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At least 1,279 records · Page 71

Deuterium trapping mechanisms in reduced activation ferritic martensitic steels and their correlation with mechanical strengthening

Development of high-strength materials often involves introduction of additional strengthening microstructures that also serve as tritium trapping sites. Such additions in fusion material development could degrade the fuel efficiency in fusion reactors and raise radiological concerns. The contribution of individual microstructure features in hydrogen trapping must be evaluated to ensure fuel efficiency and radiological safety. This study explores the mechanistic origins of deuterium trapping in reduced-activation ferritic–martensitic steels and its correlation to mechanical strengthening. A series of model alloys and engineering steels were fabricated and subjected to different heat treatments to control deuterium trapping site density. Deuterium retention was evaluated using D 2 gas charging and thermal desorption spectroscopy, focusing on the role of grain boundary, dislocation, M 23 C 6 precipitates, and TiC precipitates. Multiscale microstructure characterization and synchrotron X-ray diffraction were performed to characterize microstructure, which was correlated to the deuterium retention property. Results show that TiC precipitates exhibit the highest deuterium trapping capacity, followed by M 23 C 6 precipitates. Dislocation and grain boundary demonstrate the lowest and similar efficiencies. Furthermore, the relationship of trapping quantity and mechanical strengthening of these microstructure features was quantified, demonstrating that TiC precipitates offer highest deuterium trapping per unit of mechanical strengthening.

Retention↗

Probing the Magnetosheath Boundaries Using Interstellar Boundary Explorer (IBEX) Orbital Encounters

Inside the magnetosheath, the IBEX-Hi energetic neutral atom (ENA) imager measures a distinct background count rate that is more than 10 times the typical heliospheric ENA emissions observed when IBEX is outside the magnetosheath. The source of this enhancement is magnetosheath ions of solar wind (SW) origin that deflect around the Earth's magnetopause (MP), scatter and neutralize from the anti-sunward part of the IBEX-Hi sunshade, and continue into the instrument as neutral atoms, behaving indistinguishably from ENAs emitted from distant plasma sources. While this background pollutes observations of outer heliospheric ENAs, it provides a clear signature of IBEX crossings over the magnetospheric boundaries. In this study, we investigate IBEX encounters with the magnetosheath boundaries using ~8 yr of orbital data, and we determine the MP and bow shock (BS) locations derived from this background signal. We find 280 BS crossings from X GSE ~ 11 R e to X GSE ~ –36 R e and 241 MP crossings from X GSE ~ 6 R e to X GSE ~ –48 R e . We compare IBEX BS and MP crossing locations to those from IMP-8, Geotail, Cluster, Magion-4, ISEE, and Magnetospheric Multiscale Mission, and we find that IBEX crossing locations overlap with the BS and MP locations inferred from these other data sets. In this paper, we demonstrate how IBEX can be used to identify magnetosheath crossings, and extend boundary observations well past the terminator, thus further constraining future models of magnetosheath boundaries. Furthermore, we use the IBEX data set to show observational evidence of near-Earth magnetotail squeezing during periods of strong interplanetary magnetic field B y .

79 ASTRONOMY AND ASTROPHYSICS↗

A Framework for Performing Multiscale Stochastic Progressive Failure Analysis of Composite Structures

A framework is presented that enables coupled multiscale analysis of composite structures. The recently developed, free, Finite Element Analysis-Micromechanics Analysis Code (FEAMAC) software couples the Micromechanics Analysis Code with Generalized Method of Cells (MAC/GMC) with ABAQUS to perform micromechanics based FEA such that the nonlinear composite material response at each integration point is modeled at each increment by MAC/GMC. As a result, the stochastic nature of fiber breakage in composites can be simulated through incorporation of an appropriate damage and failure model that operates within MAC/GMC on the level of the fiber. Results are presented for the progressive failure analysis of a titanium matrix composite tensile specimen that illustrate the power and utility of the framework and address the techniques needed to model the statistical nature of the problem properly. In particular, it is shown that incorporating fiber strength randomness on multiple scales improves the quality of the simulation by enabling failure at locations other than those associated with structural level stress risers.

Bednarcyk, Brett A.↗

The Development of Directional Decohesion Finite Elements for Multiscale Failure Analysis of Metallic Polycrystals

Atomistic simulations of intergranular fracture have indicated that grain-scale crack growth in polycrystalline metals can be direction dependent. At these material length scales, the atomic environment greatly influences the nature of intergranular crack propagation, through either brittle or ductile mechanisms, that are a function of adjacent grain orientation and direction of crack propagation. Methods have been developed to obtain cohesive zone models (CZM) directly from molecular dynamics simulations. These CZMs may be incorporated into decohesion finite element formulations to simulate fracture at larger length scales. A new directional decohesion element is presented that calculates the direction of Mode I opening and incorporates a material criterion for dislocation emission based on the local crystallographic environment to automatically select the CZM that best represents crack growth. The simulation of fracture in 2-D and 3-D aluminum polycrystals is used to illustrate the effect of parameterized CZMs and the effectiveness of directional decohesion finite elements.

Saether, Erik↗

Multilevel Algorithm for Atmospheric Data Assimilation

A multiscale algorithm for the problem of optimal statistical interpolation of observed data has been developed. This problem includes the calculation of the vector of the 'analyzed' (best estimated) atmosphere flow field w(sup a) by the formula: w(sup a) = w(sup f) + P(sup f) H(sup T) y, where the quantity y is defined by the equation (H P(sup f) H(sup T) + R)y = w(sup o) - H w(sup f), using the given model forecast first guess w(sup f) and the vector of observations w(sup o); H is an interpolation operator from the regular grid to the observation network, P(sup f) is the forecast error covariance matrix, and R is the observation error covariance matrix. At this initial stage the case of univariate analysis of single level radiosonde height data is considered. The matrix R is assumed to be diagonal, and the matrix P(sup f) is assumed to be given by the formula P(sub ij)(sup f) = sigma(sub i)(sup f) mu(sub ij) sigma(sub j)(sub f), where mu(sub ij) is a smooth, decreasing function of the distance between the i-th and the j-th points. In this paper we describe a multiscale iterative process based on a multiresolution, simultaneous displacement technique and a localized variational calculation of iteration parameters.

Brandt, Achi↗

BASIN-3D: A brokering framework to integrate diverse environmental data

Diverse observational and simulation datasets are needed to understand and predict complex ecosystem behavior over seasonal to decadal and century time-scales. Integration of these datasets poses a major barrier towards advancing environmental science, particularly due to differences in the structure and formats of data provided by various sources. Here, we describe BASIN-3D (Broker for Assimilation, Synthesis and Integration of eNvironmental Diverse, Distributed Datasets), a data integration framework designed to dynamically retrieve and transform heterogeneous data from different sources into a common format to provide an integrated view. BASIN-3D enables users to adopt a standardized approach for data retrieval and avoid customizations for the data type or source. We demonstrate the value of BASIN-3D with two use cases that require integration of data from regional to watershed spatial scales. The first application uses the BASIN-3D Python library to integrate time-series hydrological and meteorological data to provide standardized inputs to analytical and machine learning codes in order to predict the impacts of hydrological disturbances on large river corridors of the United States. The second application uses the BASIN-3D Django framework to integrate diverse time-series data in a mountainous watershed in East River, Colorado, United States to enable scientific researchers to explore and download data through an interactive web portal. Thus, BASIN-3D can be used to support data integration for both web-based tools, as well as data analytics using Python scripting and extensions like Jupyter notebooks. The framework is expected to be transferable to and useful for many other field and modeling studies.

Varadharajan, C↗

Progress in Modeling Global Atmospheric CO2 Fluxes and Transport: Results from Simulations with Diurnal Fluxes

Progress in better determining CO2 sources and sinks will almost certainly rely on utilization of more extensive and intensive CO2 and related observations including those from satellite remote sensing. Use of advanced data requires improved modeling and analysis capability. Under NASA Carbon Cycle Science support we seek to develop and integrate improved formulations for 1) atmospheric transport, 2) terrestrial uptake and release, 3) biomass and 4) fossil fuel burning, and 5) observational data analysis including inverse calculations. The transport modeling is based on meteorological data assimilation analysis from the Goddard Modeling and Assimilation Office. Use of assimilated met data enables model comparison to CO2 and other observations across a wide range of scales of variability. In this presentation we focus on the short end of the temporal variability spectrum: hourly to synoptic to seasonal. Using CO2 fluxes at varying temporal resolution from the SIB 2 and CASA biosphere models, we examine the model's ability to simulate CO2 variability in comparison to observations at different times, locations, and altitudes. We find that the model can resolve much of the variability in the observations, although there are limits imposed by vertical resolution of boundary layer processes. The influence of key process representations is inferred. The high degree of fidelity in these simulations leads us to anticipate incorporation of realtime, highly resolved observations into a multiscale carbon cycle analysis system that will begin to bridge the gap between top-down and bottom-up flux estimation, which is a primary focus of NACP.

Collatz, G. James↗

Generative learning of densities on manifolds

A generative modeling framework is proposed that combines diffusion models and manifold learning to efficiently sample data densities on manifolds. The approach utilizes Diffusion Maps to uncover possible low-dimensional underlying (latent) spaces in the high-dimensional data (ambient) space. Two approaches for sampling from the latent data density are described. The first is a score-based diffusion model, which is trained to map a standard normal distribution to the latent data distribution using a neural network. The second one involves solving an Itô stochastic differential equation in the latent space. Additional realizations of the data are generated by lifting the samples back to the ambient space using Double Diffusion Maps , a recently introduced technique typically employed in studying dynamical system reduction; here the focus lies in sampling densities rather than system dynamics. The proposed approaches enable sampling high dimensional data densities restricted to low-dimensional, a priori unknown manifolds. The efficacy of the proposed framework is demonstrated through a benchmark problem and a material with multiscale structure.

Double diffusion maps↗

Multiscale Normalizing Flows for Gauge Theories

Scale separation is an important physical principle that has previously enabled algorithmic advances such as multigrid solvers. Previous work on normalizing flows has been able to utilize scale separation in the context of scalar field theories, but the principle has been largely unexploited in the context of gauge theories. This work gives an overview of a new method for generating gauge fields using hierarchical normalizing flow models. This method builds gauge fields from the outside in, allowing different parts of the model to focus on different scales of the problem. Numerical results are presented for $U(1)$ and $SU(3)$ gauge theories in 2, 3, and 4 spacetime dimensions.

Abbott, Ryan↗

Grid Convergence of High Order Methods for Multiscale Complex Unsteady Viscous Compressible Flows

Grid convergence of several high order methods for the computation of rapidly developing complex unsteady viscous compressible flows with a wide range of physical scales is studied. The recently developed adaptive numerical dissipation control high order methods referred to as the ACM and wavelet filter schemes are compared with a fifth-order weighted ENO (WENO) scheme. The two 2-D compressible full Navier-Stokes models considered do not possess known analytical and experimental data. Fine grid solutions from a standard second-order TVD scheme and a MUSCL scheme with limiters are used as reference solutions. The first model is a 2-D viscous analogue of a shock tube problem which involves complex shock/shear/boundary-layer interactions. The second model is a supersonic reactive flow concerning fuel breakup. The fuel mixing involves circular hydrogen bubbles in air interacting with a planar moving shock wave. Both models contain fine scale structures and are stiff in the sense that even though the unsteadiness of the flows are rapidly developing, extreme grid refinement and time step restrictions are needed to resolve all the flow scales as well as the chemical reaction scales.

Sjoegreen, B.↗

Efficient Multiscale Optoelectronic Prediction for Conjugated Polymers

Conjugated polymers represent a high-potential material class due to the tunability of their optoelectronic properties via straightforward processing protocols. However, to in silico tailor these optoelectronic properties, one must compute the conformationally dependent electronic structure over mesoscopic length and time scales. This task represents a challenging multiscale computational problem in which quantum chemistry and atomistic or coarse-grained molecular dynamics must be integrated. Recently, we introduced a methodology, called electronic coarse graining (ECG), that utilizes artificial neural networks to compute ab initio quality electronic structures using only the reduced degrees of freedom of coarse-grained models. Here, we adapt ECG to variable-molecular weight conjugated oligomers by casting the problem in the framework of sequence detection. A machine-learning (ML) architecture employing one-dimensional convolutional neural networks and long short-term memory networks is utilized to compute ground-state orbital energies, charge density distributions, and optical spectra solely from the coarse-grained model's configurational degrees of freedom. Robust molecular weight transferability for ECG is established via a A-ML approach that leverages model electronic Hamiltonians for ground and excited-state property determination. The accuracy and transferability of the ECG methodology opens the door for scalable optoelectronic property prediction in conjugated polymers directly from coarse-grained degrees of freedom.

36 MATERIALS SCIENCE↗

Fourier-MIONet: Fourier-enhanced multiple-input neural operators for multiphase modeling of geological carbon sequestration

Geologic carbon sequestration (GCS) is a safety-critical technology that aims to reduce the amount of carbon dioxide in the atmosphere, which also places high demands on reliability. Multiphase flow in porous media is essential to understand CO 2 migration and pressure fields in the subsurface associated with GCS. However, numerical simulation for such problems in 4D is computationally challenging and expensive, due to the multiphysics and multiscale nature of the highly nonlinear governing partial differential equations (PDEs). It prevents us from considering multiple subsurface scenarios and conducting real-time optimization. Here, we develop a Fourier-enhanced multiple-input neural operator (Fourier-MIONet) to learn the solution operator of the problem of multiphase flow in porous media. Fourier-MIONet utilizes the recently developed framework of the multiple-input deep neural operators (MIONet) and incorporates the Fourier neural operator (FNO) in the network architecture. Once Fourier-MIONet is trained, it can predict the evolution of saturation and pressure of the multiphase flow under various reservoir conditions, such as permeability and porosity heterogeneity, anisotropy, injection configurations, and multiphase flow properties. Compared to the enhanced FNO (U-FNO), the proposed Fourier-MIONet has 90% fewer unknown parameters, and it can be trained in significantly less time (about 3.5 times faster) with much lower CPU memory (<15%) and GPU memory (<35%) requirements, to achieve similar prediction accuracy. In addition to the lower computational cost, Fourier-MIONet can be trained with only 6 snapshots of time to predict the PDE solutions for 30 years. Furthermore, we observed that Fourier-MIONet can maintain good accuracy when predicting out-of-distribution (OOD) data. The excellent generalizability of Fourier-MIONet is enabled by its adherence to the physical principle that the solution to a PDE is continuous over time. Furthermore, the developed Fourier-MIONet makes it possible to solve the long-time evolution of geological carbon sequestration in a large-scale three-dimensional space accurately and efficiently.

97 MATHEMATICS AND COMPUTING↗

Magnetic Reconnection: Theoretical and Observational Perspectives: Preface

Magnetic reconnection is a fundamental plasma-physical process by which energy stored in a magnetic field is converted, often explosively, into heat and the kinetic energy of the charged particles that constitute the plasma. It occurs in a variety of astrophysical settings, ranging from the solar corona to pulsar magnetospheres and winds, as well as in laboratory fusion experiments, where it is responsible for sawtooth crashes. First proposed by R.G. Giovanelli in the late I 940s as the mechanism responsible for solar flares, magnetic reconnection was invoked at the beginning of the space age to explain not just solar flares but also the transfer of energy, mass, and momentum from the solar wind to Earth's magnetosphere and the subsequent storage and release of the transferred energy in the magnetotai\. During the half century or so that has followed the seminal theoretical works by J.W. Dungey, P.A. Sweet, E.N. Parker, and H.E. Petschek, in-situ measurements by Earth-orbiting satellites and remote-sensing observations of the solar corona have provided a growing body of evidence for the occurrence of reconnection at the Sun, in the solar wind, and in the near-Earth space environment. The last thirty years have also seen the development of laboratory reconnection experiments at a number of institutions. In parallel with the efforts of experimentalists in both space and laboratory plasma physics, theorists have investigated, analytically and with the help of increasingly powerful MHD, hybrid, and kinetic numerical simulations, the structure of the diffusion region, the factors controlling the rate, onset, and cessation of reconnection, and the detailed physics that enables the demagnetization of the ions and electrons and the topological reconfiguration of the magnetic field. Moreover, the scope of theoretical reconnection studies has been extended well beyond solar system and laboratory plasmas to include more exotic astrophysical plasma systems whose strong (10(exp 14)-10(exp 15) G) magnetic fields require that models of reconnection in these systems incorporate quantum electrodynamical, special relativistic, and radiative effects. The papers collected in this topical issue of Space Science Reviews cover different aspects of recent theoretical and observational work on magnetic reconnection in solar and space physics, astrophysics, and laboratory plasma physics. They derive from presentations given at a workshop on magnetic reconnection held in the Yosemite National Park, February 8-12,2010. The intent of the workshop was to stimulate, through a combination of tutorial talks, shorter focused talks, and extensive informal discussions, an interdisciplinary dialogue among members of the different research communities working on the problem of magnetic reconnection. One of the motivating considerations for holding the workshop was its relevance to NASA's Magnetospheric Multiscale (MMS) mission, scheduled for launch in 2014. The four identically instrumented MMS spacecraft are designed to study reconnect ion in Earth's magnetosphere and, specifically, to probe the electron diffusion region in order to determine the microphysical processes that enable the change in the topology of the magnetic field. Building on the achievements of the multi spacecraft Cluster and THEMIS missions, MMS will use the magnetosphere as an astrophysical plasma laboratory in which to test, through in-situ measurement of the plasma, energetic particles, and electric and magnetic fields, various models and theories that have emerged during the past twenty years, a period of extraordinarily productive theoretical and observational work.

Lewis, W. S.↗

Performance Evaluation of the Meteorology and Air Quality Conditions From Multiscale WRF-CMAQ Simulations for the Long Island Sound Tropospheric Ozone Study (LISTOS)

The Long Island Sound (LIS) Tropospheric Ozone Study was a multi-agency collaborative field campaign conducted during the summer of 2018 to improve the understanding of ozone chemistry and transport from New York City to areas downstream, especially the LIS and adjacent Connecticut coastline. Measurements made during this campaign were leveraged to test and evaluate the coupled WRF-CMAQ model at 12 km, 4 and 1.33 km horizontal grid spacing. Special attention was placed on the model's representation of sea breeze circulations, low level jets, and boundary layer evolution. The evaluation suggests using higher resolutions resulted in improved surface meteorology statistics throughout the whole summer, with temperature biases seeing the biggest statistical improvements when using 1.33-km grid spacing, going from –0.12 to 0.08 K. Additionally, 4-km grid spacing provided the biggest advantage when simulating ozone over the region of interest, with biases being reduced from 2.40 to 0.57 to 0.37 ppbV with increased resolution. Case studies of two high ozone concentration events (July 10 and August 6) revealed that sound breezes and low-level jets had a critical role in transporting pollutant-rich, shallow marine air masses from the LIS inland over the Connecticut coast. Modifications were made to the representation of sea surface temperatures, which subsequently improved the simulation of surface ozone predictions.

54 ENVIRONMENTAL SCIENCES↗

VOTCA: multiscale frameworks for quantum and classical simulations in soft matter

Many physical phenomena in liquids and soft matter are multiscale by nature and can involve processes with quantum and classical degrees of freedom occurring over a vast range of length- and timescales. Examples range from structure formation processes of complex polymers or even polymer blends (Svaneborg & Everaers, 2023) on the classical side to charge and energy transport and conversion processes (Lee et al., 2019) involving explicit electronic and, therefore, quantum information. The Versatile Object-oriented Toolkit for Coarse-graining Applications (VOTCA) provides multiscale frameworks built on a comprehensive set of methods for the development of classical coarse-grained potentials (VOTCA-CSG) as well as state-of-the art excited state electronic structure methods based on density-functional and many-body Green’s function theories, coupled in mixed quantum-classical models and used in kinetic network models (VOTCA-XTP).

97 MATHEMATICS AND COMPUTING↗

Multiscale Framework Simulates Utility-Scale Wind Plant in Its Natural Environment. First-ever simulation could lead to a vital understanding of wind plant performance

Wind plant performance depends on local environmental conditions—and understanding those conditions is crucial to research on wind plant dynamics. Factors such as sloping terrain, surface type and cover, atmospheric heating and cooling, and weather events all play a role in determining the winds that turbines convert into power. However, most models used for wind plant design and operation do not sufficiently account for environmental effects. This can limit our understanding of wind plant dynamics and, thus, our ability to design, site, and operate wind plants for maximum power production and reliability. To meet this challenge, Arthur and other LLNL researchers partnered with scientists at the National Center for Atmospheric Research; the University of California, Berkeley; the University of Colorado, Boulder; and Texas Tech University to develop and demonstrate a novel, mesoscale-tomicroscale wind plant modeling framework.

17 WIND ENERGY↗

Topology induced crossover between Langevin, subdiffusion, and Brownian diffusion regimes in supercooled water

Despite extensive studies of supercooled water, it remains challenging to understand its peculiar dynamic anomalous properties. In this work, we integrated full atomistic simulations of supercooled water over the temperature range of room temperature to 200 K using a quantum-mechanics-based polarizable force field with the dressed dynamics method that couples fast collision events and slow reorganization dynamics of hydrogen-bond networks. Our analysis unveils the salient multiscale features in the transient relaxation dynamics of supercooled water. A classical Langevin behavior dominates at fast timescales, while long-time relaxations unveil two different activation barriers in two temperature regions: below and above 230 K. The modulation of the entropy spectrum by temperature is elucidated in terms of a three-state model underlined by the complexity of the water dynamics associated with a topological transition of a strong hydrogen-bond network. This state-dependent network topology is quantitatively characterized by power-law exponents of inverse network connectivity from 200 to 298 K. Finally, this work provides valuable guidance for further studies on the transient relaxation dynamics of supercooled water.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗