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A treatment of the phenomenon of fast diffusion in lead is presented. The model used is based upon the fast diffusion of free solute interstitials. The very large negative enhancement coefficients found in the Pb-(Au, Ag) systems is explained by the formation of first and second order clusters of vacancies and substitutional solute atoms.
The Control-Oriented Transport SIMulator (COTSIM) is an advanced equilibrium and transport code designed for simulating tokamak discharges at computational speeds suitable for control applications. COTSIM’s modular framework enables users to select models that balance accuracy with speed according to specific needs, allowing the code to operate from fast to faster-than-real-time performance levels. This work presents recent enhancements to COTSIM’s predictive accuracy for NSTX-U scenarios, achieved by integrating neural-network-based surrogate models and self-consistent equilibrium calculations. To improve source deposition predictions, a surrogate model for NUBEAM has been incorporated. Additionally, a surrogate model for the Multi-Mode Module (MMM) now supports predictions of anomalous thermal, momentum, and particle diffusivities—key factors for modeling the evolution of temperature and rotation. Each surrogate model was specifically trained for the NSTX-U operational regime to enhance COTSIM’s accuracy while maintaining computational efficiency. Moreover, COTSIM now couples fixed-boundary equilibrium solvers with its transport solvers, enabling self-consistent predictions of plasma profiles and equilibrium evolution over the discharge. Simulation results demonstrate strong agreement between COTSIM and TRANSP predictions for NSTX-U discharges. These substantial advancements expand COTSIM’s utility in model-based control applications for NSTX-U. Potential applications include simultaneous optimization of equilibrium and transport scenarios, integration into digital twins, real-time profile estimation (e.g., temperature and rotation) from limited or noisy measurements, and advanced feedback-based scenario control.
The phase field approach to modeling fracture uses a diffuse damage field to represent cracks. This representation mollifies singularities that arise in computations with sharp interface models and some of the resultant difficulties in the mathematical and numerical treatment of fracture. Phase field fracture models have proven effective at representing crack propagation, branching, and merging. Specific formulations, beginning with brittle fracture, have also been shown to converge to classical solutions. Extensions to cover the range of material failure, including ductile and cohesive fracture, lead to an array of possible models. There exists a large body of literature focusing on this class of models and on the impact of model form on the predicted crack evolution. However, there have not been systematic studies into how optimal models may be chosen. Here, we take a first step in this direction by developing formal methods for identification of the best parsimonious model of phase field fracture given full-field data on the damage and deformation fields. We consider some of the main models that have been used for the degradation of elastic response due to damage and its propagation. Our approach builds upon Variational System Identification (VSI), a weak form variant of the Sparse Identification of Nonlinear Dynamics (SINDy). Furthermore, in this first communication we focus on synthetically generated data but we also consider central issues associated with the use of experimental full-field data, such as data sparsity and noise.
This progress report (Level 3 Milestone Number M3SF-24LL010302052) summarizes research conducted at Lawrence Livermore National Laboratory (LLNL) within the Crystalline Work Package Number SF-24LL01030205. The research is focused on actinide and radionuclide sequestration in hydrothermally altered repository materials. In FY24, we completed a rigorous analysis of Se sorption to iron oxide phases using our L-SCIE sorption database. This effort explicitly accounts for surface titration behavior of oxide surfaces that was the subject of a recently published manuscript (Han et al., 2023). With this in mind, our Se sorption analysis now yields a more robust workflow for developing self-consistent surface complexation modeling approaches that can be adapted to specific SCM conceptual and numerical approaches (i.e. non-electrostatic, diffuse layer, triple layer models). In late FY24, we plan to publish the results of our comprehensive surface complexation modeling of Se(IV) and Se(VI) sorption to iron oxide mineral phases and provide a path forward to developing robust radionuclide sorption models for use in performance assessment. In FY24, we also submitted a manuscript summarizing our approach to integrating radionuclide sorption and coprecipitation phenomena and evaluation of radionuclide partitioning values across a range of radionuclides relevant to performance assessment. We demonstrated our approach in detail using Se sorption and coprecipitation with iron oxide minerals as a test case. This manuscript was recently accepted for publication in Applied Geochemistry (Balboni et al., Accepted). We also continued experiments to identify radionuclide interaction with hydrothermally altered crystalline repository and backfill materials. Recent research performed at Los Alamos National Laboratory (LANL) and Sandia National Laboratory (SNL) has provided key insights regarding the hydrothermal alteration behavior of bentonite backfill in the presence of repository materials (steel, concrete, etc.). We are now examining how mineral alteration affects retardation behavior of a suite of radionuclides of interest to repository performance assessment. These experiments also allow us to test the predictive ability of our component additivity approach to surface complexation and ion exchange. Our guiding hypothesis is that a robust surface complexation/ion exchange model and associated database, developed using our L-SCIE approach, can effectively predict changes in radionuclide sorption behavior resulting from the hydrothermal alteration of mineralogy in a repository near field. A short update of results to date is presented below.
The paper presents a brief account of various turbulence models employed in the computation of turbulent flows, and evaluates the application of these models to selected internal flow configurations. The main conclusions of this analysis are: (1) the k-epsilon model is used in a majority of all the two-dimensional flow calculations reported in the literature; (2) modified forms of the k-epsilon model improve the performance for flows with streamline curvature and heat transfer; (3) for flows with swirl, the k-epsilon model performs rather poorly (the algebraic stress model performs better in this case); and (4) for flows with regions of secondary flow (noncircular duct flows), the algebraic stress model performs fairly well. Two important factors in the numerical solution of the model equations, namely false diffusion and inlet boundary conditions, are discussed. The existence of countergradient transport and its implications in turbulence modeling are examined. Finally, some recommendations for improving the model performance are made. The need for detailed experimental data in flows with strong curvature is emphasized.
The dwarf spheroidal satellite galaxies of the Milky Way are some of the most dark-matter-dominated objects known. Due to their proximity, high dark matter content, and lack of astrophysical backgrounds, dwarf spheroidal galaxies are widely considered to be among the most promising targets for the indirect detection of dark matter via gamma rays. Here we report on gamma ray observations of 25 Milky Way dwarf spheroidal satellite galaxies based on 4 years of Fermi Large Area Telescope (LAT) data. None of the dwarf galaxies are significantly detected in gamma rays, and we present gamma ray flux upper limits between 500MeV and 500 GeV. We determine the dark matter content of 18 dwarf spheroidal galaxies from stellar kinematic data and combine LAT observations of 15 dwarf galaxies to constrain the dark matter annihilation cross section. We set some of the tightest constraints to date on the annihilation of dark matter particles with masses between 2 GeV and 10TeV into prototypical standard model channels. We find these results to be robust against systematic uncertainties in the LAT instrument performance, diffuse gamma ray background modeling, and assumed dark matter density profile.
Nanowire arrays present many unique advantages for solar-to-chemical energy conversion. One possible advantage is that photon recycling between neighboring nanowires has the potential to increase solar energy conversion efficiencies. Here, in this work, we explore three underlying mechanisms of optical and electronic coupling between neighboring nanowires─incident photon scattering, photon recycling, and charge-carrier transport from the photoexcited nanowire to the neighboring nanowire via the underlying substrate─using single nanowire-level microscopy and spectroscopy measurements. We present a comprehensive analysis of light absorption and emission of a single nanowire at open circuit, and subsequent re-absorption and re-emission by a neighboring nanowire. We developed a novel correlated single nanowire microspectroscopy and widefield imaging methodology to spatially resolve photon communication pathways between neighboring nanowires and selectively image re-emitted and reflected photons. We developed unique multiphysics models to couple wave optics and semiconductor photophysics to especially isolate contributions from photon recycling and electronic transport to photon emission from neighboring nanowires. By systematically varying the morphologies of the nanowires modeled, we identified pathways to maximize photon recycling between neighboring nanowires. We concluded that the measured photoluminescence is more strongly influenced by the diffusion of charge carriers as compared to photon recycling in materials with moderate-to-large charge-carrier mobilities (>10 cm 2 V –1 s –1 ), and that photon recycling dictates photoluminescence intensity only when the charge-carrier mobility is low (<1 cm 2 V –1 s –1 ). The experimental and simulation platforms developed herein for photon management strategies can be leveraged by the semiconductor photocatalysis community to enhance solar-to-chemical conversion efficiencies in semiconductor nanowire arrays.
An advanced engineering model has been developed to aid in the analysis and design of hydrogen/oxygen chemical rocket engines. The complete multispecies, chemically reacting and multidiffusing Navier-Stokes equations are modelled, including the Soret thermal diffusion and the Dufour energy transfer terms. In addition to the spectrum of multispecies aspects developed, the model developed in this study is also conservative in axisymmetric flow for both inviscid and viscous flow environments and the boundary conditions employ a viscous, chemically reacting, reference plane characteristics method. Demonstration cases are presented for a 1030:1 area ratio nozzle, a 25 lbf film cooled nozzle, and a transpiration cooled plug and spool rocket engine. The results indicate that the thrust coefficient predictions of the 1030:1 and the 25 lbf film cooled nozzle are within 0.2 to 0.5 percent, respectively, of experimental measurements when all of the chemical reaction and diffusion terms are considered. Further, the model's predictions agree very well with the heat transfer measurements made in all of the nozzle test cases. The Soret thermal diffusion term is demonstrated to have a significant effect on the predicted mass fraction of hydrogen along the wall of the nozzle in both the laminar flow 1030:1 nozzle and the turbulent flow plug and spool nozzle analysis cases performed. Further, the Soret term was shown to represent an important fraction of the diffusion fluxes occurring in a transpiration cooled rocket engine.
The measurements performed by particle physics experiments must account for the imperfect response of the detectors used to observe the interactions. One approach, unfolding, statistically adjusts the experimental data for detector effects. Recently, generative machine learning models have shown promise for performing unbinned unfolding in a high number of dimensions. However, all current generative approaches are limited to unfolding a fixed set of observables, making them unable to perform full-event unfolding in the variable dimensional environment of collider data. A novel modification to the variational latent diffusion model (VLD) approach to generative unfolding is presented, which allows for unfolding of high- and variable-dimensional feature spaces. The performance of this method is evaluated in the context of semi-leptonic t\bar{t} t t ‾ production at the Large Hadron Collider.
The hydrodynamic model is further verified by applying to a gain-guided single mode VCSEL. DC effects of D(sub NN): (1) increase threshold current J(sub th) and decrease slope efficiency; (2) within the studied range (50% pumping within threshold and realistic diffusion coefficient for a single mode), the L-I relation scales with the relative Injection current (J/J(sub th) - 1). AC effects of D(sub NN): (1) decrease spectral bandwidth and responsivity of direct-current modulation; (2) within the studied range, the frequency response follows the same formal dependence as predicted without diffusion and under a linear gain model, while the resonant frequency position similarly scales with the relative injection current; (3) therefore, it is concluded that the AC effects of D(sub NN) is purely of static nature and reflected via its influence on ot and J(sub th). Within this study, the nonlinear effects of D(sub NN) are mostly reproducible with an equivalent constant diffusion coefficient.
We have recently shown that spiral density waves propagating in accretion disks can undergo a parametric instability by resonantly coupling with and transferring energy into pairs of inertial waves (or inertial-gravity waves when buoyancy is important). In this paper, we perform inviscid three-dimensional global hydrodynamic simulations to examine the growth and consequence of this instability operating on the spiral waves driven by a Jupiter-mass planet in a protoplanetary disk. We find that the spiral waves are destabilized via the spiral wave instability (SWI), generating hydrodynamic turbulence and sustained radially alternating vertical flows that appear to be associated with long wavelength inertial modes. In the interval 0.3 R{sub p}⩽R⩽0.7R{sub p}, where R {sub p} denotes the semimajor axis of the planetary orbit (assumed to be 5 au), the estimated vertical diffusion rate associated with the turbulence is characterized by α{sub diff}∼ (0.2--1.2)×10{sup −2}. For the disk model considered here, the diffusion rate is such that particles with sizes up to several centimeters are vertically mixed within the first pressure scale height. This suggests that the instability of spiral waves launched by a giant planet can significantly disperse solid particles and trace chemical species from the midplane. In planet formation models where the continuous local production of chondrules/pebbles occurs over Myr timescales to provide a feedstock for pebble accretion onto these bodies, this stirring of solid particles may add a time constraint: planetary embryos and large asteroids have to form before a gas giant forms in the outer disk, otherwise the SWI will significantly decrease the chondrule/pebble accretion efficiency.
Artificial neural networks (ANNs) were developed to accurately predict the self-diffusion constants for pure components in liquid, gas and super critical phases. The ANNs were tested on an experimental database of 6625 self-diffusion constants for 118 different chemical compounds. The presence of multiple phases results in a heavy skew in the distribution of diffusion constants and multiple approaches were used to address this challenge. First, an ANN was developed with the raw diffusion values to assess what the main drawbacks of this direct method were. The first approach for improving the predictions involved taking the log 10 of diffusion to provide a more uniform distribution and reduce the range of target output values used to develop the ANN. The second approach involved developing individual ANNs for each phase using the raw diffusion values. Results show that the log transformation leads to a model with the best self-diffusion constant predictions and an overall average absolute deviation (AAD) of 6.56%. The resultant ANN is a generalized model that can be used to predict diffusion across all three phases and over a diverse group of compounds. The importance of each input feature was ranked using a feature addition method revealing that the density of the compound has the largest impact on the ANN prediction of self-diffusion constants in pure compounds.
Moisture sorption and diffusion exacerbate hygrothermal aging and can significantly alter the chemical and mechanical properties of polymeric-based components over time. In this study, we employ a multipronged multi-scale approach to model and understand moisture diffusion and sorption processes in polyimide polymers. A reactive transport model with triple-mode sorption (i.e., Henry’s, Langmuir, and pooling), experiments, and first principles atomistic computations were combined to synergistically explore representative systems of Kapton H and Kapton HN polymers. We find that the CaHPO 4 processing aid used in Kapton HN increases the total moisture uptake (~0.5 wt%) relative to Kapton H. Henry’s mode is found to play a major role in moisture uptake for both materials, accounting for >90% contribution to total uptake.However, the pooling mode uptake in Kapton HN was ~5 times higher than in Kapton H. First principles thermodynamics calculations based on density functional theory predict that water molecules chemisorb (with binding energy ~17–25 kcal/mol) on CaHPO 4 crystal surfaces. We identify significant anisotropy in surface binding affinity, suggesting a possible route to tune and mitigate moisture uptake in Kapton-based systems through controlled crystal growth favoring exposure of CaHPO 4 (101) surfaces during manufacturing.
The Einstein Deep Survey fields are used to reexamine the problem of the origin of the extragalactic diffuse X-ray background. An approach based on fluctuation analysis is adopted, and a sophisticated Monte Carlo scheme is used to compare the X-ray images with a variety of models representing point and diffuse source contributions. Results are presented for two classes of models: those which determine the flux cutoff beyond which pointlike sources fail to contribute significantly if the log N-log S slope is fixed at the measured value of -1.5, and those which set limits on the slope of the log N-log S relation below its last measured point at the Deep Survey threshold. In both cases, it is found that the known population of X-ray-emitting quasars and any reasonable extrapolations therefrom do not comprise the entire X-ray background.
A general model of particle acceleration by plasma waves coupled with adiabatic radial diffusion in a planetary magnetic field is developed. The model assumes that a spectrum of lower hybird waves is present to resonantly accelerate ions transverse to the magnetic field. The steady state Green's function for the combined radial diffusion and wave acceleration equation is found in terms of a series expansion. The results provide a rigorous demonstration of how a quasi-Maxwellian distribution function is formed in the absence of particle collisons and elucidate the nature of turbulent heating of magnetospheric plasmas. The solution is applied to the magnetosphere of Neptune for which a number of examples are given illustrating how the spectrum of pickup N(+) ions from Triton evolves.
Vacancy diffusion serves a crucial role in many important kinetic behaviors and properties of multicomponent alloys. Essential questions, however, persist regarding how chemical complexity affects diffusion and what unique characteristics, if any, set these alloys apart from traditional metals. Using neural network kinetics model, we study vacancy diffusion in NbMoTa alloy across a broad temperature range (2600 to 800 K). Unlike pure metals, the two key diffusion parameters—diffusion correlation factor f and activation energy ΔG m —are not constant in alloys, but instead substantially decrease with decreasing temperature. This temperature dependence arises from a reduced number of active vacancy jump pathways at lower temperatures, leading to more correlated diffusion. Upon examining vacancy diffusion throughout the entire compositional space of the Nb-Mo-Ta system, we discover that the slowest vacancy diffusion surprisingly occurs in the non-equimolar region, rather than the equimolar concentration where the configurational entropy is highest. The diffusion barrier spectrum, characterizing the diffusion energy landscape, is an intrinsic material characteristic, which controls both f and ΔG m and, thereby, the diffusivity. Lastly, we find that the vacancy diffusion rate drops noticeably in the presence of local chemical order in the NbMoTa system, particularly for MoTa alloys with long-range B2 order.
A 2-terminal, dual-junction, epitaxially integrated, GaAsP/Si tandem solar cell with an 3rd party certified efficiency of 23.4 % was fabricated via MOCVD growth on an ex-situ produced Si sub cell. The drastic efficiency improvement over the authors previous peer-reviewed demonstration of such a device architecture is examined. Critical advancements in top cell design to maximize short wavelength response were critical in enabling improved top cell response. An in-depth analysis of this champion tandem cell has identified key loss mechanisms which elucidate the pathway for further efficiency gains. First, voltage dependent collection efficiency in the GaAsP top cell is the primary cause of fill factor losses currently limiting efficiency. Analysis of spectrally resolved I–V measurements and analytical device modeling and indicate poor diffusion length due to elevated dislocation densities as the likely cause for the voltage dependent collection efficiency. Second, modeling for the GaAs 0.75 P 0.25 top cell, using experimental data at multiple dislocation densities, provides quantitative understanding of the current and voltage losses associated with threading dislocations providing a clear efficiency pathway with reduction in dislocation density. Finally, Si subcell modeling identifies the pathway for further Si subcell advances over the present, simplistic design, which has yet to employ the known benefits of rear surface texture or dielectric passivation.
Compared to full-order numerical models, the state-variable model (SVM) achieves multiple orders of reduction in computation time for simulation of electrochemical/thermal transport and reaction processes occurring inside lithium-ion and similar batteries with porous electrode construction. The model efficiently describes Li diffusion dynamics in active material and electrolyte phases, charge transfer kinetics, charge balance, and electron transport processes. The model requires a separate pre-processing step to identify SVM parameters.