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At least 1,567 records · Page 87

INGRID: An interactive grid generator for 2D edge plasma modeling

A fusion boundary-plasma domain is defined by axisymmetric magnetic surfaces where the geometry is often complicated by the presence of one or more X-points; and modeling boundary plasmas usually relies on computational grids that account for the magnetic field geometry. The new grid generator INGRID (Interactive Grid Generator) presented here is a Python-based code for calculating grids for fusion boundary plasma modeling, for a variety of configurations with one or two X-points in the domain. INGRID first performs partitioning over the domain consisting of a small number of patches conforming to the magnetic field and wall geometry; then it generates a subgrid on each of the patches and joins them into a global grid. This domain partitioning strategy makes possible a uniform treatment of various configurations with one or two X-points in the domain. This includes single-null, double-null, and other configurations with two X-points in the domain. The INGRID design allows generating grids either interactively, via a parameterfile driven GUI, or using a non-interactive script-controlled workflow. Results of testing demonstrate that INGRID is a flexible, robust, and user-friendly grid-generation tool for fusion boundary-plasma modeling.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

Development and implementation of an in situ high-resolution isotopic microscope for measuring metabolic interactions in soil mesocosms (Final Report)

Many of our planet’s ecosystems rely on the activities of soil microbial communities. These microbes have significant and wide-ranging effects: they metabolize carbon and other nutrients, interact with plants and fungi, and perform other processes important to soil health. However, our ability to directly observe the enzymatic and metabolic activities of microbes within soil is currently limited, not only by the complexity of soil microbial communities themselves, but also by the lack of experimental tools to study them and their molecular interactions in situ. These challenges hinder our understanding of the life-sustaining processes of biomass decomposition and the manner in which it contributes to the movement of freed carbon within soil ecosystems. Here we aimed to develop a novel ultrahigh-resolution isotopic microscope that combines complementary imaging modalities to gain insights into metabolic cycling in soil. This final report covers both the portion of this work that was initially completed at the University of North Carolina at Chapel Hill from 2018 to 2019 (as award DE-SC0019012) and then from 2020 to 2022 (2023 in NCE) at the University of Massachusetts Chan Medical School. This goal was to build an integrated platform consisting of fluorescence microscopy, Raman microspectroscopy, and nanospray desorption electrospray ionization Fourier transform ion cyclotron resonance mass spectrometry (nanoDESI-FTICR-MS) to directly investigate microbial activities and molecular transformations occurring in soil by exploiting the use of both fluorescent labels and stable isotope probing. Specifically, our instrument was conceived as integrating: (A) fluorescence detection to localize soil microbes, identify bacteria taking up polysaccharides, and monitor gene expression of enzymes involved in decomposition; (B) Raman microspectroscopy to determine which microbes incorporate decomposition products into their biomass, as well as which decomposers are metabolically active; and (C) nanoDESI-FTICR-MS imaging to spatially probe, in real-time, the metabolites in the surrounding area, which we expect will reveal the distributions of the products of the enzymatic breakdown of polysaccharides, as well as specialized metabolites acting as cell-cell signals between decomposers. To enable these measurements, we exploited fluorescence-based probes to map the microbes that are enzymatically active (per A) and utilized stable isotope-labeled substrates to visualize both the microbial and molecular fate of decomposed biomass (B and C, respectively). To accomplish our goal, we combined the expertise of a multidisciplinary group of scientists to pursue the construction of this microscope and to investigate scientific questions that would be facilitated by such a capability. We aimed to employ this spatially informative, high-resolution isotopic microscope to visualize the critical steps of biomass degradation and the molecular fate of other environmentally relevant substrates within soil mesocosms. Accordingly, this technology will enhance our understanding of the microbial and metabolic interactions occurring within soil communities that are relevant to carbon degradation and other soil processes. The instrument has been developed and housed at EMSL, where it will be available to the entire EMSL User Base, enabling a variety of related DOE-relevant systems to be interrogated in the future by diverse scientific research groups.

59 BASIC BIOLOGICAL SCIENCES↗

Nonlinear Interactions of Alfven Waves in Multi-ion Plasmas (Final Report)

Alfven waves are of fundamental importance in magnetized plasmas, such as the solar wind and Earth’s magnetosphere. When the wave amplitude is large, nonlinear interactions among waves, such as three wave resonance, dominate the dynamics. The nonlinear process can be used as a diagnosis of the plasma composition because the composition affects the dispersion relation of Alfven waves, especially when the wave frequency is close to ion cyclotron frequencies. Previous experiments on the Large Plasma Device (LAPD) had demonstrated that the relative density of two ion species can be determined by examining nonlinear interaction of two counter-propagating Alfven waves. In this project we extended the study to a plasma with three ion species, where multiple wave bands exist, and more nonlinear interactions are possible. We also investigated the feasibility of launching two co-propagating waves to measure the ion composition. The findings of this study have implication in developing new technology to measure cold ion populations in space plasmas.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

Unsupervised Learning Based Interaction Force Model for Nonspherical Particles in Incompressible Flows

This project provides a neural network-based interaction force model for gas-solid flows from low to intermediate Reynolds numbers and concentration, which can be linked to MFiX-DEM. We have constructed a database of the interaction force between the irregular-shaped particles using a spherical harmonic method and the fluid phase based on the particle-resolved direct numerical simulation (PR-DNS) with immersed boundary-based gas kinetic scheme. Unsupervised learning method, i.e., variational auto-encoder (VAE) has been applied to extract the primitive shape factors determining the drag force, lifting forces, and torque. The interaction force model has been trained and validated with a simple but effective multi-layer feed-forward neural network: multi-layer perceptron (MLP), which will be concatenated after the encoder of the previously trained VAE for geometry feature extraction for single, irregular particles. We have trained transpose convolutional neural networks with the PR-DNS data to predict the velocity and pressure gradient of the single particle systems and utilized them to calculate drag force of multi-particle systems. This model can provide high computational efficiency because it does not require collecting multiparticle system data from PR-DNS.

99 GENERAL AND MISCELLANEOUS↗

TOLERANCES OF CRAB DISPERSION AT THE INTERACTION POINT IN THE HADRON STORAGE RING OF THE ELECTRON-ION COLLIDER

The Electron Ion Collider (EIC) presently under construction at Brookhaven National Laboratory will collide polarized high energy electron beams with hadron beams with luminosities up to 10^34 cm^?2 s^?1 in the center mass energy range of 20-140 GeV. Due to the detector solenoid in the interaction region, the design horizontal crabbing angle will be coupled to the vertical plane if uncompensated. In this article, we study the tolerances of crab dispersion at the interaction point in the EIC Hadron Storage Ring (HSR). Both strong-strong and weak-strong simulations are used. We found that there is a tight tolerance of vertical crabbing angle at the interaction point in the HSR.

Luo, Y.↗

Strong Turbulent Alfvén Wave Interactions in a Laboratory Plasma (Final Scientific/Technical Report)

Turbulence is a fundamental and widespread aspect of plasma behavior, occurring in nearly every natural plasma system that is observed, from clusters of distant galaxies to plasmas within the solar system. Plasma continuously streaming from the sun, known as the solar wind, has been particularly fruitful in understanding plasma turbulence due to the availability of spacecraft data. An ensuring mystery in solar wind turbulence is the unexpected observation of residual energy – there is excess energy in the magnetic fluctuations compared to the velocity fluctuations even though an Alfvén wave in Magnetohydrodynamics has equal amounts of energy in each. The project studied strong non-linear interaction between counter-propagating Alfvén waves in the lab for the first time. Dr. Mel Abler, who was hired as a postdoctoral researcher under this grant, obtained the first observation of residual energy in a counter-propagating Alfvén wave interaction. Dr. Abler showed experimentally that the interaction of two counter-propagating Alfvén waves produces daughter modes that are not not normal modes of the system and only exist in the presence of the nonlinear drive. These Alfvén quasimodes contain either negative residual energy (more energy in magnetic fluctuations) or positive residual energy (more energy in velocity fluctuations).

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

Corrinoids as model nutrients to probe microbial interactions in a soil ecosystem

Earth’s soils are habitats for microbial communities that drive biogeochemical cycling, plant growth, and carbon storage and persistence. The thousands of microbial species living in soil form an intricate web of interactions involving the exchange of molecules produced by different microbes. Understanding in detail how these molecular exchanges occur and how they shape microbial communities may lead to new methods to improve soil health, bioremediation efforts, and better understanding of biogeochemical processes. The overall goal of this research is to gain a deeper knowledge of the microbial interactions that drive soil community structure. However, the high functional and genomic diversity in soil microbiomes has posed a challenge for current microbiology methods to achieve this goal. This research leverages a model group of key metabolites related to cobalamin (vitamin B 12 ), known as corrinoids, to investigate microbial interactions.

59 BASIC BIOLOGICAL SCIENCES↗

US Perspectives on the Conventional/Nuclear Interactions and the Impacts on Nuclear Escalation Risks and Future Arms Control Prospects

This short paper is organized around a few discrete questions. How have U.S. perspectives on conventional and nuclear interactions evolved in recent years? How have these changing perspectives shaped U.S. thinking on perceived nuclear escalation risks with Russia, both long standing and newly emerged? And finally, what do these U.S. perceptions of interactions and risks mean for future potential arms control either in the conventional or nuclear arena? The answers to these questions appear to show some promise for future work in this area. Conventional and nuclear interactions are increasing, and thus the two domains cannot be kept as deliberately or artificially separated as in the past. There is a growing mutual recognition regarding the interplay between conventional and nuclear capabilities in the strategic stability equation. There is also an increasingly shared recognition in the United States and Russia that nuclear escalation risks are likely to originate in conventional crises or conflict, placing an imperative on some form of conflict prevention procedures at the lower end of the spectrum. This requirement suggests some potential areas for risk reduction measures outside of those traditionally considered in conventional and nuclear arms control over the past several decades.

98 NUCLEAR DISARMAMENT, SAFEGUARDS, AND PHYSICAL P↗

Shear thinning rheology in a concentrated suspension of fibers: the role of attractive interactions

Fiber-reinforced composites are ubiquitously encountered in the engineering, automobile, and aerospace industries. Fabrication of these composites requires the mixing of fibers dispersed in a liquid, and the final structure is affected by fiber properties, interactions, and flow fields. A good understanding of the rheology of fiber suspensions can aid in the design and optimization of the fabrication processes. To this end, we simulate the simple shear flow of fiber suspensions by accounting for short range lubrication, van der Waals attractive, electrostatic repulsion interactions, as well as friction between fibers. Direct numerical simulations are performed using the Immersed Boundary Method where the fibers are modeled as flexible slender bodies governed by the Euler-Bernoulli beam theory and the fluid flow is resolved using the Navier-Stokes equations. The simulation results for the suspension viscosity and yield stress are consistent with the experimental data from the literature for polyamide fiber suspensions. The shear-thinning behavior becomes stronger as we increase the magnitude of attractive interactions. Furthermore, we perform a parametric study varying fiber flexibility, volume fraction, aspect ratio, inertia and examine suspension viscosity and yield stress.

Khan, Monsurul↗

Impact of intermolecular interactions on the spectroscopic signals, energetics, and redox behavior of high valent 237 Np (Final Report)

Our studies have led to definitive spectral assignments for neptunyl-neptunyl interactions in aqueous solutions that will be valuable for predicting Np(V) speciation in solution. We have definitively demonstrated how actinyl-hydrogen and actinyl-cation interactions can influence spectral features for uranyl and neptunyl system. In addition, we have developed DFT methodologies to predict enthalpies of formation for uranyl and neptunyl systems and linked stability to chemical descriptors (hydrogen interaction energies or the electrostatic attraction energies). The counterion influenced the rate of Np(V) oxidation to Np(VI) neptunates and [Np(VII)O 4 OH] 3- complexes. Oxidation to Np(VII) during ozonolysis takes place through the presence of hydroxyl radicals.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Sensitivity of the MicroBooNE experiment to dark trident interactions

This note describes an ongoing search for dark tridents in the MicroBooNE detector. The dark trident interaction is a proposed interaction of sub-GeV dark matter with ordinary matter that would allow an exploration of a hidden dark sector composed of a dark scalar or fermion χ and a dark photon A'. This dark matter candidate can be produced at fixed-target neutrino beams such as the NuMI beam and travel uninterrupted to the MicroBooNE liquid argon detector. The interaction with the liquid argon occurs through the process χ + Ar → χ + Ar + A' where the dark photon promptly decays inside the detector through: A' → e + + e - . Two event selection strategies are explored, one using boosted decision trees and the other one applying a convolutional neural network. For both analyses a projected sensitivity in MicroBooNE after 2 × 10 21 protons on target is presented.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

Status of ICARUS NUMI Interaction Cross-Section Analysis

The ICARUS experiment, utilizing Liquid Argon Time Projection Chamber (LAr TPC) technology, has been installed at Fermilab in Chicago, Illinois, following its initial operation in Italy and subsequent refurbishment at CERN. ICARUS completed commissioning in June 2022. Currently, the experiment is in the phase of analyzing data from its two runs of physics data acquisition and gearing up for the third run. While its primary objective is to function as the far detector of the Short Baseline Neutrino program (SBN), seeking sterile neutrino signatures, ICARUS also offers diverse physics capabilities, including searches beyond the standard model and measurements of cross-sections. In addition to being exposed to the common Booster Neutrino (BNB) beamline, ICARUS also receives off-axis neutrinos from the Main Injector (NuMI) beam. Due to the off-axis angle between NuMI and ICARUS, coupled with contributions from both pion and kaon decays to neutrino fluxes, interactions of NuMI neutrinos within ICARUS can be detected over a range of several GeV in energy. These interactions present opportunities for crucial cross-section measurements and model tests within an energy range that overlaps both the SBN oscillation search and a portion of the DUNE spectrum. This poster presentation will delve into our efforts to conduct a muon-neutrino cross-section measurement, where the signal is defined by events with no pions produced in the final state of the interaction, along with some preliminary muon-neutrino inclusive measurements. Additionally, it will provide updates on the current status and future plans, including reconstruction, selection, and analysis procedures.

43 PARTICLE ACCELERATORS↗

Neutrino Interaction Model and Uncertainties for MicroBooNE Analyses

The MicroBooNE experiment is searching for an excess of electron-like events at low neutrino energy, as well as making the first high-statistics measurements of neutrino interactions on argon. Interpretation of these results requires a reliable model of neutrino interaction physics with well-motivated uncertainties. This note describes the neutrino interaction model and uncertainties adopted for use in MicroBooNE’s cross section measurements and low-energy excess search.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

Selection of charged-current neutrino-induced $K^+$ production interactions in MicroBooNE

MicroBooNE is an 85-ton active mass liquid-argon time projection chamber (LArTPC) neutrino detector exposed to the Booster Neutrino Beam (BNB) at Fermilab. MicroBooNE’s physics goals is the precision measurement of neutrino interactions on argon in the 1 GeV energy regime. The study of neutrino interactions producing a $K^+$ in the final-state can improve the background estimates for future proton-decay experiments looking for the $p → K^+$$_ν$ channel on argon such as DUNE. In this document we present a simulation study for a selection of events with a $K^+$ that is produced in a charged-current neutrino interaction and decays into a $µ^+$ and a $ν_µ$ in the MicroBooNE detector. It will focus on how we use the particle identification techniques developed by MicroBooNE to achieve a sample with a 7% efficiency and 66.7% purity. This is the first step toward a charged-current kaon production cross section measurement in argon.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

Reconstruction and Selection of Neutrino Interactions in MicroBooNE using Deep Convolutional Neural Networks

In this document, we describe a new reconstruction workflow developed for the MicroBooNE experiment. It features the use of Deep Convolutional Neural Networks trained to recognize key structures within the data sufficient for the 3D reconstruction of neutrino interactions within the detector. As a test of the reconstruction utility, the products of the reconstruction workflow are used to select inclusive charged-current (CC) $\nu_e$ and $\nu_\mu$ interactions in both simulated and real MicroBooNE data. In simulation, our $\nu_e$ and $\nu_\mu$ selections achieve an efficiency of 57% and 68\%, respectively, with a purity of 91% and 96%, respectively. We find that these selections are competitive with the inclusive selections used for the most recent MicroBooNE LEE searches. In particular, the CC-$\nu_e$ inclusive selection efficiency improves by over 20% while also improving sample purity. As a first step in quantifying potential bias, the data and Monte Carlo expectati ons are compared for both selections using the MicroBooNE open data. Within statistical and systematic uncertainties, both the electron and muon CC-inclusive event samples agree. A comparison of the real data events chosen by our work and another reconstruction framework shows that the two analyses each identify a sizeable fraction of events the other does not. This suggests that future analyses integrating the strengths of each could lead to combined gains. This work demonstrates, for the first time on real LArTPC data, state-of-the-art neutrino interaction reconstruction centered around deep learning algorithms.

43 PARTICLE ACCELERATORS↗

Conceptual Design of Integrated Energy Systems with Market Interaction Surrogate Models

Most integrated energy system (IES) optimization frameworks employ the price-taker approximation, which ignores important interactions with market and can result in overestimated economic values. In this work, we pro-pose a machine learning surrogate-assisted optimization framework to quantify the IES/market interactions and thus go beyond price taker. We use time series clustering to generate representative IES operation profiles for the IES optimization problem and use machine learning surrogate models to predict the IES/market interaction. We quantify the accuracy of the time series clustering and surrogate models in a case study to optimally retrofit a nuclear power plant with polymer electrolyte membrane electrolyzer to co-produce electricity and hydrogen.

Chen, Xinhe↗

Interparticle Characterization of Mechanical Biomass Particle-Particle and Particle-Wall Interactions

The biomass materials industry faces significant challenges in managing material variability and its impact on storage and handling systems. Physical properties such as moisture content, particle size, and density fluctuate considerably, leading to operational issues like bridging and ratholing that disrupt material flow. These variations create a complex cascade effect throughout the process chain, affecting transportation, storage, and conversion processes. The economic consequences of this variability manifest in increased operational costs, maintenance requirements, and system downtime. Environmental factors further complicate the situation, as weather conditions and seasonal availability influence material properties and system performance. Engineers employ specialized equipment design, material characterization protocols, and pre-processing steps like size reduction and homogenization to address these challenges. A critical knowledge gap exists between continuous-level constitutive models and particle-scale behavior. This project developed a novel device to quantify interparticle mechanics between biomass particles, measuring friction and adhesion forces between particles and wall materials. The research focused on corn stover and southern pine forest residue, creating a comprehensive database of particle interactions. This breakthrough enables direct application in particle-based computational modeling, advancing the field's understanding of biomass handling characteristics and supporting the development of more reliable and efficient storage and handling systems. The project's outcomes contribute significantly to understanding biomass's mechanical and flow characteristics, particularly how variability at the particle level affects larger-scale handling operations. This knowledge is crucial for engineering feedstock supply systems that consistently meet quality and cost specifications for various conversion processes. The innovative experimental setup developed through this research represents a significant advancement in biomass characterization methodology. Providing precise measurements of particle-level interactions establishes a foundation for more accurate predictive modeling of bulk material behavior. This enhanced understanding of fundamental particle mechanics enables engineers to anticipate better and address handling challenges before they manifest in full-scale operations. This research opens new avenues for optimizing biomass handling systems through data-driven design approaches. The comprehensive database of particle interactions serves as a valuable resource for future research and development efforts, potentially leading to more efficient and cost-effective biomass processing solutions. This advancement in particle-level mechanics could revolutionize how biomass handling systems are designed and operated, contributing to more sustainable and reliable renewable energy production.

09 BIOMASS FUELS↗

Investigation of mechanochemical interactions of hydrogen with earth materials in a subsurface gas storage

The goal of this project is to investigate the molecular interactions of H 2 with earth materials (EMs) that may potentially affect economics and safety of H 2 geological storage (HGS). We investigated (1) the H 2 intercalation into interlayers of phyllosilicates, (2) the competitive adsorption of H 2 /CH 4 onto porous materials, and (3) solubility of H 2 in interfacial and confined hydrocarbons. Our results indicate that (i) H 2 intercalation into hydrated interlayers is thermodynamically unfavorable and H 2 solubility in hydrated clay interlayers is in the same order of magnitude as that in bulk water, (ii) CH 4 outcompetes H 2 in adsorption onto kerogen, due to stronger CH 4 -kerogen interactions than H 2 -kerogen interactions, (iii) H 2 tends to dissolve more in oil than in water, and the introduction of CO 2 as a cushion gas reduces H 2 partitioning near the kaolinite surfaces. The outcomes provide foundational knowledge for preparing the USA for future storage site selection and storage system design, supporting DOE missions in clean and secured energy.

08 HYDROGEN↗