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The influence of initial conditions on specific features of the formation and development of a cathode-directed ionization wave between two flat electrodes in argon at atmospheric pressure was studied at nanosecond time resolution using a high-speed photoelectron monitor and numerical simulations based on the two-dimensional axisymmetric diffusion–drift model.
This project is a data-driven analysis of approaches to facilitate solar adoption in LMI communities and areas with high potential benefits to the electric grid. The cornerstone of the project was two waves of randomized field experiments. The first wave involved novel approaches to reaching LMI communities based on insights from literature. The second wave involved areas of high potential of solar to the electricity grid, as determined in collaboration with the local utilities in Connecticut. Both waves were followed by surveys to develop insight into the effectiveness of different approaches. A third key element of the project is the development of new agent-based models of solar energy diffusion that explicitly incorporate the real spatial and temporal constraints that influence solar diffusion, based on real-world data on solar adoption. The project also involved outreach and dissemination of key findings to relevant stakeholders, including a guidebook on strategies to accelerate solar deployment to low and moderate income communities, multiple academic publications, and numerous presentations of the research findings.
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The chemical vapor infiltration (CVI) process involves infiltrating a porous preform with reacting gases that undergo chemical transformation at high temperatures to deposit the ceramic phase within the pores, ultimately leading to a dense composite. The conventional CVI process in composite manufacturing needs to follow an isothermal approach to minimize temperature differences between the external and internal surfaces of the preform, ensuring that reactive gases infiltrate internal pores before external surfaces seal. This study addresses the challenge of premature pore closure in CVI processes through microwave heating. A frequency-domain microwave solver is developed in Open-FOAM to investigate volumetric heating mechanisms within the preform. Through numerical studies, we demonstrate the capability of microwave heating of creating an inside-out temperature inversion. This inversion accelerates reactions proximal to the preform center, effectively mitigating the risk of premature external pore closure and ensuring uniform densification. The results reveal a significant enhancement in temperature inversion when high-permittivity reflectors are incorporated to generate resonant waves. This microwave heating strategy is then coupled with high-fidelity direct numerical simulation (DNS) of reacting flow, enabling the analysis of resulting densification processes. The DNS simulation includes detailed chemistry and realistic diffusion coefficients. The numerical results can be used to estimate the impact of microwave-induced temperature inversion on densification in productions.
A long-standing area of research for Eulerian shock wave physics codes has been the treatment of strength and damage for materials. Here we present a method that will aid in the analysis of strength and failure in shock physics applications where excessive diffusion of critical variables can occur and control the solution outcome. Eulerian methods excel for large deformation simulations in general but are inaccurate in capturing structural behavior. Lagrangian methods provide better structural response, but finite element meshes can become tangled. Therefore, a technique for merging Lagrangian and Eulerian treatments of material response, within a single numerical framework, was implemented in the Multiple Component computational shock physics hydrocode. The capability is a Lagrangian/Eulerian Particle Method (LEPM) that uses particles to interface a Lagrangian treatment of material strength with a more traditional Eulerian treatment of the Equation of State (EOS). Lagrangian numerical methods avoid the advection diffusion found in Eulerian methods, which typically strongly affects strength constitutive law internal variables, such as equivalent plastic strain, porosity and/or damage. The Lagrangian capability enhances existing capabilities and permits accurate predictions of high rate, large deformation and/or shock of mechanical structures.
Although current phenomenological burn models are useful for describing the average or bulk reactive flow behaviour of heterogeneous explosives, one fundamental weakness inherent to these models is the loss of detailed microstructural information at the scale of the calculation. In order to include the effects of the microstructure, and in particular the underlying material heterogeneities that influence the build-up to detonation, a new paradigm is put forth for modelling sub-grid, reaction-induced fluctuations (i.e. “hot spots”) at the continuum level. This modelling approach assumes that the reaction rate is stochastic, rather than deterministic, and it uses Langevin-type equations with a mathematical framework built upon Itô calculus and Lambourn's CIM model for shocked heterogeneous explosives. This approach follows directly from our previous letter, Ref. [1], and is inspired by the probability density function (pdf) methods used in turbulent reactive flows. Here, the stochastic burn model is derived, implemented, and exercised far beyond what has been shown in previous work. New hydrocode simulation results demonstrate the role of stochastic fluctuations during shock initiation; these fluctuations are approximated by collections of discrete particles, that evolve with drift (i.e. deterministic) and diffusion (i.e. stochastic) coefficients. Furthermore, the particle values are propagated and averaged to calculate the heat release, yield strength, and material impedance in each computational cell. Hydrocode simulation results further show how the fluctuating hot spot energy may or may not be transmitted to the wave front, and result in a detonation wave-like structure. The fundamental stochastic nature of this model permits simulations to have varying outcomes with the same initial conditions; this allows for go/no-go estimation (e.g., marginal or failed detonations), which might possibly be calibrated using the statistical distributions from real materials. Mesoscale calculations of shocked heterogeneous explosives also show that these fluctuations are physically justified (i.e., Ref. [2]), and it is hypothesized that the pdf functions provide a link between the meso(grain) and continuum scales for practical engineering calculations. Hence, our approach is a paradigm shift for building efficient, reduced order continuum burn models, that represent the sub-grid stochastic behaviour of shocked heterogeneous solid explosives.
Recent efforts to characterize small ( M w < 3 ) seismic events at local distances have become more important because of the increased observation of human-triggered and induced seismicity and the need to advance nuclear explosion monitoring capabilities. The signals generated by low-magnitude seismic sources necessitate the use of nearby short-period observations, which are sensitive to local geological heterogeneity. Local to near-regional distance ( < 300 km ) surface and shear waves can dominate short-period observations from small, shallow seismic sources. In this work, we utilize these observations to estimate precise, relative locations and magnitudes of ~ 700 industrial mining events in Wyoming, using nearly 360,000 observations. The precise, relative location estimates (with formal location uncertainty estimates of less than 1 km) collapse a diffuse collection of mining events into discrete clusters associated with individual blasting operations. We also invert the cross-correlation amplitudes to estimate precise, relative moment magnitude estimates, which help validate and identify disparities in the event sizes reported by regional network catalogs. Joint use of multiple phases allows for the inclusion of more seismic events due to the increase in the number of observations. In some cases, using a single phase allowed us to relocate only 50% of the original reported seismic events within a cluster. Combining shear- and surface-wave phases increased the number of events to above 90% of the original events, allowing us to characterize a broader range of event sizes, source to station distances, and event distributions. This analysis takes a step toward making a fuller characterization of small industrial seismic events observed at local distances.
Within the ongoing U.S.-based program on the development of liquid metal plasma-facing components, numerical simulations and analyses are performed to address the feasibility of the open-surface Li divertor. In the previous scoping studies (Smolentsev, 2021), heat-removal capabilities of the divertor were assessed using a simplified flow model for a slug-type velocity profile and constant flow thickness. Here, new analyses take into account forces acting on the flowing Li layer. Three reduced-order mathematical models are applied under the conditions of the U.S. Fusion Nuclear Science Facility (FNSF) to access magnetohydrodynamic (MHD) flow development effects, velocity distribution, and surface waves: 1) fully developed MHD flow; 2) quasi-2-D developing MHD flow; and 3) multiphase MHD flow. The obtained results for MHD flows and the surface heat flux computed with the plasma code scrape-off layer plasma simulation for ITER (SOLPS-ITER) are then used as input data to compute the temperature distribution in the divertor by solving the convection–diffusion energy equation.
Drift-wave turbulence produces anomalous transport via cross-correlations between fluctuations. This transport has profound implications for confinement, structure formation, and virtually all aspects of the non-linear turbulent dynamics. Herein, we use a data-driven method based on deep learning in order to study turbulent transport in the 2D Hasegawa–Wakatani system and infer a reduced mean-field model from numerical solution. In addition to the usual turbulent diffusion, we find an effect which couples the particle flux to the local gradient of vorticity, which tends to modulate the density profile. The direct coupling to the shear is relatively weak. In addition, the deep learning method finds a model for spontaneous zonal flow generation by negative viscosity, stabilized by non-linear and hyperviscous terms. We compare these results to analytic calculations using quasilinear theory and wave kinetics, finding qualitative agreement, though the calculations miss certain higher-order effects. A simplified, 1-D model for the evolution of the profile, flow, and intensity based on the deep learning results is solved numerically and compared to previous models for staircasing based on bistability. We see that the physics uncovered by the deep learning method provided simple explanations for the formation of zonal structures in the density, flow, and turbulence fields. We highlight the important role of symmetry in the deep learning method and speculate on the portability of the method to other applications.
The use of visible photon fluxes to influence catalytic reactions on metal nanoparticle surfaces has attracted attention based on observations of reaction mechanisms and selectivity not observed under equilibrium heating. These observations suggest that photon fluxes can selectively impact the rates of certain elementary steps, creating nonequilibrium energy distributions among various reaction pathways. However, quantitative studies validating these hypotheses on metal nanoparticle surfaces are lacking. Here, we examine the influence of continuous wave visible photon fluxes on the CO desorption rates from 1 to 2 nm diameter Pt and Pd nanoparticle surfaces supported on γ-Al 2 O 3 . Temperature-programmed desorption measurements quantified via diffuse reflectance infrared Fourier transform spectroscopy demonstrate that visible photon fluxes significantly enhanced the rate of CO desorption from Pt nanoparticles in a wavelength-dependent manner. 440 nm photons most efficiently promoted CO desorption from Pt nanoparticle surfaces, aligning with the excitation energy for the interfacial electronic transition within the Pt–CO bond. Conversely, visible photon fluxes had no measurable influence on CO desorption rates from Pd nanoparticle surfaces after accounting for photon-induced heating. Density functional theory calculations demonstrate that the Pt–CO bond exhibits a narrower LUMO resonance, stronger coupling between the photoexcitation and forces induced on the metal–C bond, and vibrational energy dissipation that more effectively couples to desorption as compared to Pd–CO. These results demonstrate the specificity photons provide in facilitating chemical reactions on metal nanoparticle surfaces and substantiate the idea that photon fluxes can steer processes and outcomes of catalytic reactions in ways not achievable by equilibrium heating.
Scientific codes are an indispensable link between theory and experiment; in (astro-)plasma physics, such numerical tools are one window into the universe’s most extreme flows of energy. The discretization of Maxwell’s equations – needed to make highly magnetized (astro)physical plasma amenable to its numerical modeling – introduces numerical diffusion. It acts as a source of dissipation independent of the system’s physical constituents. Understanding the numerical diffusion of scientific codes is the key to classifying their reliability. It gives specific limits in which the results of numerical experiments are physical. We aim at quantifying and characterizing the numerical diffusion properties of our recently developed numerical tool for the simulation of general relativistic force-free electrodynamics by calibrating and comparing it with other strategies found in the literature. Our code correctly models smooth waves of highly magnetized plasma. We evaluate the limits of general relativistic force-free electrodynamics in the context of current sheets and tearing mode instabilities. We identify that the current parallel to the magnetic field (j ∥ ), in combination with the breakdown of general relativistic force-free electrodynamics across current sheets, impairs the physical modeling of resistive instabilities. We find that at least eight numerical cells per characteristic size of interest (e.g., the wavelength in plasma waves or the transverse width of a current sheet) are needed to find consistency between resistivity of numerical and of physical origins. High-order discretization of the force-free current allows us to provide almost ideal orders of convergence for (smooth) plasma wave dynamics. The physical modeling of resistive layers requires suitable current prescriptions or a sub-grid modeling for the evolution of j ∥ .
Here, we present a generative learning framework for probabilistic sampling that extends Probabilistic Learning on Manifolds (PLoM), which is designed to generate statistically consistent realizations of a random vector in a finite-dimensional Euclidean space, informed by a (representative) set of observations. In its original form, PLoM constructs a reduced-order probabilistic model by combining three main components: (a) kernel density estimation to approximate the underlying probability measure, (b) Diffusion Maps to characterize the manifold of the data, and (c) a reduced-order Itô Stochastic Differential Equation (ISDE) to sample from the learned distribution. However, its sampling dynamics are posed in the ambient space and the retained number of reduced coordinates is chosen by projection-reconstruction error. In practice, this often (i) requires more coordinates than the data’s intrinsic dimension to achieve stable sampling and (ii) lacks a smooth, basis-independent lifting back to the data domain; moreover, standard Diffusion Maps emphasize harmonic eigenfunctions and can miss non-harmonic latent structure. We address these limitations by decoupling geometry learning from sampling: a first Diffusion Maps pass identifies non-harmonic coordinates on which we formulate a full-order ISDE directly in the latent space, while Double Diffusion Maps captures multiscale geometric features and Geometric Harmonics (GH) learns a smooth lifting map to the ambient variables that is independent of the particular diffusion basis. This hybrid design preserves the system’s dynamical richness with a compact geometric representation and enables principled out-of-sample inference. The effectiveness and robustness of the proposed method are illustrated through two numerical studies: one based on data generated from two-dimensional Hermite polynomial functions and another based on high-fidelity simulations of a detonation wave in a reactive flow.
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.
We investigated the temperature dependence of the current fluctuations in thin films of the quasi-two-dimensional 1T-TaS 2 van der Waals material. The current fluctuations, determined from the derivative current–voltage characteristics of two-terminal 1T-TaS 2 devices, appear prominently at the electric fields that correspond to the transitions between various charge-density-wave macroscopic quantum condensate phases and at the onset of the depinning of the charge density wave domains. The depinning threshold field, ED, monotonically increases with decreasing temperature within the nearly commensurate charge-density-wave phase. The ED value increases with the decreasing 1T-TaS 2 film thickness, revealing the surface pinning of the charge density waves. Our analysis suggests that the domain depinning is pronounced in the nearly commensurate phase. It is induced by the electric field but facilitated by local heating. Furthermore, the measured trends for ED of the domain depinning are important for understanding the physics of charge density waves in quasi-two-dimensional crystals and for developing electronic devices based on this type of quantum materials.
The implementation of optimal strictly localized atomic orbitals basis for plutonium (Pu) using norm-conserving pseudopotential density-functional theory (DFT) is presented. The basis set was applied to the α, β, γ, δ, δ', and ε phases of Pu, δ-Pu surface, and δ-PuGa alloys. The computed properties of the Pu phases and δ-Pu surface were in good agreement with both available experimental data and prior DFT calculations based on plane-wave methodologies. Results for the δ-PuGa alloys were also in good agreement with experimental data. Finally, the reliability of the basis set was further demonstrated by using ab initio molecular dynamics to model the diffusion coefficient and activation barrier for atomic diffusion in a δ-PuGa alloy.
Laser-based techniques have become an appealing option for determining the thermal diffusivities (D) and conductivities (k) of nuclear materials that would otherwise prove too hazardous or difficult to measure otherwise. These techniques have enabled non-destructive and non-contact thermal transport measurements with micron-level spatial resolution. In this study, a new time-domain technique known as square-wave pulse thermoreflectance (SPTR) is described and used to determine D values of both uranium sesquisilicide (U 3 Si 2 ) and uranium nitride (UN) phases in a composite fuel. An extensive sensitivity analysis was conducted that identified the optimal measurement parameters of standard materials with a range of thermal transport properties comparable to those of various fuel types. The standards were measured using both SPTR as well as a spatial-domain thermoreflectance technique (SDTR). Several U 3 Si 2 and UN phase regions of polished UN/U 3 Si 2 (70/30 vol.%) samples were measured using both methods, and the resulting calculated D and k values are reported, with both techniques showing excellent agreement between samples. Finally, the ability to use the technique for local diffusivity mapping was demonstrated over a multiphase region, and could be used to determine thermal transport properties in precipitates, secondary phases, and irradiation-damaged regions of post-irradiated fuel samples.
Aftershocks of the 2012 Off-Coast of Sumatra Earthquake Sequence exhibit a complex and diffuse spatial distribution. The first-order complexity in aftershock distribution is clear and well beyond the influence of typical earthquake location uncertainty. The sequence included rupture of multiple fault segments, spatially separated. We use surface-wave based relative centroid locations to examine whether, at the small scale, the distribution of the aftershocks was influenced by location errors. Surface-wave based relative location has delineated precise oceanic transform fault earthquake locations in multiple regions. However, the relocated aftershocks off the coast of Sumatra seldom align along simple linear trends that are compatible with the corresponding fault strikes as estimated for the GCMT catalog. The relocation of roughly 60 moderate-earthquake epicentroids suggests that the faulting involved in the 2012 earthquake aftershock sequence included strain release along many short fault segments. Statistical analysis and temporal variations of aftershocks show a typical decay of the aftershocks but a relatively low number of aftershocks, as is common for intraplate oceanic earthquakes. Coulomb stress calculations indicate that most of the moderate-magnitude aftershocks are compatible with stress changes predicted by the large-event slip models. Furthermore the patterns in the aftershocks suggest that the formation of the boundary and eventual localization of deformation between the Indian and Australian plate is a complicated process.
Analytic treatment is presented of the electrostatic instability of an initially planar electron hole in a plasma of effectively infinite particle magnetization. It is shown that there is an unstable mode consisting of a rigid shift of the hole in the trapping direction. Its low frequency is determined by the real part of the force balance between the Maxwell stress arising from the transverse wavenumber $k$ and the kinematic jetting from the hole’s acceleration. The very low growth rate arises from a delicate balance in the imaginary part of the force between the passing-particle jetting, which is destabilizing, and the resonant response of the trapped particles, which is stabilizing. Nearly universal scalings of the complex frequency and $k$ with hole depth are derived. Particle in cell simulations show that the slow-growing instabilities previously investigated as coupled hole–wave phenomena occur at the predicted frequency, but with growth rates 2 to 4 times greater than the analytic prediction. This higher rate may be caused by a reduced resonant stabilization because of numerical phase-space diffusion in the simulations.