance. The extended state observer (ESO) is designed to estimate the velocity, acceleration, and disturbance. By estimating the state variables and disturbance using the ESO, the model predictive control (MPC) finds the optimal control input by predicting future system behavior. Additionally, the direct current controller is designed so that the direct current converges to zero. Because the proposed method is not designed based on the cascade structure from the viewpoint of velocity control, the optimization control for the velocity and currents can be defined. Thus, the proposed method is robust against external disturbances and parameter uncertainties owing to feedback linearization, state feedback, and ESO-based MPC using the acceleration PMSM model. The proposed control algorithm was experimentally verified and it showed improved velocity tracking performance compared with ESO-based MPC using the conventional PMSM model.

42 ENGINEERING↗
DOE OSTI2023

Linearization of an Electron Beam’s Longitudinal Phase Space Using a Hollow-Channel Plasma

The removal of undesired beam nonlinear energy chirp (time-energy correlation) or linearization of the beam longitudinal phase space (LPS) is crucial for high-brightness linac-based scientific applications, such as x-ray free-electron lasers. In this paper, we propose that a low-density hollow channel plasma can be used as a near-ideal passive linearizer to significantly linearize the beam LPS and, at the same time, preserve the beam emittance. Physically, the passage of the beam through the hollow plasma channel excites a strong quasicosinoidal longitudinal wakefield that acts to mitigate the beam nonlinear energy chirp by superimposing a reverse chirp on the beam. The theoretical analyses and large-scale three-dimensional start-to-end simulations confirm that the beam longitudinal phase space can be almost completely linearized without noticeable beam emittance growth. In conclusion, application of such a near-ideal linearizer may significantly improve the performance of numerous accelerator-based applications.

43 PARTICLE ACCELERATORS↗
DOE OSTI2020

DiHydrogen

DiHydrogen is the second version of the Hydrogen fork of the well-known distributed linear algebra library, Elemental. DiHydrogen is a GPU-accelerated distributed multilinear algebra interface with a particular emphasis on the needs of the scalable distributed deep learning training and inference. DiHydrogen is part of the Livermore Big Artificial Neural Network (LBANN) software stack.

Maruyama, Naoya↗
DOE OSTI2025

OpenFerro v0.1.0

OpenFerro is a Python package for on-lattice atomistic dynamics simulation of ferroic materials. OpenFerro is based on JAX, a high-performance linear algebra package supporting auto-differentiation and GPU acceleration. OpenFerro is designed to minimize the effort required to build on-lattice Hamiltonian models, and to perform molecular dynamics (MD) and Landau-Lifshitz-Gilbert simulations. Unlike existing codes, OpenFerro provides a unified interface to model different types of local order parameters.

Xie, Pinchen [Lawrence Berkeley National Laborator↗
DOE OSTI2023

Search for Isoscalar Resonances in yp ¿ wnp AT GLUEX

The GlueX experiment has produced the world?s largest statistics for peripheral photoproduction of mesons with a goal to search for exotic mesons. This document presents results on the search for possible intermediate resonances which decay to ??. Little is known for the states seen in this decay channel such as the ?(1420) and ?(1650). This channel also allows to search for the never seen exotics JPC = 0??, 2+?, and the elusive 2??. The ?, in this reaction, is identified via the ?+???0 decay mode and the ? and ?0 are identified via the decay to 2?. Data from the GlueX Phase I was analyzed. This data was obtained at the Thomas Jefferson National Accelerator Facility utilizing the GlueX detector and a tagged linearly polarized photon beam incident on a liquid hydrogen target. A model using the expected major wave contributors, 1+? and 1??, shows two structure in the region where the ?(1420) and ?(1650) are expected. A third structure is seen at threshold with the quantum numbers of an h1 resonance, however, more studies are needed to assert the nature of this structure. Instabilities on the m = ±1 were shown to come from mathematical ambiguities. This only occurs between the m = 1 and m = ?1 projections when both reflectivities are present in close amount. Expanding the intensity equation for a single wave allows to extract coefficients that can help describe the data. A tangent study is also presented in this document which consists on a direct fit to the lineshape of an isolated N(1535). This baryonic resonance is well established, but its Breit-Wigner parameters have only been obtained from partial wave analysis. In contrast, this study obtained the Breit-Wigner parameters using a direct fit and finds the value of the width to be 70.5± 9.2 MeV; this is half of the accepted value.

Barriga, Edmundo↗
DOE OSTI2021

Evaluation of Two-Dimensional to One-Dimensional Site Response for Idaho National Laboratory

We perform two-dimensional (2D) site response analyses accounting for spatial variability of soil properties and subsurface geometry of the Eastern Snake River Plane (ESRP), and quantify their effects on ground surface motion relative to one-dimensional (1D) site response analyses at the Idaho National Laboratory (INL). We first present the development of random field idealizations of the repeated basalt lava flows, heterogeneously inter-layered with sediments, from seismic velocity data collected over four decades in the ESRP. Using realizations of the stochastic fields mapped on 2D deterministic finite element models, we perform 2D viscoelastic and equivalent-linear wave propagation simulations, and quantify the mean and variance of site response aggravation factors, defined as the response spectral ratio of 2D to 1D analyses on the ground surface. Results are shown to be insensitive to the constitutive material behavior considered here, for strains induced by rock outcrop peak ground acceleration (PGA) as high as 0.7g: viscoelastic and equivalent-linear analyses predict peak mean 2D/1D aggravation factor 1.05 at period T=0.075 sec (i.e. the 2D response spectrum is 5% higher than the corresponding 1D at that period, on average), which corresponds to the wavelength of the horizontal correlation length of the random field (50m). For periods longer than the fundamental period of the site (here, T 1 =0.3125 sec), the propagating wavelengths are too long to be affected by the 1D site response and the aggravation factor becomes equal to 1. The standard deviation of the natural logarithms of the 2D/1D aggravation factors is ~0.15 for periods shorter than the fundamental period of the site, and decays thereafter at a steady rate.

58 GEOSCIENCES↗
DOE OSTI2023

Gauge-invariant gravitational waves in matter beyond linearized gravity

Modeling the propagation of gravitational waves (GWs) in media other than vacuum is complicated by the gauge freedom of linearized gravity in that, once nonlinearities are taken into consideration, gauge artifacts can cause spurious acceleration of the matter. To eliminate these artifacts, we propose how to keep the theory of dispersive GWs gauge-invariant beyond the linear approximation and, in particular, obtain an unambiguous gauge-invariant expression for the energy–momentum of a GW in a dispersive medium. Using analytic tools from plasma physics, we propose an exactly gauge-invariant 'quasilinear' theory, in which GWs are governed by linear equations and also affect the background metric on scales large compared to their wavelength. As a corollary, the gauge-invariant geometrical optics of linear dispersive GWs in a general background is formulated. As an example, we show how the well-known properties of vacuum GWs are naturally and concisely yielded by our theory in a manifestly gauge-invariant form. We also show how the gauge invariance can be maintained within a given accuracy to an arbitrary order in the GW amplitude. These results are intended to form a physically meaningful framework for studying dispersive GWs in matter.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2024

Nonlinear manifold reduced order model

Traditional linear subspace reduced order models (LS-ROMs) are able to accelerate physical simulations in which the intrinsic solution space falls into a subspace with a small dimension, i.e., the solution space has a small Kolmogorov n-width. However, for physical phenomena not of this type, e.g., any advection-dominated flow phenomena such as in traffic flow, atmospheric flows, and air flow over vehicles, a lowdimensional linear subspace poorly approximates the solution. To address cases such as these, we have developed a fast and accurate physics-informed neural network ROM, namely nonlinear manifold ROM (NM-ROM), which can better approximate high-fidelity model solutions with a smaller latent space dimension than the LS-ROMs. Our software takes advantage of the existing numerical methods that are used to solve the corresponding full order models. The efficiency is achieved by developing a hyper-reduction technique in the context of the NM-ROM. Numerical results show that neural networks can learn a more efficient latent space representation on advection-dominated data from 1D and 2D Burgers' equations. A speedup of up to 2.6 for 1D Burgers' and a speedup of 11.7 for 2D Burgers' equations are achieved with an appropriate treatment of the nonlinear terms through a hyper-reduction technique.

Choi, Youngsoo↗
DOE OSTI2020

Polymer Chain Diffusion in All-Polymer Nanocomposites: Confinement vs Chain Acceleration

All-polymer nanocomposites, in which soft, polymer-based nanoparticles are dispersed in the polymer matrix, have received great interest lately due to their potential use in a range of applications, including drug delivery and self-healing materials. However, the impact of this new class of nanoparticles on the dynamics of a linear polymer matrix in an all-polymer nanocomposite is still largely unknown. In this work, we report that the addition of polystyrene soft nanoparticles accelerates the diffusion of high molecular weight linear PS chains over a range of nanoparticle loadings. Our results show that at nanoparticle loadings below 1%, the diffusion of the linear matrix increases, presumably via a constraint release mechanism. At loadings above 1%, the increase in diffusion is mitigated by confinement effects of the nanoparticles. Thus, the response of these all-polymer nanocomposites is dominated by the balance of entropic confinement of the chain, which slows diffusion and a constraint release mechanism that speeds up the diffusion. However, the diffusion of the linear chain in the all-polymer nanocomposite is faster than that of the same chain in the melt at all loadings, diverging from the behavior of most nanocomposites with hard, impenetrable nanoparticles. Thus, the mechanism that accelerates the chain diffusion dominates in these systems. This behavior is unusual and fundamentally different than what has been reported for nanocomposites with hard inorganic nanoparticles, indicating that new perspectives are needed for these materials where the control of loading can either accelerate or decelerate the dynamics of the matrix in a distinctive manner.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

Linear Modelling from Betatron Phase Measurements at the Fermilab Recycler NOvA ring

Utilizing the measurement of coherent betatron oscillation phase has emerged as fast and precise approach for identifying and rectifying errors in implementing a desired lattice in CESR (Cornell Electron Storage Ring). One key advantage of betatron phase measurement over β measurement is its sensitivity to phase variations between widely separated points. This methodology has been successfully implemented for the Recycler NOvA Ring at Fermilab, with the adoption of TAO (Tool for Accelerator Optics) developed by Cornell University. By employing this technique, a linear model of the Recycler NOvA ring is established, enabling the correction of quadrupole errors.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2021

A fast and accurate physics-informed neural network reduced order model with shallow masked autoencoder

Traditional linear subspace reduced order models (LS-ROMs) are able to accelerate physical simulations in which the intrinsic solution space falls into a subspace with a small dimension, i.e., the solution space has a small Kolmogorov n-width. However, for physical phenomena not of this type, e.g., any advection-dominated flow phenomena such as in traffic flow, atmospheric flows, and air flow over vehicles, a low-dimensional linear subspace poorly approximates the solution. To address cases such as these, we have developed a fast and accurate physics-informed neural network ROM, namely nonlinear manifold ROM (NM-ROM), which can better approximate high-fidelity model solutions with a smaller latent space dimension than the LS-ROMs. Our method takes advantage of the existing numerical methods that are used to solve the corresponding full order models. The efficiency is achieved by developing a hyper-reduction technique in the context of the NM-ROM. Numerical results show that neural networks can learn a more efficient latent space representation on advection-dominated data from 1D and 2D Burgers' equations. A speedup of up to 2.6 for 1D Burgers' and a speedup of 11.7 for 2D Burgers' equations are achieved with an appropriate treatment of the nonlinear terms through a hyper-reduction technique. Lastly, a posteriori error bounds for the NM-ROMs are derived that take account of the hyper-reduced operators.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

Nonlinear manifold-based reduced order model

Traditional linear subspace reduced order models (LS-ROMs) are able to accelerate physical simulations, in which the intrinsic solution space falls into a subspace with a small dimension, i.e., the solution space has a small Kolmogorov n-width. However, for physical phenomena not of this type, e.g., any advection-dominated flow phenomena such as in traffic flow, atmospheric flows, and air flow over vehicles, a low-dimensional linear subspace poorly approximates the solution. To address cases such as these, we have developed a fast and accurate physicsinformed neural network ROM, namely nonlinear manifold ROM (NM-ROM), which can better approximate high-fidelity model solutions with a smaller latent space dimension than the LSROMs. Our method takes advantage of the existing numerical methods that are used to solve the corresponding full order models. The efficiency is achieved by developing a hyper-reduction technique in the context of the NM-ROM. Numerical results show that neural networks can learn a more efficient latent space representation on advection-dominated data from 1D and 2D Burgers’ equations. A speedup of up to 2.6 for 1D Burgers’ and a speedup of 11.7 for 2D Burgers’ equations are achieved with an appropriate treatment of the nonlinear terms through a hyper-reduction technique. Finally, a posteriori error bounds for the NM-ROMs are derived that take account of the hyper-reduced operators.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

A digital twin based on OpenFAST linearizations for real-time load and fatigue estimation of land-based turbines

Monitoring a wind turbine requires intensive instrumentation, which would be too cost-prohibitive to deploy to an entire wind plant. This work presents a technique that uses readily available measurements to estimate signals that would otherwise require additional instrumentation. This study presents a digital twin concept with a focus on estimating wind speed, thrust, torque, tower-top position, and loads in the tower using supervisory control and data acquisition (SCADA) measurements. The model combines a linear state-space model obtained using OpenFAST linearizations, a wind speed estimator, and a Kalman filter algorithm that integrates measurements with the state model to perform state estimations. The measurements are: top acceleration, generator torque, pitch, and rotational speed. The article extends previous work that derived the linear state-space model using a different method. The new implementation, based on OpenFAST linearization capability, allows for a systematic extension of the method to more states, inputs, outputs, and to the offshore environment. Results from the two methods are compared, and the validation is made with additional measurements using the GE 1.5-MW turbine located at the National Renewable Energy Laboratory test site. Real-time damage equivalent loads of the tower bottom moment are estimated with an average accuracy of approximately 10%. Overall, the results from this proof of concept are encouraging, and further application of the model will be considered.

17 WIND ENERGY↗