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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 397 records · Page 22
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

3D Cell-centered hydrodynamics with subscale closure model and multi-material remap

We extend a higher-order finite volume cell-centered hydrodynamic (CCH) formulation to include an interface-aware subscale closure model and a multi-material remap for simulating 3D compressible hydrodynamic problems within an arbitrary Lagrangian-Eulerian (ALE) framework. This CCH formulation involves a multidirectional approximate Riemann solution using quadratic polynomial reconstructions of the stress tensor and the velocity. At the subscale level, we determine pair-wise material interactions by solving a distinct approximate Riemann problem at the common interface, using the volume of fluids (VOF) method to find the interface. Material interactions are constrained to ensure smooth pressure equilibration among materials. The accuracy and robustness of the ALE method is demonstrated by simulating a suite of 3D Cartesian multi-material problems covering both gas and solid dynamics, where each test case has two or more materials.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2021

On the use of neural networks for energy reconstruction in high-granularity calorimeters

We contrasted the performance of deep neural networks — Convolutional Neural Network (CNN) and Graph Neural Network (GNN) — to current state of the art energy regression methods in a finely 3D-segmented calorimeter simulated by GEANT4. This comparative benchmark gives us some insight to assess the particular latent signals neural network methods exploit to achieve superior resolution. A CNN trained solely on a pure sample of pions achieved substantial improvement in the energy resolution for both single pions and jets over the conventional approaches. It maintained good performance for electron and photon reconstruction. We also used the Graph Neural Network (GNN) with edge convolution to assess the importance of timing information in the shower development for improved energy reconstruction. We implement a simple simulation based correction to the energy sum derived from the fraction of energy deposited in the electromagnetic shower component. This serves as an approximate dual-readout analogue for our benchmark comparison. Although this study does not include the simulation of detector effects, such as electronic noise, the margin of improvement seems robust enough to suggest these benefits will endure in real-world application. We also find reason to infer that the CNN/GNN methods leverage latent features that concur with our current understanding of the physics of calorimeter measurement.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2023

Multi-dimensional incoherent Thomson scattering system in PHAse Space MApping (PHASMA) facility

A multi-dimensional incoherent Thomson scattering diagnostic system capable of measuring electron temperature anisotropies at the level of the electron velocity distribution function (EVDF) is implemented on the PHAse Space MApping facility to investigate electron energization mechanisms during magnetic reconnection. This system incorporates two injection paths (perpendicular and parallel to the axial magnetic field) and two collection paths, providing four independent EVDF measurements along four velocity space directions. For strongly magnetized electrons, a 3D EVDF comprised of two characteristic electron temperatures perpendicular and parallel to the local magnetic field line is reconstructed from the four measured EVDFs. As a result, validation of isotropic electrons in a single magnetic flux rope and a steady-state helicon plasma is presented.

47 OTHER INSTRUMENTATION↗
DOE OSTI2020

Three dimensions, two microscopes, one code: Automatic differentiation for x-ray nanotomography beyond the depth of focus limit

Conventional tomographic reconstruction algorithms assume that one has obtained pure projection images, involving no within-specimen diffraction effects nor multiple scattering. Advances in x-ray nanotomography are leading toward the violation of these assumptions, by combining the high penetration power of x-rays, which enables thick specimens to be imaged, with improved spatial resolution that decreases the depth of focus of the imaging system. We describe a reconstruction method where multiple scattering and diffraction effects in thick samples are modeled by multislice propagation and the 3D object function is retrieved through iterative optimization. We show that the same proposed method works for both full-field microscopy and for coherent scanning techniques like ptychography. Our implementation uses the optimization toolbox and the automatic differentiation capability of the open-source deep learning package TensorFlow, demonstrating a straightforward way to solve optimization problems in computational imaging with flexibility and portability.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2024

Subject-specific multi-scale modeling of the fate of inhaled aerosols

Determining the fate of inhaled aerosols in the respiratory system is essential in assessing the potential toxicity of inhaled airborne materials, responses to airborne pathogens, or in improving inhaled drug delivery. The availability of high-resolution clinical lung imaging and advances in the reconstruction of lung airways from CT images have led to the development of subject-specific in-silico 3D models of aerosol dosimetry, often referred to as computational fluid-particle-dynamics (CFPD) models. As CFPD models require extensive computing resources, they are typically confined to the upper and large airways. These models can be combined with lower-dimensional models to form multiscale models that predict the transport and deposition of inhaled aerosols in the entire respiratory tract. Understanding where aerosols deposit is only the first of potentially several key events necessary to predict an outcome, being a detrimental health effect or a therapeutic response. To that end, multiscale approaches that combine CFPD with physiologically-based pharmacokinetics (PBPK) models have been developed to evaluate the absorption, distribution, metabolism, and excretion (ADME) of toxic or medicinal chemicals in one or more compartments of the human body. CFPD models can also be combined with host cell dynamics (HCD) models to assess regional immune system responses. Here, this paper reviews the state of the art of these different multiscale approaches and discusses the potential role of personalized or subject-specific modeling in respiratory health.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2024

Accelerating Multivariate Functional Approximation Computation with Domain Decomposition Techniques⋆

Modeling large datasets through Multivariate Functional Approximations (MFA) provide an elegant way to handle many visualization and scientific analysis workflows. The process necessitates scalable data partitioning methods to compute MFA representations efficiently without compromising the accuracy or continuity of the reconstructed solution. We propose a domain -decomposed method for computing the MFA with B -spline bases, which reduces the total work per task and uses a restricted Additive Schwarz (RAS) method to converge the control point data degrees -of -freedom along subdomain boundaries. We provide an in-depth analysis of the parallel approach with domain decomposition solvers, aiming to minimize local subdomain error residuals and recover high -order continuity at subdomain interfaces with appropriate choices of knot overlaps. The communication cost, determined by the overlap regions in the RAS implementation, is optimized to recover the numerical error profile of the single subdomain case. Our proposed method stands in contrast to previous methods, which typically only recover either C 0 or at best C 1 continuity for arbitrary B -spline degree expansions, or those that require post -processing to blend discontinuities in the reconstructed data. We demonstrate the effectiveness of our approach using analytical and real -world datasets in 1D, 2D, and 3D through both strong and weak scaling studies. The performance results indicate that the overall cost of computing the approximation is directly proportional to the underlying nearest -neighbor communication implementation, and is only weakly dependent on the overlap region size that determines the size of the messages. This finding underscores the efficiency and scalability of our proposed method, making it a promising solution for handling large datasets in scientific workflows.

additive Schwarz solvers↗
DOE OSTI2020

Three-dimensional super line-localization in low signal-to-noise microscope images via prior-apprised unsupervised learning (PAUL)

Biological processes such as processive enzyme turnover and intracellular cargo tracking involve the dynamic motion of a small "article" along a curvilinear biopolymer track. To understand these processes that occur across multiple length and time scales, one must acquire both the trajectory of the particle and the position of the track along which it moves, possibly by combining high-resolution single-particle tracking with conventional microscopy. Yet, usually there is a significant resolution mismatch between these modalities: while the tracked particle is localized with a precision of 10 nm, the image of the surroundings is limited by optical difraction, with 200 nm lateral and 500 nm axial resolutions. Compared to the particle's trajectory, the surrounding curvilinear structure appears as a blurred and noisy image. This disparity in the spatial resolutions of the particle trajectory and the surrounding curvilinear structure image makes data reconstruction, as well as interpretation, particularly challenging. Analysis is further complicated when the curvilinear structures are oriented arbitrarily in 3D space. Here, we present a prior-apprised unsupervised learning (PAUL) approach to extract information from 3D images where the underlying features resemble a curved line such as a filament or microtubule. This three-stage framework starts with a Hessian-based feature enhancement, which is followed by feature registration, where local line segments are detected on repetitively sampled subimage tiles. In the final stage, statistical learning, segments are clustered based on their geometric relationships. Principal curves are then approximated from each segment group via statistical tools including principal component analysis, bootstrap and kernel transformation. This procedure is characterized on simulated images, where sub-voxel medium deviations from true curves have been achieved. The 3D PAUL approach has also been implemented for successful line localization in experimental 3D images of gold nanowires obtained using a multifocal microscope. Lastly, this work not only bridges the resolution gap between two microscopy modalities, but also allows us to conduct 3D super line-localization imaging experiments, without using super-resolution techniques.

42 ENGINEERING↗
DOE OSTI2023

Fast 2D Bicephalous Convolutional Autoencoder for Compressing 3D Time Projection Chamber Data

High-energy large-scale particle colliders produce data at high speed in the order of 1 terabytes per second in nuclear physics and petabytes per second in high energy physics. Developing real-time data compression algorithms to reduce such data at high throughput to fit permanent storage has drawn increasing attention. Specifically, at the newly constructed sPHENIX experiment at the Relativistic Heavy Ion Collider (RHIC), a time projection chamber is used as the main tracking detector, which records particle trajectories in a volume of three-dimensional (3D) cylinder. The resulting data are usually very sparse with occupancy around 10.8%. Such sparsity presents a challenge to conventional learning-free lossy compression algorithms, such as SZ, ZFP, and MGARD. The 3D convolutional neural network (CNN)-based approach, Bicephalous Convolutional Autoencoder (BCAE), outperforms traditional methods both in compression rate and reconstruction accuracy. BCAE can also utilize the computation power of graphical processing units suitable for deployment in a modern heterogeneous highperformance computing environment. This work introduces two BCAE variants: BCAE++ and BCAE-2D. BCAE++ achieves a 15% better compression ratio and a 77% better reconstruction accuracy measured in mean absolute error compared with BCAE. BCAE-2D treats the radial direction as the channel dimension of an image, resulting in a 3× speedup in compression throughput. In addition, we demonstrate an unbalanced autoencoder with a larger decoder can improve reconstruction accuracy without significantly sacrificing throughput. Lastly, we observe both the BCAE++ and BCAE-2D can benefit more from using half-precision mode in throughput (76 - 79% increase) without loss in reconstruction accuracy. The source code and links to data and pretrained models can be found at https://github.com/BNL-DAQ-LDRD/NeuralCompression_v2

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2022

Analysis of Defects in Metal Additive Manufacturing with Augmented Data Generation

Laser powder bed fusion (LPBF) is a method of additive manufacturing (AM) that selectively melts and fuses together microscopic metallic powder. LPBF offers the benefit of producing custom structures out of high strength metals that can be difficult to fabricate with conventional methods. The challenge of LPBF is that 3D printed structures often have internal pores due to process flaws. Pulsed thermal tomography (PTT) is a method for reconstructing the depth profile of materials, allowing the visualization internal voids in solids. In prior work, we developed a convolutional neural network (CNN) which, having been trained on simulated 2D PTT images of subsurface elliptical defects, was able to classify the semi-major radii, semi-minor radii, and angular orientation of the best-fit ellipses in previously unseen PTT images. The unseen PTT images contained subsurface irregular defect shapes imported from scanning electron microscopy (SEM) images of metallic LPBF-printed specimens. Training the CNN on irregular defect shapes instead of on elliptical shapes would make the resulting classifications more descriptive of actual defect shapes. However, this requires a much higher volume of SEM images of material defects, which are difficult to obtain because of random occurrence of defects in LPBF. To address this challenge, we developed a generative adversarial network (GAN) to augment the existing dataset of SEM defect images. The GAN model is demonstrated to create novel yet realistic defect shapes that can be used as input for simulated PTT images to train CNN.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Improving ICARUS track reconstruction algorithms

The ICARUS experiment is part of the Short-Baseline Neutrino program at Fermilab. Its primary objective is to explore the possible existence of sterile neutrinos in the O(1 eV) mass range and to clarify the anomalies observed in the Liquid Scintillator Neutrino Detector and MiniBooNE experiments. The ICARUS-T600 detector is a Liquid Argon Time Projection Chamber, capable of producing high-resolution 3D images and precise calorimetric measurements of ionizing particles. This technology allows for a detailed study of neutrino interactions across a broad energy range, from a few keV to several hundred GeV. The track reconstruction is achieved through a software framework that applies a series of pattern recognition algorithms, transforming raw detector signals into fully reconstructed event topologies. This process involves identifying interaction vertices, particle tracks, and electromagnetic showers within the TPC. However, in certain cases, these algorithms may mistakenly break a single particle track into several shorter segments, interpreting each as a distinct particle. Since track length is used to estimate the particle's energy, such fragmentation can result in an energy underestimation of several hundred MeV. Furthermore, when a track is split into multiple segments, the particle identification (which relies on analyzing the energy loss as a function of the residual range) may fail, potentially leading to the loss of the entire event. To mitigate this problem, we have developed a dedicated algorithm designed to identify and reconnect (“stitch”) the tracks that were erroneously divided into multiple segments.

Ricci, Alessandro Maria [Pisa U.; INFN, Pisa] (ORC↗
DOE OSTI2020

Simulation-driven optimization of high-order meshes in ALE hydrodynamics

Here we propose tools for high-order mesh optimization and demonstrate their benefits in the context of multi-material Arbitrary Lagrangian-Eulerian (ALE) compressible shock hydrodynamic applications. The mesh optimization process is driven by information provided by the simulation which uses the optimized mesh, such as shock positions, material regions, known error estimates, etc. These simulation features are usually represented discretely, for instance, as finite element functions on the Lagrangian mesh. The discrete nature of the input is critical for the practical applicability of the algorithms we propose and distinguishes this work from approaches that strictly require analytical information. Our methods are based on node movement through a high-order extension of the Target-Matrix Optimization Paradigm (TMOP). The proposed formulation is fully algebraic and relies only on local Jacobian matrices, so it is applicable to all types of mesh elements, in 2D and 3D, and any order of the mesh. We discuss the notions of constructing adaptive target matrices and obtaining their derivatives, reconstructing discrete data in intermediate meshes, node limiting that enables improvement of global mesh quality while preserving space-dependent local mesh features, and appropriate normalization of the objective function. The adaptivity methods are combined with automatic ALE triggers that can provide robustness of the mesh evolution and avoid excessive remap procedures. The benefits of the new high-order TMOP technology are illustrated on several simulations performed in the high-order ALE application BLAST.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Hybrid achromatic microlenses with high numerical apertures and focusing efficiencies across the visible

Abstract Compact visible wavelength achromats are essential for miniaturized and lightweight optics. However, fabrication of such achromats has proved to be exceptionally challenging. Here, using subsurface 3D printing inside mesoporous hosts we densely integrate aligned refractive and diffractive elements, forming thin high performance hybrid achromatic imaging micro-optics. Focusing efficiencies of 51–70% are achieved for 15μm thick, 90μm diameter, 0.3 numerical aperture microlenses. Chromatic focal length errors of less than 3% allow these microlenses to form high-quality images under broadband illumination (400–700 nm). Numerical apertures upwards of 0.47 are also achieved at the cost of some focusing efficiency, demonstrating the flexibility of this approach. Furthermore, larger area images are reconstructed from an array of hybrid achromatic microlenses, laying the groundwork for achromatic light-field imagers and displays. The presented approach precisely combines optical components within 3D space to achieve thin lens systems with high focusing efficiencies, high numerical apertures, and low chromatic focusing errors, providing a pathway towards achromatic micro-optical systems.

47 OTHER INSTRUMENTATION↗
DOE OSTI2024

One-Step Synthesis and Operando Electrochemical Impedance Spectroscopic Characterization of Heterostructured MoP–Mo 2 N Electrocatalysts for Stable Hydrogen Evolution Reaction

Here this study presents a novel synthesis of self-standing MoP and Mo 2 N heterostructured electrocatalysts with enhanced stability and catalytic performance. Facilitated by the controlled phase and interfacial microstructure, the seamless structures of these catalysts minimize internal resistivity and prevent local corrosion, contributing to increased stability. The chemical synthesis proceeds with etching step to activate the surface, followed by phosphor-nitriding in a chemical vapor deposition chamber to produce MoP-Mo 2 N@Mo heterostructured electrocatalysts. X-ray diffraction analyses confirm the presence of MoP, Mo 2 N, and Mo phases in the electrocatalyst. Morphology studies using scanning electron microscopy characterizes the hierarchical growth of structures, indicating successful formation of the heterostructure. X-ray photoelectron spectroscopy (XPS) analyses of the as-synthesized and post-catalytic activity samples reveal the chemical shift in terms of binding energy (BE) of Mo 3d XPS peak, especially after catalytic activity. The XPS BE shifts attributed to changes in oxidation state, electron transfer, and surface reconstruction during catalysis. Electrochemical evaluation of the catalysts demonstrates the superior performance of the MoP-Mo 2 N@Mo heterostructured catalyst in hydrogen evolution reactions (HER), with lower overpotentials and enhanced Tafel slopes. Stability tests reveal changes in double layer capacitance over time, suggesting surface reconstruction and increased active surface area during catalysis. Operando electrochemical impedance spectroscopy (EIS) further elucidates the dynamic changes in resistance and charge transfer during HER. Overall, comprehensive understanding of the synthesis, characterization, and electrochemical behavior of the developed MoP-Mo 2 N@Mo heterostructured electrocatalyst as presented in this paper highlights their potential utilization in sustainable energy applications.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

First look at neutron emission shape characteristics of ignition hotspots at the National Ignition Facility (invited)

The nuclear imaging system has been capturing neutron images of inertial confinement fusion (ICF) driven implosions for over a decade at the National Ignition Facility. This imaging system has evolved from one to three nearly orthogonal lines-of-sight, allowing for the study of three-dimensional shape characteristics of ignition shots. Limited-view tomography algorithms help visualize the burning hotspot in 3D and assess neutron source geometry using Legendre mode parameters. With its neutron, gamma-ray, and x-ray image reconstruction capabilities, NIS has provided critical insight into mechanisms that have limited implosion performance, such as fill tube diameter for ignition-type targets. This comprehensive diagnostic suite opens a window into the shape characteristics of ignition shots and how symmetry affects ICF implosion performance. In more recent ignition shots, neutron yields have visibly increased. Analyzing the shape and size of the reconstructed neutron source has shown an expansion of the burn volume, which is indicative of more efficient alpha heating during the implosion process.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2020

Hydrodynamic instabilities and heat transfer characteristics in the duct flow of a fluid in the supercritical thermodynamic regime

The behavior of fluids at supercritical thermodynamic conditions is inherently complex due to large variations in thermodynamic and transport properties. Recent numerical and experimental investigations illustrate ongoing interest for these fluids, especially supercritical CO 2 and supercritical water, for a variety of applications. For example, supercritical water reactors (SCWR) operate in this extreme condition of high-pressure and temperature, resulting in highly dynamic flow fields and unexpected heat transfer regimes. The potential heat transfer benefits in this regime are directly associated with the extreme variations in thermodynamic and transport properties, which occur at, and above, the critical point. This work characterizes the hydrodynamic instabilities that arise for fluids at supercritical thermodynamic conditions when buoyancy forces are significant. Two specific configurations are considered, a natural convection cavity flow, and a mixed convection, heated, horizontal channel flow. Natural convection flow in a cavity is a classical configuration with expected behavior below the critical point. This configuration aids in characterizing the effect of the variable properties in the supercritical thermodynamic regime. Further, limited studies in the existing literature have been conducted for low-Reynolds and intermediate-Rayleigh numbers, mixed-convection channel flows for supercritical water, which is the focus of the channel flow configuration. To investigate the thermally driven hydrodynamic instabilities in this regime, a high-order fully-implicit numerical method is used. Such strong variations in thermophysical properties (in particular, density) are difficult to simulate and an altogether compressible framework is needed. Therefore, the compressible Navier-Stokes equations are solved without any additional assumptions. The fully implicit, high-order in space and time, reconstructed discontinuous Galerkin method as implemented within the multi-physics code called ALE3D (Arbitrary Lagrangian and Eulerian in 2D and 3D), developed at Lawrence Livermore National Laboratory (LLNL), is used. This fully implicit, L-stable method accurately captures the compressible nature of the ow in the limit of very low Mach number. It has been widely accepted that above the critical point, only one phase is observed. However, recent research has indicated the existence of the distinct gas-like and liquid-like regions separated by the Widom line, the locus of the maxima of the specific heat. Along the Widom line, density decreases 6-fold, viscosity drops by a factor of 2, while specific heat spikes by an order of magnitude. These variations, specifically in density and viscosity, produce a thick pseudo-interface and flow dynamics behavior akin to film boiling. A pseudo-film at the heated wall of the cavity and the horizontal channel is observed where buoyancy forces induce mixing through the specific configurations. Further the local Rayleigh and Richardson numbers provide maps of the flow field and the buoyancy forces driving the microscopic mixing. In the first chapter, I describe a background of supercritical fluid and the various applications. The second chapter focuses on the mathematical model and numerical method used for simulations, where a description of the equation of state for supercritical water is described. The third chapter focuses on the natural convection cavity with a heated bottom wall. In this cavity a gas-like and a liquid-like flow within the supercritical thermodynamic regime are observed. The fourth chapter focuses on a forced convection, horizontal channel, distinguishing between the gas-like, liquid-like, and mixed flow regimes. Mixed convection flow, with the addition of gravitational forces in the horizontal channel show the influence of variable properties on the hydrodynamic development, heat transfer, and rising instabilities. The last chapter of this research focuses on characterizing the unstable hydrodynamics through time-averaging processes and analysis of the movement of energy through the developing plumes.

42 ENGINEERING↗
DOE OSTI2023

A hybrid 3D/2D field response calculation for liquid argon detectors with PCB based anode plane

Liquid Argon Time Projection Chamber (LArTPC) technology is commonly utilized in neutrino detector designs. It enables detailed reconstruction of neutrino events with high spatial precision and low energy threshold. Its field response (FR) model describes the time-dependent electric currents induced in the anode-plane electrodes when ionization electrons drift nearby. An accurate and precise FR is a crucial input to LArTPC detector simulations and charge reconstruction. Established LArTPC designs have been based on parallel wire planes. It allows accurate and computationally economic two-dimensional (2D) FR models utilizing the translational symmetry along the direction of the wires. Recently, novel LArTPC designs utilize electrodes formed on printed circuit board (PCB) in the shape of strips with through holes. The translational symmetry is no longer a good approximation near the electrodes and a new FR calculation that employs regions with three dimensions (3D) has been developed. Extending the 2D models to 3D would be computationally expensive. Fortuitously, the nature of strips with through holes allows for a computationally economic approach based on the finite-difference method (FDM). In this paper, we present a new software package pochoir that calculates LArTPC field response for these new strip-based anode designs. This package combines 3D calculations in the volume near the electrodes with 2D far-field solutions to achieve fast and precise field response computation. We apply the resulting FR to simulate and reconstruct samples of cosmic-ray muons and 39 Ar decays from a Vertical Drift (VD) detector prototype operated at CERN. We find the difference between real and simulated data within 5%. Current state-of-the-art LArTPC software requires a 2D FR which we provide by averaging over one dimension and estimate that variations lost in this average are smaller than 7%.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2020

Snapshot multifocal light field microscopy

Light field microscopy (LFM) is an emerging technology for high-speed wide-field 3D imaging by capturing 4D light field of 3D volumes. However, its 3D imaging capability comes at a cost of lateral resolution. In addition, the lateral resolution is not uniform across depth in the light field dconvolution reconstructions. To address these problems, here, we propose a snapshot multifocal light field microscopy (MFLFM) imaging method. The underlying concept of the MFLFM is to collect multiple focal shifted light fields simultaneously. We show that by focal stacking those focal shifted light fields, the depth-of-field (DOF) of the LFM can be further improved but without sacrificing the lateral resolution. Also, if all differently focused light fields are utilized together in the deconvolution, the MFLFM could achieve a high and uniform lateral resolution within a larger DOF. We present a house-built MFLFM system by placing a diffractive optical element at the Fourier plane of a conventional LFM. The optical performance of the MFLFM are analyzed and given. Both simulations and proof-of-principle experimental results are provided to demonstrate the effectiveness and benefits of the MFLFM. We believe that the proposed snapshot MFLFM has potential to enable high-speed and high resolution 3D imaging applications.

He, Kuan↗
DOE OSTI2023

Effects of Coronal Magnetic Field Configuration on Particle Acceleration and Release during the Ground Level Enhancement Events in Solar Cycle 24

Ground level enhancements (GLEs) are extreme solar energetic particle (SEP) events that are of particular importance in space weather. In solar cycle 24, two GLEs were recorded on 2012 May 17 (GLE 71) and 2017 September 10 (GLE 72), respectively, using a range of advanced modern instruments. Here we conduct a comparative analysis of the two events by focusing on the effects of large-scale magnetic field configuration near active regions on particle acceleration and release. Although the active regions are both located near the western limb, temporal variations of SEP intensities and energy spectra measured in situ display different behaviors at early stages. By combining a potential field model, we find the coronal mass ejection (CME) in GLE 71 originated below the streamer belt, while in GLE 72 it originated near the edge of the streamer belt. We reconstruct the CME shock fronts with an ellipsoid model based on nearly simultaneous coronagraph images from multiple viewpoints and further derive the 3D shock geometry at the GLE onset. The highest-energy particles are primarily accelerated in the shock–streamer interaction regions, i.e., likely at the nose of the shock in GLE 71 and the eastern flank in GLE 72, due to quasi-perpendicular shock geometry and confinement of closed fields. Subsequently, they are released to the field lines connecting to near-Earth spacecraft when the shocks move through the streamer cusp region. This suggests that magnetic structures in the corona, especially shock–streamer interactions, may have played an important role in the acceleration and release of the highest-energy particles in the two events.

79 ASTRONOMY AND ASTROPHYSICS↗