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At least 1,207 records · Page 67

Automation of the ICME Workflow Incorporating Material Digital Twins at Different Length Scales Within a Robust Information Management System

Recent successes in Integrated Computational Materials Engineering (ICME) have demonstrated the potential in designing fit-for-purpose materials for a given application in a cost and time efficient manner. However, the material design process must contain a level of automation in the material decision process, implementing some optimization algorithms, to truly enable the full benefits of ICME, particularly when considering materials at multiple length/time scales. In this work, we will demonstrate how the GRC ICME schema and Python framework automates a workflow that captures, analyzes, maintains, and disseminates the digital footprint in the context of tailoring resin material at the nanoscale of a woven composite Y-joint at the macroscale for an Aurora D8 double bubble fuselage. This digital footprint incorporates the interaction of both structural digital twins and material twins at various length scales.

Brandon L. Hearley↗

Diffuse Auroral Intensities Produced By Whistler Mode and Electron Cyclotron Harmonic Waves

Whistler mode waves and ECH waves as observed by the THEMIS-D and THEMIS-E satellites have been analyzed. It is observed that ECH waves are very weak for pitch-angle diffusion whereas whistler mode waves are more efficient. Bounce-averaged pitch-angle diffusion rates at the edge of atmospheric loss-cone have been calculated for both waves. Further, these are used to obtain electron precipitation flux inside the loss-cone. The electron flux at the edge of the loss-cone is represented by the kappa distribution. Numerical calculation of precipitation flux is performed by varying the fitting parameters (which determine the shape of electron precipitation flux) appearing in the kappa distribution. Precipitation flux is used to obtain volume excitation rates and height-integrated volume excitation rates for the seven excitation states. Calculations have been performed using two standard atmosphere models corresponding to mean and high solar and geomagnetic activities. It has been observed from studies that the excitation rates produced by the ECH wave are several orders of magnitude smaller than the rates produced by whistler mode waves. We show that the scattering efficiency of whistler Mode is higher than that of ECH waves. The oblique whistler mode waves allow efficient scattering of electrons in diffuse aurora. Outcomes are discussed.

Arvind K. Tripathi↗

Maryland Space Weather UnderGround (SWUG) Educational Outreach Program for Solar Eclipse Study

Space Weather UnderGround (SWUG) is an educational outreach program that provides hands-on experiences in Science, Technology, Engineering, and Mathematics (STEM) to high school and undergraduate students. It has three ultimate goals: 1. Educating the future STEM workforce. 2. Building a cost-effective, research-capable ground 3. magnetometer array across the United States. Collecting geomagnetic field data at high spatial resolution for heliophysics research. SWUG students build, test, and deploy the Simple Aurora Monitor (SAM)-III kit, purchased by Reeve Engineers. The SAM-III kit is a fluxgate magnetometer that measures geomagnetic field changes at a resolution of 1 nT/sec. Dr. Charles Smith initiated the SWUG program at the University of New Hampshire, and it has since expanded to Alaska and Maryland. This presentation introduces the Maryland SWUG activities. The MD-SWUG program was started in the Fall of 2022 with the goal of deploying magnetometers to the solar eclipse sites in 2023 and 2024. Two student-built magnetometers were deployed to the Southwest Research Institute and the Los Alamos National Laboratory for the 2023 annular solar eclipse, with plans to deploy at least four magnetometers to the 2024 total solar eclipse sites, including the University of Texas at Dallas. During a solar eclipse, reduced solar irradiance weakens ionospheric currents and reduces geomagnetic fields by up to 30 nT at the eclipse sites. A recent study suggested that solar eclipses impact geomagnetic fields not only along the eclipse path but also at their magnetic conjugate locations. The SWUG program will help us understand this electrodynamic coupling between the conjugate locations during a solar eclipse by providing better data coverage along the solar eclipse path.

Hyunju Connor↗

Preface to the Special Issue on Modeling and Data Analysis Methods for the SMILE mission

The SMILE (Solar wind Magnetosphere Ionosphere Link Explorer) project (http://www.nssc.cas.cn/smile/, https://www.cosmos.esa.int/web/smile/mission) is a joint spacecraft mission of the European Space Agency (ESA) and the Chinese Academy of Sciences (CAS) with an expected launch in 2025. SMILE aims to study the global interactions of solar wind–magnetosphere–ionosphere innovatively by imaging the Earth’s magnetosheath and cusps in soft X-rays and the northern auroral region in ultraviolet (UV) while simultaneously measuring plasma and magnetic field parameters in the solar wind and magnetosheath along a highly-elliptical and highly-inclined orbit. This special issue is composed of 22 articles, presenting recent progress in modeling and data analysis techniques developed for the SMILE mission. In this preface, we categorize the articles into the following seven topics and provide brief summaries: (1) instrument descriptions of the Soft X-ray Imager (SXI), (2) numerical modeling of the X-ray signals, (3) data processing of the X-ray images, (4) boundary tracing methods from the simulated images, (5) physical phenomena and a mission concept related to the scientific goals of SMILE-SXI, (6) studies of the aurora, and (7) ground-based support for SMILE.

SMILE↗

Calculating the High-Latitude Ionospheric Electrodynamics Using A Machine Learning-Based Field-Aligned Current Model

We introduce a new framework called Machine Learning (ML) based Auroral Ionospheric electrodynamics Model (ML-AIM). ML-AIM solves a current continuity equation by utilizing the ML model of Field Aligned Currents of Kunduri et al. (2020, https://doi.org/10.1029/2020JA027908), the FAC-derived auroral conductance model of Robinson et al. (2020, https://doi.org/10.1029/2020JA028008), and the solar irradiance conductance model of Moen and Brekke (1993, https://doi.org/10.1029/92gl02109). The ML-AIM inputs are 60-min time histories of solar wind plasma, interplanetary magnetic fields (IMF), and geomagnetic indices, and its outputs are ionospheric electric potential, electric fields, Pedersen/Hall currents, and Joule Heating. We conduct two ML-AIM simulations for a weak geomagnetic activity interval on 14 May 2013 and a geomagnetic storm on 7–8 September 2017. ML-AIM produces physically accurate ionospheric potential patterns such as the two-cell convection pattern and the enhancement of electric potentials during active times. The cross polar cap potentials (ΦPC) from ML-AIM, the Weimer (2005, https://doi.org/10.1029/2004ja010884) model, and the Super Dual Auroral Radar Network (SuperDARN) data-assimilated potentials, are compared to the ones from 3204 polar crossings of the Defense Meteorological Satellite Program F17 satellite, showing better performance of ML-AIM than others. ML-AIM is unique and innovative because it predicts ionospheric responses to the time-varying solar wind and geomagnetic conditions, while the other traditional empirical models like Weimer (2005, https://doi.org/10.1029/2004ja010884) designed to provide a quasi-static ionospheric condition under quasi-steady solar wind/IMF conditions. Plans are underway to improve ML-AIM performance by including a fully ML network of models of aurora precipitation and ionospheric conductance, targeting its characterization of geomagnetically active times.

auroral electrodynamics↗

Inner Magnetospheric Physics

The inner magnetosphere extends from just above the topside ionosphere to approximately 8 RE geocentric distance. Magnetospheric physics is a young science that only started to be recognized as a region with the space observations by Explorer 1 in 1958. The region is mostly populated by ionized gas or plasma from Earth’s ionosphere. Plasma populations are differentiated by their energies primarily. From the least energetic to most are the plasmasphere, ring current, and radiation belts, extending from about 1 eV to 10 MeV in energy and from 1,000s cm-3 down to a few particles per cubic centimeter and less, respectively. The solar wind and solar erupted coronal mass ejections (CMEs) arriving and interacting with Earth’s magnetic field creates a dynamo effect that drives million ampere currents along magnetic field lines that close through the ionosphere. The solar wind dynamo also creates a 100s kV electric field across the magnetosphere that drives convective motion of the plasma within it. The solar wind driven currents compress Earth’s magnetic field on the sunward side and greatly extents the field on the nightside to form the magnetotail. The energy stored in the magnetotail is impulsively released when magnetic field lines there merge, releasing energy into the plasma trapped by the magnetic field. Those plasma become the ring current that loses plasma into the atmosphere to produce the aurora and at the same time ring current plasma can be further energized by wave-particle interactions to become the radiation belts. The presentation will review these topics and a few of the underlying physical processes that are involved in this highly coupled planetary system.

inner magnetosphere↗

Inertia of Ionospheric Conductance During Electron Precipitation Events

Using the SuperThermal Electron Transport (STET) model coupled with the Super‐thermal Proton Electron Atomic Hydrogen—tRansport in the Ionosphere and Thermosphere (SPEAH‐RIT) codes, we demonstrate that temporal variability of ionospheric conductance is defined by several time scales: the temporal variations of the magnetospheric source, the start time of electron precipitation, and the termination of the corresponding source. In these cases, the time scales are defined by dissipation of energetic electrons and effective recombination processes. The results presented in this paper were applied in the regions of pulsating aurora and polararcs, demonstrating the fact that ionospheric conductance requires some time to form and decay. These time delays constitute an effective “inertia” in the conductance calculation which is- not accounted for in many global models which assume an instantaneous connection between precipitation and conductance. Ionospheric conductance inertia influences the temporal variation in ionospheric and magnetospheric electric fields, and as a result, impacts magnetosphere‐ionosphere (MI) dynamics on a global scale.

George V Khazanov↗

Impact of the May 2024 Gannon Storm on Low Earth Orbit Dosimetric Quantities: Comparisons of the NAIRAS Model -Real Time and Re-analysis- with ISS Data

The solar storm of May 2024, the Gannon storm, led to a display of aurora over the globe. One of the less known effects of that storm is on the radiation doses in low-Earth orbit: it first led to a Forbush decrease in dose rates and then to a Solar Energetic Particle (SEP) event that increased the total dose suffered near the international space station. The Nowcast of Aerospace Ionizing RAdiation System (NAIRAS) model predicts dosimetric and radiative flux quantities for assessing human radiation exposure levels and radiation effects on flight electronic systems from the surface of the Earth to deep space. It runs in near-real-time at the CCMC, but also has a run-on-request that allows for a more precise computation thanks to carefully processed data. NAIRAS was able to compute the effects of the Gannon storm at the ISS in real-time and provide ideas of the doses encountered by the astronauts and the experiments in and outside of the station. In this work, we show the simulations of the doses encountered during the storm and compare them with several measurements at the ISS. This work shows the capabilities of NAIRAS to address the problem of the SEP events at low Earth orbit and its real-time capabilities.

Guillaume Gronoff↗

Connecting Uranus’ Magnetosphere and Upper Atmosphere via Electron Precipitation

Abstract. The precipitation of electrons is a key process through which significant energy is transferred from Uranus’ magnetosphere to its upper atmosphere. These electrons drive atmospheric ionization, thermospheric heating, and auroral emission at Uranus, and their properties are critical to address Uranus’ energy crisis as well as auroral phenomena. We combine measurements of auroral precipitation at Earth, Saturn, and Jupiter, with relevant Voyager 2 observations to estimate the properties of precipitating electrons at Uranus. In order to produce the measured aurora, energy fluxes of ~0.1mW/m 2 are required with energies of ~5keV.The acceleration of particles between the magnetosphere and thermosphere is predicted to be Earth-like, but with significantly lower overall energy flux and field aligned currents due to Uranus’ sparser magnetosphere. The height-integrated Joule heating rates of ~0.2 mW/m 2 are an order of magnitude lower than Earth

Daniel Gershman↗

Advancing Wildfire Monitoring with TEMPO and ML tools: Hourly Smoke and Fire‑Front Mapping and Near‑Surface NO₂ Predictions

Wildfires impose substantial impacts on communities and regions downwind of wildfire smoke. We present a TEMPO‑enabled workflow that generates value‑added Level‑3 smoke‑plume masks and fire‑front maps for large wildfires, such as 2024 Park Fire, using the self‑supervised deep learning system SIT‑FUSE, along with near‑surface NO₂ predictions produced by a foundation model (Microsoft Aurora). We conclude by outlining a roadmap for expanding these capabilities to additional Western U.S. wildfire events and for delivering actionable tools to stakeholders. This open-source, reproducible workflow provides a scalable framework for cross-agency wildfire monitoring to overcome traditional limitations in smoke-cloud discrimination and air-quality forecasting by incorporating TEMPO data and beyond.

Xiaohua Pan↗

Anaerobic Digestion Implementation at Dairies in Colorado

Anaerobic digestion (AD) of animal manure to produce biogas (methane) fuel has a wide variety of environmental benefits to air and water quality in addition to reducing greenhouse gas (GHG) emissions. In addition, the resulting biogas fuel and digestate products can help meet the energy needs and provide revenue to the farm. This case study explores the potential benefits and drawbacks of implementing an AD system at the Aurora Organic Dairy (AOD) High Plains Complex in Gill, Colorado. The project is envisioned as digester located at the AOD Complex with a single feed stream of manure from the Complex. The project would be implemented by a third-party contractor who would build and operate the system and pay the dairy a fraction of the profits, in addition to providing by-products such as digestate solids and liquids for fertilizer.

43 PARTICLE ACCELERATORS↗

Pele: An Exascale-Ready Suite of Combustion Codes

High fidelity simulations of realistic combustion devices are extremely demanding computationally because of the requirements to capture complex fuel chemical decomposition, its intricate interactions with turbulent, often multiphase, flows, and the wide separation of space and time scales between the thin flame and the device boundaries. Software required to carry out such computations tends to be extremely complex, particularly when designed to exploit hardware accelerators, and can be difficult to port and maintain. We present Pele, a performance portable suite of tools for the simulation of combustion systems, including codes to evolve reactive multiphase configurations in the low Mach number and compressible flow regimes, along with a set of inter-compatible post processing and in situ analysis tools. The Pele suite of tools is built on top of the AMReX framework for block-structured adaptive mesh refinement, which provides efficient data structures and algorithms that enable the development of a wide variety of efficient mesh and particle based PDE integration schemes. A hierarchical MPI+X parallelism scheme supports CPU-only and accelerated architectures, where X can be OpenMP, CUDA, and HIP based approaches for intra-node computational work distribution. The algorithms and data structures underlying the Pele simulation and analysis tools are highly scalable and performant across a wide variety of high-performance computing platforms, including DOEs newest exascale-class machines, Frontier and Aurora. The simulation and analysis tools are fully documented and freely distributed as open source via GitHub. We present key algorithmic and software challenges, solution strategies, performance and resulting set of capabilities.

AMReX↗

Developing Multiphysics, Integrated, High-Fidelity, Massively Parallel Computational Capabilities for Fusion Applications Using MOOSE

As the need for fusion as a clean, sustainable, and abundant energy source grows internationally, so does the need for multiphysics, computational tools to model, study, and predict the complex interactions between plasma, materials, and engineering processes. These tools have a crucial role to play in solving scientific and engineering challenges and accelerating fusion energy deployment. To address these needs, modeling capabilities should enable massively parallel, multiphysics, fully integrated high-fidelity simulations of fusion systems. Additional attributes, such as being open source and modular while maintaining high software quality assurance standards will maximize impact by ensuring accessibility for all and wide acceptance, rapid expansion and development, as well as reliability, efficiency, and robustness. In this paper, we describe how the Multiphysics Object-Oriented Simulation Environment (MOOSE) framework, which has a track record of success in the fission space thanks to the attributes listed above, can be leveraged in the fusion energy field. We highlight key successes of the MOOSE application in the fission space and describe how MOOSE has been and is being applied to fusion applications in the United States---e.g., Tritium Migration Analysis Program, version 8 (TMAP8), MOOSE Fusion Module, Fusion ENergy Integrated multiphys-X (FENIX)---and the United Kingdom---e.g., AURORA, Achlys, Apollo. These efforts aim to establish a suite of tools that can be further extended to accelerate fusion energy deployment.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

Initial Development of Fusion Magnet Simulation Capabilities for Performance and Safety Evaluation Using the MOOSE Framework

Fusion energy holds the promise of being a transformative technology as a carbon-neutral, sustainable source of energy. Whole device modeling and the development of fusion digital twins will be increasingly important for emerging fusion device concepts at both national laboratories and within the commercial fusion industry. However, meeting the challenge of whole device modeling of fusion energy devices requires robust, multiphysics, multiscale modeling and simulation technologies capable of running on large-scale supercomputers. Detailed analysis of individual systems at-scale is also required to ensure safe and efficient operation as well as provide the safety basis for future device designs and licensing activities. In a tokamak, toroidal and poloidal magnets confine and shape the fusion plasma to promote the fusion reaction. High plasma temperatures and high magnetic field requirements in modern design concepts (leading to high amounts of energy stored within each magnet) impose electrical, thermal, and mechanical loads on the magnet components, which in turn impacts the safety considerations of the magnet and their supporting systems. Idaho National Laboratory (INL) has a history of working in this space, including development and benchmarking of the Magnetic System Circuitry Analysis Program (MSCAP) and Magnet Arcing (MAGARC) codes to study magnet quench events; notably, MAGARC was used to study quenching during the ITER Engineering Design Activity. However, these legacy codes and capabilities are not parallel and scalable, and new tools are required for future advances in this area, which leads to the INL-developed Multiphysics Object-Oriented Simulation Environment (MOOSE) framework. Developed originally for fission reactor systems under United States Department of Energy, Office of Nuclear Energy modeling and simulation programs, the MOOSE framework has been well-suited to multiscale, multiphysics modeling and simulation needs for nuclear systems. The framework is open-source, well-tested, under continuous development and deployment, and developed to a Nuclear Quality Assurance, Level 1 software quality standard. MOOSE has also been used in the fusion space previously in several projects: INL’s Tritium Migration Analysis Program, Version 8 (TMAP8) for tritium migration, UK Atomic Energy Authority’s A Unified Resource for OpenMC (fusion) Reactor Applications (AURORA) code for fusion thermo-mechanical and neutronics analysis, and Argonne National Laboratory’s Cardinal for high-fidelity computational fluid dynamics and neutronics. However, to model superconducting magnets, several MOOSE enhancements are required: additions to the current MOOSE electromagnetic capabilities, new material libraries for superconductors of interest (such as YBCO), as well as fusion-specific models for thermo-mechanics. This talk will discuss initial development activities to build these capabilities in MOOSE, focusing on initial validation and benchmarking activities. Proposed coupling workflows and future work to support the simulation of fusion magnets and magnet structural assemblies for performance and safety evaluation in MOOSE will also be discussed.

70 - PLASMA PHYSICS AND FUSION TECHNOLOGY↗

Streptococcus pneumoniae HtrA is a dynamic and monomeric virulence factor capable of forming larger oligomeric complexes

Abstract High‐temperature requirement A (HtrA) proteases are a conserved family of serine proteases central to protein quality control and bacterial virulence. While Gram‐negative and human HtrAs are structurally well studied, Gram‐positive homologs remain essentially uncharacterized. Here, we present the first integrated structural and mechanistic analysis of a Gram‐positive HtrA, from Streptococcus pneumoniae , a virulence factor essential for adhesion and infection in vivo. Proteomic profiling of an htrA knockout and cleavage assays demonstrate that S. pneumoniae HtrA is required for protein quality control, with the PDZ domain mediating substrate recognition. Biochemically, S. pneumoniae HtrA exists exclusively as a monomer in solution, a striking divergence from canonical trimeric HtrAs that we show is shared with other Gram‐positive homologs. NMR analyses reveal that the monomer dynamically samples open and closed conformations, while cryo‐EM of a catalytic mutant identifies a hexamer stabilized by a unique LoopA–PDZ interaction. Together, these findings define S. pneumoniae HtrA as a dynamic monomer with interdomain coupling between its protease and PDZ domains, establishing Gram‐positive HtrAs as a mechanistically divergent subgroup within the HtrA family.

Lee, Eunjeong [Department of Biochemistry and Mole↗

Longitudinal Surveillance for Chronic Health Conditions in Former United States Department of Energy Site Workers

The aim of the study was to determine (1) the rate at which rescreening former Department of Energy site workers identifies noncommunicable chronic diseases and (2) the development of comorbid conditions. Incidence and prevalence of hypertension, diabetes, reduced kidney function, and peripheral neuropathy at both initial and return screenings were calculated. Risk ratio of chronic disease development at return screening based on the presence of other conditions at initial screening were estimated with generalized linear regression. Prevalence of reduced kidney function was 19% at initial examination and 30% at return examination. The screening program was responsible for identifying 81% of reduced kidney function cases. Similar findings were present for the other chronic conditions examined. As a result, former worker health surveillance programs help identify significant health conditions among DOE workers, subcontractors, and visitors. Longitudinal screening of participants detects additional chronic conditions.

59 BASIC BIOLOGICAL SCIENCES↗

Reduction of Chemokine CXCL9 Expression by Omega-3 Fatty Acids via ADP-Ribosylhydrolase ARH3 in MIN6 Insulin-Producing Cells

Type 1 diabetes (T1D) results from the autoimmune destruction of the insulin-producing β cells of the pancreas. Omega-3 fatty acids protect β cells and reduce the incidence of T1D, but the mechanism is poorly understood. We have shown that omega-3 fatty acids reduce pro-inflammatory cytokine-mediated β-cell apoptosis by upregulating the expression of the ADP-ribosylhydrolase ARH3. Here, we further investigate the β-cell protection mechanism of ARH3 by performing siRNA analysis of its gene Adprhl2 in MIN6 insulin-producing cells, subsequent treatment with a cocktail of the pro-inflammatory cytokines IL-1β + IFN-γ + TNF-α, followed by proteomics analysis. ARH3 regulated proteins from several pathways related to the nucleus (splicing, RNA surveillance, and nucleocytoplasmic transport), mitochondria (metabolic pathways), and endoplasmic reticulum (protein folding). ARH3 also regulated the levels of proteins related to antigen processing and presentation, and the chemokine-signaling pathway. We further studied the role of ARH3 in regulating the chemokine CXCL9. We found that ARH3 reduces the cytokine-induced expression of CXCL9, which is dependent on omega-3 fatty acids. In conclusion, we demonstrate that omega-3 fatty acids regulate CXCL9 expression via ARH3, which may have a role in protecting β cells from immune attack thereby preventing T1D development.

59 BASIC BIOLOGICAL SCIENCES↗

Modern chemical graph theory

Abstract Graph theory has a long history in chemistry. Yet as the breadth and variety of chemical data is rapidly changing, so too do graph encoding methods and analyses that yield qualitative and quantitative insights. Using illustrative cases within a basic mathematical framework, we showcase modern chemical graph theory's utility in Chemists' analysis and model development toolkit. The encoding of both experimental and simulation data is discussed at various levels of granularity of information. This is followed by a discussion of the two major classes of graph theoretical analyses: identifying connectivity patterns and partitioning methods. Measures, metrics, descriptors, and topological indices are then introduced with an emphasis upon enhancing interpretability and incorporation into physical models. Challenging data cases are described that include strategies for studying time dependence. Throughout, we incorporate recent advancements in computer science and applied mathematics that are propelling chemical graph theory into new domains of chemical study. This article is categorized under: Molecular and Statistical Mechanics > Molecular Dynamics and Monte‐Carlo Methods Structure and Mechanism > Computational Materials Science Structure and Mechanism > Molecular Structures

Leite, Leonardo S. G.↗