SEARCH · Search NASA
Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.
Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.
A new frequency domain hot-electron bolometer (HEB) mixer model is described, which takes into account the non-uniform temperature distribution found in superconducting diffusion-cooled devices.
Using dissipative particle dynamics (DPD), we simulate nanoscale segregation, water diffusion, and proton conductivity in hydrated sulfonated polystyrene (sPS). We employ a novel model [Lee et al. J. Chem. Theory Comput. 11(9), 4395-4403 (2015)] that incorporates protonation/deprotonation equilibria into DPD simulations. The polymer and water are modeled by coarse-grained beads interacting via short-range soft repulsion and smeared charge electrostatic potentials. The proton is introduced as a separate charged bead that forms dissociable Morse bonds with the base beads representing water and sulfonate anions. Morse bond formation and breakup artificially mimics the Grotthuss mechanism of proton hopping between the bases. The DPD model is parameterized by matching the proton mobility in bulk water, dissociation constant of benzenesulfonic acid, and liquid-liquid equilibrium of water-ethylbenzene solutions. The DPD simulations semi-quantitatively predict nanoscale segregation in the hydrated sPS into hydrophobic and hydrophilic subphases, water self-diffusion, and proton mobility. As the hydration level increases, the hydrophilic subphase exhibits a percolation transition from isolated water clusters to a 3D network. The analysis of hydrophilic subphase connectivity and water diffusion demonstrates the importance of the dynamic percolation effect of formation and breakup of temporary junctions between water clusters. The proposed DPD model qualitatively predicts the ratio of proton to water self-diffusion and its dependence on the hydration level that is in reasonable agreement with experiments.
Diffusion-induced grain boundary migration is a phenomenon in which a grain boundary moves in response to the driving forces generated by diffusing solute species. For example, diffusing solute species change the atomic volume in the host material, either by filling a vacancy with a misfitting solute atom or by expanding host lattices through interstitial diffusion. These volume changes are inhomogeneous and are stored as elastic energy in the material that drives grain boundaries. In this paper, we introduce our previously developed Cahn-Hilliard–phase-field-crystal model (CH-PFC) as a computational tool to investigate diffusion-induced grain boundary migration in crystalline materials. This multiscale phase-field model couples the composition field of a diffusing species with the crystallographic texture of a host material. We apply the CH-PFC model to battery electrodes and investigate whether interstitial solute diffusion induces grain growth in FePO 4 /LiFePO 4 electrodes. To this end, we compute grain growth in 60 FePO 4 electrodes by conducting two parallel trials: In the first trial, we cycle the electrode and calculate diffusion-induced grain growth. In the second trial, we do not cycle the electrode and calculate curvature-driven grain growth. We find a statistically significant grain growth in the cycled electrodes and negligible grain growth in the noncycled electrodes. Altogether, we show that the CH-PFC model not only predicts electrode microstructures as a function of the Li composition, but also predicts the crystallographic features of an electrode during battery operation.
Not Available
The objectives of the study were to establish the behavior of conditional scalar dissipation and diffusion at the extreme values of mass fraction and to derive and evaluate closure models for these terms using the scalar probability density function. The conditional scalar dissipation, its derivative with respect to mass fraction, and conditional scalar diffusion are all found to be zero at extreme values of mass fraction. A model for conditional scalar dissipation is derived which exhibits the correct behavior at the extreme values of scalar concentration.
Micro/meso/macroporous (hierarchical) zeolites show remarkable catalytic performance for reactions involving bulky reactants. However, quantitative assessment of the microstructural characteristics contributing to the observed performance remains elusive. Here, structure–activity relationships are established for a set of micro/mesoporous self-pillared pentasil (SPP) zeolites using two parallel liquid-phase reactions (benzyl alcohol alkylation and self-etherification) based on analysis of mass transport and reaction kinetics. A reaction–diffusion mathematical model is developed that quantitatively assigns the catalytic contributions of the external surface and micropores of SPP zeolites for these reactions. In addition, the effect of the zeolite external surface structure on the corresponding catalytic activity is quantitatively assessed by comparing SPP zeolites (with MFI structure) with MCM-22 (with MWW structure). This work demonstrates that reaction–diffusion modeling allows quantitative description of the catalytic performance of hierarchical zeolites and provides a model reaction to assess nm-sized characteristic diffusion lengths in MFI.
Scanning electron microscopy (SEM) is resource intensive, which limits its throughput in some applications. As an alternative, we propose applying computer vision and machine learning to generate high-quality synthetic SEM micrographs from micrographs obtained using light optical microscopy (LOM). Working with a correlated LOM/SEM dataset of dual-phase steel images, we test generative models of various architectures, including encoder-decoder networks, generative adversarial networks (GANs), and diffusion-based models. We find that the diffusion models significantly outperform other methods on both qualitative and quantitative assessments, while preserving key metallurgical meaning. This work establishes diffusion as the state-of-the-art for microscopy modality transfer and demonstrates the potential of AI-powered microscopy to enhance LOM with micron scale structural recreation.
Formation and motion of the silver nanoparticles inside an ion-exchanged soda-lime glass in the presence of a focused high intensity continuous wave Ar{sup +} laser beam (intensity: 9.2 x 10{sup 4} W/cm{sup 2}) have been studied in here. One-dimensional diffusion equation has been used to model the diffusion of the silver ions into the glass matrix, and a two-dimensional reverse diffusion model has been introduced to explain the motion of the silver clusters and their migration toward the glass surface in the presence of the laser beam. The results of the mentioned models were in agreement with our measurements on thickness of the ion-exchange layer by means of optical microscopy and recorded morphology of the glass surface around the laser beam axis by using a Mirau interferometer. SEM micrographs were used to extract the size distribution of the migrated silver particles over the glass surface.
Understanding of climate impact on ecosystems globally requires site-specific model calibration. Here we introduce a novel diffusion-based uncertainty quantification (DBUQ) method for efficient model calibration. DBUQ is a score-based diffusion model that leverages Monte Carlo simulation to estimate the score function and evaluates a simple neural network to quickly generate samples for approximating parameter posterior distributions. DBUQ is stable, efficient, and can effectively calibrate the model given diverse observations, thereby enabling rapid and site-specific model calibration on a global scale. This capability significantly advances Earth system modeling and our understanding of climate impacts on Earth systems. We demonstrate DBUQ's capability in E3SM land model calibration at the Missouri Ozark AmeriFlux forest site. Both synthetic and real-data applications indicate that DBUQ produces accurate parameter posterior distributions similar to those generated by Markov Chain Monte Carlo sampling but with 30X less computing time. This efficiency marks a significant stride in model calibration, paving the way for more effective and timely climate impact analyses.
Model problem comparisons in slab geometry are made between two forms of homogenized diffusion theory and enhanced diffusion theory. The pin-cell discontinuity factors for homogenized diffusion calculations are derived from homogenized variational nodal P1 response matrices and from standard finite differencing. Enhanced diffusion theory consists of applying quasi-reflected interface conditions to reduce variational nodal Pn response matrices to one degree of freedom per interface, without homogenization within the cell. As expected both homogenized diffusion methods preserve reaction rates exactly if the discontinuity factors are derived from the P 11 reference solutions. If no reference lattice solution is available, discontinuity factors may be approximated from single cells with reflected boundary conditions; the computational effort is then comparable to calculating the enhanced diffusion response matrices. In this situation enhanced diffusion theory gives the most accurate results and finite difference discontinuity factors the least accurate. (authors)
Data from a 1152×760×1280 direct numerical simulation of a Rayleigh-Taylor mixing layer modeled after a small-Atwood-number water-channel experiment is used to investigate the validity of gradient diffusion and similarity closures a priori. The budgets of the mean flow, turbulent kinetic energy, turbulent kinetic energy dissipation rate, heavy-fluid mass fraction variance, and heavy-fluid mass fraction variance dissipation rate transport equations across the mixing layer were previously analyzed at different evolution times to identify the most important transport and mixing mechanisms. Here a methodology is introduced to systematically estimate model coefficients as a function of time in the closures of the dynamically significant terms in the transport equations by minimizing the L2 norm of the difference between the model and correlations constructed using the simulation data. It is shown that gradient-diffusion and similarity closures used for the turbulent kinetic energy K, turbulent kinetic energy dissipation rate ε, heavy-fluid mass fraction variance S, and heavy-fluid mass fraction variance dissipation rate χ equations capture the shape of the exact, unclosed profiles well over the nonlinear and turbulent evolution regimes. Using order-of-magnitude estimates for the terms in the exact transport equations and their closure models, it is shown that several of the standard closures for the turbulent production and dissipation (destruction) must be modified to include Reynolds-number scalings appropriate for Rayleigh-Taylor flow at small to intermediate Reynolds numbers. The late-time, large Reynolds number coefficients are determined to be different from those used in shear flow applications and largely adopted in two-equation Reynolds-averaged Navier-Stokes (RANS) models of Rayleigh-Taylor turbulent mixing. In addition, it is shown that the predictions of the Boussinesq model for the Reynolds stress agree better with the data when additional buoyancy-related terms are included. It is shown that an unsteady RANS paradigm is needed to predict the transitional flow dynamics from early evolution times, analogous to the small Reynolds number modifications in RANS models of wall-bounded flows in which the production-to-dissipation ratio is far from equilibrium. Although the present study is specific to one particular flow and one set of initial conditions, the methodology could be applied to calibrations of other Rayleigh-Taylor flows with different initial conditions (which may give different results during the early-time, transitional flow stages, and perhaps asymptotic stage). Finally, the implications of these findings for developing high-fidelity eddy viscosity-based turbulent transport and mixing models of Rayleigh-Taylor turbulence are discussed.
Abstract Previous efforts to understand structure‐function relationships in high ionic conductivity materials for solid state batteries have predominantly relied on density functional theory (DFT‐) based ab initio molecular dynamics (MD). Such simulations, however, are computationally demanding and cannot be reasonably applied to large systems containing more than a hundred atoms. Here, an artificial neural network (ANN) is trained to accelerate the calculation of high accuracy atomic forces and energies used during such MD simulations. After carefully training a robust ANN for four and five element systems, nearly identical lithium ion diffusivities are obtained for Li 10 GeP 2 S 12 (LGPS) when benchmarking the ANN‐MD results with DFT‐MD. Applying the ANN‐MD approach, the effect of chlorine doping on the lithium diffusivity is calculated in an LGPS‐like structure and it is found that a dopant concentration of 1.3% maximizes ionic conductivity. The optimal concentration balances the competing consequences of effective atomic radii and dielectric constants on lithium diffusion and agrees with the experimental composition. Performing simulations at the resolution necessary to model experimentally relevant and optimal concentrations would be infeasible with traditional DFT‐MD. Systems that require a large number of simulated atoms can be studied more efficiently while maintaining high accuracy with the proposed ANN‐MD framework.
Boron diffusion from the gas phase in silicon carbide is described on the basis of a two-component model. 'Shallow' boron, i.e., boron at silicon sites, is a slow component with a high surface concentration. Its diffusivity is proportional to the concentration of positively charged intrinsic point defects, which are presumably interstitial silicon atoms. 'Deep' boron, i.e., impurity-defect pairs of boron-carbon vacancy, is a fast component with lower surface concentration. The ratio between the surface concentrations of the components depends on the pressure of silicon or carbon vapors in the gas phase. The diffusion and interaction of components are described by the set of diffusion-reaction equations. The diffusion retardation observed on the concentration-profile tail is related to the capture of impurity-defect pairs and excess vacancies by traps of background impurities and defects.
Diffusive-convective physical vapor transport (PVT) in cylindrical, vertical ampoules of aspect ratio (length/radius) between 0.5 and 10 was modeled numerically. The transport of a crystal forming component through an inert component that undergoes zero net transport was considered. Systems were treated in which: (1) with unequal molecular weight of the components and with temperature gradients typically employed in PVT, convective flow arises dominantly from solutal density gradients; and (2) with equal molecular weight of the components, convective flow can arise only from thermal expansion. It was found that, due to the diffusion-induced horizontal density gradients, buoyancy-driven convective flows are superimposed on the diffusive-advective fluxes without threshold. Net recirculation sets in adjacent to the growing interface, in contrast to the corresponding monocomponent situation where marginally stable convective modes fill the whole fluid space. Depending on the orientation of the main transport direction with respect to gravity, convection can either reduce or enhance the diffusion-induced radial concentration gradients. Significant enhancement of the net transport rate was found to occur only when the whole vapor space between source and growing crystal is filled by a convective recirculation roll. Solutal and thermal convection results are similar; yet for quantitative discussions, thermal and solutal Rayleigh numbers are not interchangeable in contrast to convective situations that lack net mass transport across the fluid space.
Local structure of dirubidium tetralithium tris(selenate(VI)) dihydrate - Rb{sub 2}Li{sub 4}(SeO{sub 4}){sub 3}{center_dot} 2H{sub 2}O has been determined basing on the modeling of X-ray diffuse scattering. The origin of observed structured diffuse streaks is SeO{sub 4} tetrahedra switching between two alternative positions in two quasi-planar layers existing in each unit cell and formation of domains with specific SeO{sub 4} tetrahedra configuration locally fulfilling condition for C-centering in the 2a Multiplication-Sign 2b Multiplication-Sign c superstructure cell. The local structure solution is characterized by a uniform distribution of rather large domains (ca. thousand of unit cells) in two layers, but also monodomains can be taken into account. Inside a single domain SeO{sub 4} tetrahedra are ordered along ab-diagonal forming two-string ribbons. Inside the ribbons SeO{sub 4} and LiO{sub 4} tetrahedra share the oxygen corners, whereas ribbons are bound to each other by a net of hydrogen bonds and fastened by corner sharing SeO{sub 4} tetrahedra of the neighboring layers. - Graphical abstract: Experimental sections of the reciprocal space showing diffraction effects observed for RLSO. Bragg spots are visible on sections with integer indices (1 kl section - on the left), streaks - on sections with fractional ones (1.5 kl section - on the right). At the center: resulting local structure of the A package modeled as a microdomain: two-string ribbons of ordered oxygen-corners-sharing SeO{sub 4} and LiO{sub 4} terahedra extended along ab-diagonal are seen; ribbons are bound by hydrogen bonds (shown in pink); the multiplied 2a Multiplication-Sign 2b unit cell is shown. Highlights: Black-Right-Pointing-Pointer X-ray diffuse scattering in RLSO was registered and modeled. Black-Right-Pointing-Pointer The origin of diffuse streaks is SeO{sub 4} tetrahedra switching in two structure layers. Black-Right-Pointing-Pointer The local structure is characterized by a uniform distribution of microdomains. Black-Right-Pointing-Pointer Inside a single domain SeO{sub 4} tetrahedra are ordered along ab-diagonal forming ribbons. Black-Right-Pointing-Pointer The ribbons are bound to each other by a net of hydrogen bonds.
Gas flow in shale is associated with both organic matter (OM) and inorganic matter (IOM) which contain nano-pores ranging in size from a few to hundreds of nano-meters. In addition to the non-continuum effect which leads to an apparent permeability of gas higher than the intrinsic permeability, the surface diffusion of adsorbed gas in organic pores also can influence the apparent permeability through its own transport mechanism. In this study, a generalized lattice Boltzmann model (GLBM) is employed for gas flow through the reconstructed shale matrix consisting of OM and IOM. The Expectation–Maximization (EM) algorithm is used to assign the pore size distribution to each component, and the dusty gas model (DGM) and generalized Maxwell–Stefan model (GMS) are adopted to calculate the apparent permeability accounting for multiple transport mechanisms including viscous flow, Knudsen diffusion and surface diffusion. Effects of pore radius and pressure on permeability of both IOM and OM as well as effects of Langmuir parameters on OM are investigated. The effect of total organic content and distribution on the apparent permeability of the reconstructed shale matrix at different surface diffusivity is also studied. It is found that the influence of pore size and pressure on the apparent permeability of organic matter is affected by the surface diffusion of adsorbed gas. Furthermore, surface diffusion plays a significant role in determining apparent permeability and the velocity distribution of shale matrix.
Deep geologic disposal of high activity and long-lived radioactive waste is gaining increasing support in many countries, where suitable low permeability geological formation in combination with engineered barriers are used to provide long term waste contaminant and minimise the impacts to the environment and risk to the biosphere. This modelling study examines the solute transport in fractured media under low flow velocities that are relevant to a deep geological environment. In particular, reactive solute transport through fractured media is studied using a 2-D model, that considers advection and diffusion, to explore the coupled effects of kinetic and equilibrium chemical processes. The effects of water velocity in the fracture, matrix porosity and diffusion on solute transport are investigated and discussed. Some illustrative modelled results are presented to demonstrate the use of the model to examine the effects of media degradation on solute transport, under the influences of hydrogeological (diffusion dominant) and microbially mediated chemical processes. The challenges facing the prediction of long term degradation such as cracks evolution, interaction and coalescence are highlighted. The potential of a novel microstructure informed modelling approach to account for these effects is discussed, particularly with respect to investigating multiple phenomena impact on material performance. The GRM code is used to examine the effects of media degradation for a geological waste disposal package, under the combined hydrogeological (diffusion dominant) and chemical effects in low groundwater flow conditions that are typical of deep geological disposal systems. An illustrative reactive transport modelling application demonstrates the use of the code to examine the interplay of kinetic controlled biogeochemical reactive processes with advective and diffusive transport, under the influence of media degradation. The initial model results are encouraging which show the disposal system to evolve in a physically realistic manner. In the example presented the reactive-transport coupling develops chemically reducing zones, which limit the transport of uranium. This illustrates the potential significance of media degradation and chemical effect on the transport of radionuclides which would need to be taken into account when examining the long-term behaviour and containment properties of the geological disposal system. Microstructure-informed modelling and its potential linkage with continuum flow modelling is a subject of ongoing studies. The approach of microstructure-informed modelling is discussed to provide insight and a mechanistic understanding of macroscopic parameters and their evolution. The proposed theoretical and methodological basis for microstructure-informed modelling of porous quasi-brittle media has the potential to develop into an explanatory and predictive tool for deriving mechanism-based, as opposed to phenomenological, evolution laws for macroscopic properties. These concepts in micro-scale modelling are likely to be applicable to the diffusion process, in addition to advective transport illustrated here for porous media. (authors)
The visible infrared imaging radiometer suite on the Suomi National Polar-orbiting Partnership satellite calibrates its reflective solar bands through observations of a sunlit solar diffuser (SD) panel. Sunlight passes through a perforated plate, referred to as the SD screen, before reaching the SD. It is critical to know whether the SD screen transmittance measured prelaunch is accurate. Several factors such as misalignments of the SD panel and the measurement apparatus could lead to errors in the measured transmittance and thus adversely impact on-orbit calibration quality through the SD. We develop a mathematical model to describe the transmittance as a function of the angles that incident light makes with the SD screen, and apply the model to fit the prelaunch measured transmittance. The results reveal that the model does not reproduce the measured transmittance unless the size of the apertures in the SD screen is quite different from the design value. We attribute the difference to the orientation alignment errors for the SD panel and the measurement apparatus. We model the alignment errors and apply our transmittance model to fit the prelaunch transmittance to retrieve the "true" transmittance. To use this model correctly, we also examine the finite source size effect on the transmittance. Furthermore, we compare the product of the retrieved "true" transmittance and the prelaunch SD bidirectional reflectance distribution function (BRDF) value to the value derived from on-orbit data to determine whether the prelaunch SD BRDF value is relatively accurate. The model is significant in that it can evaluate whether the SD screen transmittance measured prelaunch is accurate and help retrieve the true transmittance from the transmittance with measurement errors, consequently resulting in a more accurate sensor data product by the same amount.