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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.
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Theoretical Pathway toward Improved Reverse Osmosis Membrane Selectivity for Neutral Solutes: Inspiration from Gas Separations
We report highly rigid membrane materials with tailored structures have exhibited permeabilities and selectivities that exceed the polymer upper bound in gaseous and organic solvent separations for challenging mixtures containing species that are similar in size and shape. One potential question is whether such membrane materials can maintain meaningful guest diffusivities in situations where the microporous spaces are essentially full of guest molecules. Here, we use a simplified transition state theory approach to estimate the diffusivity of water and small organics within a microporous membrane. The transition state theory model is parameterized using experimental values from zeolites and carbon molecular sieve materials found in the literature. We demonstrate the differences in transport and Maxwell–Stefan diffusivities based on guest species loading with different isotherm behaviors. These calculations theorize a path forward for highly selective reverse osmosis membranes for aqueous phase separations.
Balancing Accuracy, Efficiency, and Flexibility in Radiation Calculations for Dynamical Models
Not Available
Seasonal and Morphological Controls on Nitrate Retention in Arctic Deltas
Abstract Estimates of nitrate loading to the Arctic Ocean are limited by the lack of field observations within deltas partly due to logistical constraints. To overcome this limitation, we use a remote sensing framework to estimate retention of nitrate in Arctic deltas. We achieve this by coupling hydrological and biogeochemical process models at the network scale for five major Arctic deltas. Binary masks of delta channels were used to simulate flow direction and magnitude through networks. Models were parameterized using historical and seasonal observations. Simulated nitrate retention ranged from 2.9% to 15% of the incoming load. Retention rates were largest during winter but smallest during spring conditions when increased discharges export large nitrate masses to the coast. Under future climate scenarios, retention rates fall by ∼1%–10%. Arctic deltas have an important effect on the magnitude of nitrate entering Arctic seas and the inclusion of processing in deltas can improve flux estimates.
Uptake of N 2 O 5 by aqueous aerosol unveiled using chemically accurate many-body potentials
The reactive uptake of N 2 O 5 to aqueous aerosol is a major loss channel for nitrogen oxides in the troposphere. Despite its importance, a quantitative picture of the uptake mechanism is missing. Here we use molecular dynamics simulations with a data-driven many-body model of coupled-cluster accuracy to quantify thermodynamics and kinetics of solvation and adsorption of N 2 O 5 in water. The free energy profile highlights that N 2 O 5 is selectively adsorbed to the liquid–vapor interface and weakly solvated. Accommodation into bulk water occurs slowly, competing with evaporation upon adsorption from gas phase. Leveraging the quantitative accuracy of the model, we parameterize and solve a reaction–diffusion equation to determine hydrolysis rates consistent with experimental observations. We find a short reaction–diffusion length, indicating that the uptake is dominated by interfacial features. The parameters deduced here, including solubility, accommodation coefficient, and hydrolysis rate, afford a foundation for which to consider the reactive loss of N 2 O 5 in more complex solutions.
Annual cycle observations of aerosols capable of ice formation in central Arctic clouds
The Arctic is warming faster than anywhere else on Earth, prompting glacial melt, permafrost thaw, and sea ice decline. These severe consequences induce feedbacks that contribute to amplified warming, affecting weather and climate globally. Aerosols and clouds play a critical role in regulating radiation reaching the Arctic surface. However, the magnitude of their effects is not adequately quantified, especially in the central Arctic where they impact the energy balance over the sea ice. Specifically, aerosols called ice nucleating particles (INPs) remain understudied yet are necessary for cloud ice production and subsequent changes in cloud lifetime, radiative effects, and precipitation. Here, we report observations of INPs in the central Arctic over a full year, spanning the entire sea ice growth and decline cycle. Further, these observations are size-resolved, affording valuable information on INP sources. Our results reveal a strong seasonality of INPs, with lower concentrations in the winter and spring controlled by transport from lower latitudes, to enhanced concentrations of INPs during the summer melt, likely from marine biological production in local open waters. This comprehensive characterization of INPs will ultimately help inform cloud parameterizations in models of all scales.
The roles of 4f- and 5f-orbitals in bonding: A magnetochemical, crystal field, density functional theory, and multi-reference wavefunction study
The electronic structures of 4f 3 /5f 3 Cp" 3 M and Cp" 3 M·alkylisocyanide complexes, where Cp" is 1,3-bis-(trimethylsilyl)cyclopentadienyl, are explored with a focus on the splitting of the f-orbitals, which provides information about the strengths of the metal–ligand interactions. While the f-orbital splitting in many lanthanide complexes has been reported in detail, experimental determination of the f-orbital splitting in actinide complexes remains rare in systems other than halide and oxide compounds, since the experimental approach, crystal field analysis, is generally significantly more difficult for actinide complexes than for lanthanide complexes. In this study, a set of analogous neodymium(III) and uranium(III) tris-cyclopentadienyl complexes and their isocyanide adducts was characterized by electron paramagnetic resonance (EPR) spectroscopy and magnetic susceptibility. The crystal field model was parameterized by combined fitting of EPR and susceptibility data, yielding an accurate description of f-orbital splitting. The isocyanide derivatives were also studied using density functional theory, resulting in f-orbital splitting that is consistent with crystal field fitting, and by multi-reference wavefunction calculations that support the electronic structure analysis derived from the crystal-field calculations. The results highlight that the 5f-orbitals, but not the 4f-orbitals, are significantly involved in bonding to the isocyanide ligands. The main interaction between isocyanide ligand and the metal center is a σ-bond, with additional 5f to π* donation for the uranium complexes. As a result, while interaction with the isocyanide π*-orbitals lowers the energies of the 5f xz 2 and 5f yz 2- orbitals, spin–orbit coupling greatly reduces the population of 5f xz 2 and 5f yz 2 in the ground state.
Molecular dynamics investigation of Na{sup +} in Na{sub 2}Ni{sub 2}TeO{sub 6}
An inter-atomic potential for Na{sub 2}Ni{sub 2}TeO{sub 6} in the Parrinello- Rahman-Vashishta (PRV) model is parameterized empirically. The potential reproduces variety of structural and transport properties of that material in good agreement with recent experimental results. The study provides fresh insights on the migration channels and mechanism of Na{sup +} in the system.
Fluctuating hydrodynamics of multicomponent membranes with embedded proteins
A simulation method for the dynamics of inhomogeneous lipid bilayer membranes is presented. The membrane is treated using stochastic Saffman-Delbrück hydrodynamics, coupled to a phase-field description of lipid composition and discrete membrane proteins. Multiple applications are considered to validate and parameterize the model. The dynamics of membrane composition fluctuations above the critical point and phase separation dynamics below the critical point are studied in some detail, including the effects of adding proteins to the mixture.
The segmented non-uniform dielectric module design for uniformity control of plasma profile in a capacitively coupled plasma chamber
Low-temperature plasma technique is one of the critical techniques in IC manufacturing process, such as etching and thin-film deposition, and the uniformity greatly impacts the process quality, so the design for the plasma uniformity control is very important but difficult. It is hard to finely and flexibly regulate the spatial distribution of the plasma in the chamber via controlling the discharge parameters or modifying the structure in zero-dimensional space, and it just can adjust the overall level of the process factors. In the view of this problem, a segmented non-uniform dielectric module design solution is proposed for the regulation of the plasma profile in a CCP chamber. The solution achieves refined and flexible regulation of the plasma profile in the radial direction via configuring the relative permittivity and the width of each segment. In order to solve this design problem, a novel simulation-based auto-design approach is proposed, which can automatically design the positional sequence with multi independent variables to make the output target profile in the parameterized simulation model approximate the one that users preset. This approach employs an idea of quasi-closed-loop control system, and works in an iterative mode. It starts from initial values of the design variable sequences, and predicts better sequences via the feedback of the profile error between the output target profile and the expected one. It never stops until the profile error is narrowed in the preset tolerance.
Effect of surface roughness and size of beam on squeeze-film damping—Molecular dynamics simulation study
We use large-scale molecular dynamics (MD) to characterize fluid damping between a substrate and an approaching beam. We focus on the near contact regime where squeeze film (where fluid gap is comparable to the mean free path of the gas molecules) and many-body effects in the fluid become dominant. The MD simulations provide explicit description of many-body and non-equilibrium processes in the fluid as well as the surface topography. We study how surface roughness and beam width increases the damping coefficient due to their effect on fluid mobility. We find that the explicit simulations are in good agreement with prior direct simulation Monte Carlo results except at near-contact conditions where many-body effects in the compressed fluid lead the increased damping and weaker dependence on beam width. We also show that velocity distributions near the beam edges and for short gaps deviate from the Boltzmann distribution indicating a degree of local non-equilibrium. These results will be useful to parameterize compact models used for microsystem device-level simulations and provide insight into mesoscale simulations of near-contact damping.
Selecting an Informative/Discriminating Multivariate Response for Inverse Prediction
nverse prediction is important in a wide variety of scientific and engineering contexts. One might use inverse prediction to predict fundamental properties/characteristics of an object using measurements obtained from it. This can be accomplished by “inverting” parameterized forward models that relate the measurements (responses) to the properties/characteristics of interest. Sometimes forward models are science based; but often, forward models are empirically based, using the results of experimentation. For empirically-based forward models, it is important that the experiments provide a sound basis to develop accurate forward models in terms of the properties/characteristics (factors). While nature dictates the causal relationship between factors and responses, experimenters can influence control of the type, accuracy, and precision of forward models that can be constructed via selection of factors, factor levels, and the set of trials that are performed. Whether the forward models are based on science, experiments or both, researchers can influence the ability to perform inverse prediction by selecting informative response variables. By using an errors-in-variables framework for inverse prediction, this paper shows via simple analysis and examples how the capability of a multivariate response (with respect to being informative and discriminating) can vary depending on how well the various responses complement one another over the range of the factor-space of interest. Insights derived from this analysis could be useful for selecting a set of response variables among candidates in cases where the number of response variables that can be acquired is limited by difficulty, expense, and/or availability of material.
Explicit inclusion of electronic correlation effects in molecular dynamics
Here, we design a quantum molecular dynamics method for strongly correlated electron metals. The strong electronic correlation effects are treated within a real-space version of the Gutzwiller variational approximation (GA), which is suitable for the inhomogeneity inherent in the process of quantum molecular dynamics (MD) simulations. We also propose an efficient algorithm based on the second-moment approximation to the electronic density of states for the search of the optimal variation parameters, from which the renormalized interatomic MD potentials are fully determined. By considering a minimal one-correlated-orbital Anderson model with parameterized spatial dependence of tight-binding hopping integrals, this fast GA-MD method is benchmarked with that using exact diagonalization to solve the GA variational parameters. The efficiency and accuracy are illustrated. We have demonstrated the effect of temperature coupled with electronic correlation on structural properties simulated with MD. This method will open up an unprecedented opportunity enabling large-scale quantum MD simulations of strongly correlated electronic materials.
Anomalous transport on regular fracture networks: Impact of conductivity heterogeneity and mixing at fracture intersections
We investigate here transport on regular fracture networks that are characterized by heterogeneity in hydraulic conductivity. We discuss the impact of conductivity heterogeneity and mixing within fracture intersections on particle spreading. We show the emergence of non-Fickian transport due to the interplay between the network conductivity heterogeneity and the degree of mixing at nodes. Specifically, lack of mixing at fracture intersections leads to subdiffusive scaling of transverse spreading but has negligible impact on longitudinal spreading. An increase in network conductivity heterogeneity enhances both longitudinal and transverse spreading and leads to non-Fickian transport in longitudinal direction. Based on the observed Lagrangian velocity statistics, we develop an effective stochastic model that incorporates the interplay between Lagrangian velocity correlation and velocity distribution. The model is parameterized with a few physical parameters and is able to capture the full particle transition dynamics.
Quantification of Arctic Soil and Permafrost Properties Using Ground-Penetrating Radar and Electrical Resistivity Tomography Datasets
Improving understanding of Arctic ecosystem climate feedback and parameterization of models that simulate freeze-Thaw dynamics require advances in quantifying soil and snow properties. Due to the significant spatiotemporal variability of soil properties and the limited information provided by point-scale measurements (e.g., cores), geophysical methods hold potential for improving soil and permafrost characterization. In this study, we evaluate the use of a ground-penetrating radar (GPR) to estimate thaw layer thickness, snow depth, and ice-wedge characteristics in an ice-wedge-dominated tundra region near Barrow, AK, USA. To this end, we analyze GPR and point-scale measurements collected along several parallel transects at the end of the growing season and the end of frozen season. In addition, we compare the structural information extracted from the GPR data with electrical resistivity tomography (ERT) information about ice-wedge characteristics. Our study generally highlights the value of GPR data collected in the frozen season, when conditions lead to the improved GPR signal-To-noise ratio, facilitate data acquisition, and reduce acquisition-related ecosystem disturbance relative to growing season. We document for the first time that GPR data collected during the frozen season can provide reliable estimates of active layer thickness and geometry of ice wedges. We find that the ice-wedge geometry extracted from GPR data collected during the frozen season is consistent with ERT data, and that GPR data can be used to constrain the ERT inversion. Consistent with recent studies, we also find that GPR data collected during the frozen season can provide good estimates of snow thickness, and that GPR data collected during the growing season can provide reliable estimate thaw depth. As a result, our quantification of the value of the GPR and ERT data collected during growing and frozen seasons paves the way for coupled inversion of the datasets to improve understanding of permafrost variability.
An Investigation of Potential Intermittency Induced Outage Modes for Wind Generation
In a deterministic contingency analysis (DCA), renewable generation such as wind is modeled similar to a conventional generator. In the current practices of a probabilistic contingency analysis (PCA), the outage parameters for a renewable generator reflect only failures of its parts. Intermittency due to wind speed changes or cloud cover may cause renewable generation to ramp up or down within a short time period and, after the transient is finished, stay at a certain generation level before a new ramp starts. A fast ramp-down transient can last for some time and thus, have an effect similar to a failure or de-rated generation of a conventional generator. This paper investigates potential outage modes that are induced by the intermittency of renewable generation, as well as the modeling and parameterization of such outage modes in the PCA framework. Historical time-series wind generation data are used in this study to extract different types of transients and perform statistical analyses to obtain input data such that the intermittency induced outages (IIOs) can be included in PCA.
The Second Wind Forecast Improvement Project (WFIP2): Observational Field Campaign
The science of wind energy forecasting has taken a leap forward with the unique meteorological observations gathered in complex terrain during the Second Wind Forecast Improvement Project (WFIP2). The Second Wind Forecast Improvement Project (WFIP2) is a U.S. Department of Energy (DOE) and National Oceanic and Atmospheric Administration (NOAA) funded program, with private-sector and university partners, which aims to improve the accuracy of numerical weather prediction (NWP) model forecasts of wind speed in complex terrain for wind energy applications. A core component of WFIP2 was an 18-month field campaign which took place in the U.S. Pacific Northwest between October 2015 and March 2017. A large suite of instrumentation was deployed in a series of telescoping arrays, ranging from 500 km across to a densely instrumented 2 x 2 km area similar in size to a high-resolution NWP model grid cell. Observations from these instruments are being used to improve our understanding of the meteorological phenomena that affect wind energy production in complex terrain, and to evaluate and improve model physical parameterization schemes. We present several brief case studies using these observations to describe phenomena that are routinely difficult to forecast, including wintertime cold pools, diurnally driven gap flows, and mountain waves/wakes. Observing system and data product improvements developed during WFIP2 are also described.