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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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At least 613 records · Page 34
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

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.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2016

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.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2014

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.

36 MATERIALS SCIENCE↗
DOE OSTI2014

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.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2014

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.

36 MATERIALS SCIENCE↗
DOE OSTI2015

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.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2017

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.

42 ENGINEERING↗
DOE OSTI2017

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.

36 MATERIALS SCIENCE↗
DOE OSTI2015

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.

58 GEOSCIENCES↗
DOE OSTI2017

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.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2018

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.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2019

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.

17 WIND ENERGY↗
DOE OSTI2020

A Bayesian Approach for Statistical–Physical Bulk Parameterization of Rain Microphysics. Part I: Scheme Description

A novel framework is proposed for the bulk parameterization of rain microphysics: the Bayesian Observationally Constrained Statistical–Physical Scheme (BOSS). It is designed to facilitate direct constraint by observations using Bayesian inference. BOSS combines existing process-level microphysical knowledge with flexible process rate formulations and parameters constrained by observations within a Bayesian framework. Furthermore, using a raindrop size distribution (DSD) normalization method that relates DSD moments to one another via generalized power series, generalized multivariate power expressions are derived for the microphysical process rates as functions of a set of prognostic DSD moments. The scheme is flexible and can utilize any number and combination of prognostic moments and any number of terms in the process rate formulations. This means that both uncertainty in parameter values and structural uncertainty associated with the process rate formulations can be investigated systematically, which is not possible using traditional schemes. In this paper, BOSS is compared to two- and three-moment versions of a traditional bulk rain microphysics scheme (denoted as MORR). It is demonstrated that some process formulations in MORR are analytically equivalent to the generalized power expressions in BOSS using one or two terms, while others are not. BOSS is able to replicate the behavior of MORR in idealized one-dimensional rainshaft tests, but with a much more flexible and systematic design. Part II of this study describes the application of BOSS to derive rain microphysical process rates and posterior parameter distributions in Bayesian experiments using Markov chain Monte Carlo sampling constrained by synthetic observations.

58 GEOSCIENCES↗
DOE OSTI2013

Birds Shed RNA-Viruses According to the Pareto Principle

A major challenge in disease ecology is to understand the role of individual variation of infection load on disease transmission dynamics and how this influences the evolution of resistance or tolerance mechanisms. Such information will improve our capacity to understand, predict, and mitigate pathogen-associated disease in all organisms. In many hostpathogen systems, particularly macroparasites and sexually transmitted diseases, it has been found that approximately 20% of the population is responsible for approximately 80% of the transmission events. Although host contact rates can account for some of this pattern, pathogen transmission dynamics also depend upon host infectiousness, an area that has received relatively little attention. Therefore, we conducted a meta-analysis of pathogen shedding rates of 24 host (avian) – pathogen (RNA-virus) studies, including 17 bird species and five important zoonotic viruses. We determined that viral count data followed the Weibull distribution, the mean Gini coefficient (an index of inequality) was 0.687 (0.036 SEM), and that 22.0% (0.90 SEM) of the birds shed 80% of the virus across all studies, suggesting an adherence of viral shedding counts to the Pareto Principle. The relative position of a bird in a distribution of viral counts was affected by factors extrinsic to the host, such as exposure to corticosterone and to a lesser extent reduced food availability, but not to intrinsic host factors including age, sex, and migratory status. These data provide a quantitative view of heterogeneous virus shedding in birds that may be used to better parameterize epidemiological models and understand transmission dynamics.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

Hicks Pries, Sulman, et al 2018. In situ incubation of 13C-labeled litter in three soil pits at Blodgett Forest, CA from 2013 to 2016

Even though over half of the world's soil organic carbon (SOC) is stored in subsoils (>20 cm deep), and the old ages of subsoil OC indicate its cycling differs from surface SOC, there are few studies examining in situ decomposition processes in deep soils. The purpose of this dataset is to elucidate these processes. In this study we added 13C-labeled fine roots to 15, 55, and 95 cm depths of a well-characterized coniferous forest Alfisol and monitored the amount of root-derived C remaining over 6, 12, and 30 months. We recovered the root-derived C in microbial phospholipid fatty acids (PLFAs) after 6 months and in coarse (>2 mm) particulate, fine (<2 mm) particulate, and dense, mineral-associated pools after 6, 12, and 30 months. Overall, root decomposition in the first 6 months was similar among all depths but significantly diverged at 30 months with faster decomposition at 15 cm than at 95 cm. There were more fungal and Gram negative-associated PLFAs at 15 cm than at 95 cm, and 13C analysis revealed those microbial groups preferred the added root carbon to native SOC. Mineral-associations were not the cause of slower decomposition at depth because similar amounts of applied root C was recovered in the dense fraction at all depths. The largest difference among depths was in the amount of root C recovered in the coarse particulate fraction, which was greater at 95 cm (50%) than at 15 cm (15%). Slower decomposition of the particulate pool at depth likely contributed to the increase in C:N ratios and depletion of δ13C values below 60 cm depth in our soil profiles. Simulations of these soils using the CORPSE model, which incorporates microbial priming effects and mineral stabilization of SOC, reproduced patterns of particulate and mineral-associated SOC over both time and depth and suggested that a lack of priming by root exudates at depth could account for the slower decomposition rate of particulate root material. Decomposition of deep particulate SOC may increase if root exudation or dissolved OC transport to depth increases.This dataset includes (1) characterization of the soil pits prior to litter addition, (2) characterization of how the litter changed after 6, 12, and 30 months, and (3) the CORPSE model structure, parameterization, and output.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

MENT-Flow: maximum-entropy phase space tomography using normalizing flows

Generative models can be trained to reproduce low-dimensional projections of high-dimensional phase space distributions. Normalizing flows are generative models that parameterize invertible transformations, allowing exact probability density evaluation and sampling. Consequently, flows are unbiased entropy estimators and could be used to solve the high-dimensional maximum-entropy tomography (MENT) problem. In this work, we evaluate a flow-based MENT solver (MENT-Flow) against exact maximum-entropy solutions and Minerbo's iterative MENT algorithm in two dimensions.

Hoover, Austin↗