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DOE OSTI2024

Droplet Growth or Evaporation Does Not Buffer the Variability in Supersaturation in Clean Clouds

Abstract Water vapor supersaturation in clouds is a random variable that drives activation and growth of cloud droplets. The Pi Convection–Cloud Chamber generates a turbulent cloud with a microphysical steady state that can be varied from clean to polluted by adjusting the aerosol injection rate. The supersaturation distribution and its moments, e.g., mean and variance, are investigated for varying cloud microphysical conditions. High-speed and collocated Eulerian measurements of temperature and water vapor concentration are combined to obtain the temporally resolved supersaturation distribution. This allows quantification of the contributions of variances and covariances between water vapor and temperature. Results are consistent with expectations for a convection chamber, with strong correlation between water vapor and temperature; departures from ideal behavior can be explained as resulting from dry regions on the warm boundary, analogous to entrainment. The saturation ratio distribution is measured under conditions that show monotonic increase of liquid water content and decrease of mean droplet diameter with increasing aerosol injection rate. The change in liquid water content is proportional to the change in water vapor concentration between no-cloud and cloudy conditions. Variability in the supersaturation remains even after cloud droplets are formed, and no significant buffering is observed. Results are interpreted in terms of a cloud microphysical Damköhler number (Da), under conditions corresponding to , i.e., the slow-microphysics regime. This implies that clouds with very clean regions, such that is satisfied, will experience supersaturation fluctuations without them being buffered by cloud droplet growth. Significance Statement The saturation ratio (humidity) in clouds controls the growth rate and formation of cloud droplets. When air in a turbulent cloud mixes, the humidity varies in space and time throughout the cloud. This is important because it means cloud droplets experience different growth histories, thereby resulting in broader size distributions. It is often assumed that growth and evaporation of cloud droplets buffers out some of the humidity variations. Measuring these variations has been difficult, especially in the field. The purpose of this study is to measure the saturation ratio distribution in clouds with a range of conditions. We measure the in-cloud saturation ratio using a convection cloud chamber with clean to polluted cloud properties. We found in clouds with low concentrations of droplets that the variations in the saturation ratio are not suppressed.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2019

Compact hard x-ray split-delay system based on variable-gap channel-cut crystals

We present the concept and a prototypical implementation of a compact x-ray split-delay system that is capable of performing continuous on-the-fly delay scans over a range of ~10 ps with sub-100 nanoradian pointing stability. The system consists of four channel-cut silicon crystals, two of which have gradually varying gap sizes from intentional 5 deg asymmetric cuts. The delay adjustment is realized by linear motions of these two monolithic varying-gap channel cuts, where the x-ray beam experiences pairs of anti-parallel reflections, and thus becomes less sensitive in output beam pointing to motion imperfections of the translation stages. The beam splitting is accomplished by polished crystal edges. A high degree of mutual coherence between the two branches at the focus is observed by analyzing small-angle coherent x-ray scattering patterns. We envision a wide range of applications including single-shot x-ray pulse temporal diagnostics, studies of high-intensity x-ray–matter interactions, as well as measurement of dynamics in disordered material systems using split-pulse x-ray photon correlation spectroscopy.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

DOE OSTI2019

Data Assessment and Assimilation for Atmospheric Radiation Measurement Data Using Dynamic Bayesian Networks

A data-driven machine learning approach to scientific sensor data assessment may automatically learn directly from the structural and temporal patterns in abundant observations. The project has developed data assessment and assimilation models to screen datasets using dynamic Bayesian network (DBN) and deep learning (DL) methods, which rely on correlations between variables, across time, and between spatial locations, to detect poor or invalid data with a high degree of confidence.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2025

Greenhouse gas flux response in biochar- and compost-amended urban soils under simulated soil hydrologic dynamics

Understanding greenhouse gas emission dynamics in lawn soils is essential for improving climate change mitigation strategies in urban and suburban environments. This project measured fluxes of carbon dioxide (CO 2 ), methane (CH 4 ), and nitrous oxide (N 2 O) fluxes from turfgrass soil columns amended with biochar, compost, biochar-compost blend, control (no amendment) in a controlled greenhouse mesocosm experiment. This project simulated contrasting water saturation regimes consisting of normal irrigation with sprinkler, transient half or full saturation by water table manipulation, and subsequent drying phase under both sod or seeded grass conditions. Compost-amended columns (compost or biochar-compost blend) exhibited higher average CO 2 fluxes (6 µmol m-2 s-1) compared to biochar or control columns (4.5 – 5.0 µmol m-2 s-1) across all saturation levels under sod conditions. Seeded grass conditions generally resulted in less CO 2 emissions. The CO 2 fluxes were positively correlated with air temperature and negatively correlated with soil moisture. Biochar-amended columns retained high soil moisture (~95 %) throughout experiments, demonstrating superior moisture retention compared to compost. CH 4 and N 2 O fluxes exhibited temporal increases (5-8 nmol m-2 s-1) during saturation and drainage phases, indicating their sensitivity to hydrologic conditions. These findings suggest that temperature and amendment types are primary driver of CO 2 emissions, while CH 4 and N 2 O fluxes are more responsive to water saturation dynamics in lawn soils.

54 ENVIRONMENTAL SCIENCES↗

NASA NTRS1995

Intercomparison of hydrologic processes in global climate models

In this report, we address the intercomparison of precipitation (P), evaporation (E), and surface hydrologic forcing (P-E) for 23 Atmospheric Model Intercomparison Project (AMIP) general circulation models (GCM's) including relevant observations, over a variety of spatial and temporal scales. The intercomparison includes global and hemispheric means, latitudinal profiles, selected area means for the tropics and extratropics, ocean and land, respectively. In addition, we have computed anomaly pattern correlations among models and observations for different seasons, harmonic analysis for annual and semiannual cycles, and rain-rate frequency distribution. We also compare the joint influence of temperature and precipitation on local climate using the Koeppen climate classification scheme.

Lau, W. K.-M.↗

NASA NTRS1999

Observational Review of Gamma-Ray Bursts

The observed gamma-ray temporal, spectral, intensity and spatial distribution characteristics of GRBs, primarily from data obtained from the Compton Observatory, will be described. The talk will concentrate on recent studies of burst properties, correlations of GRB parameters and other statistical studies that have only recently come to light with the unprecedented sample of over two thousand GRBs, along with some mention of studies in progress by members of the BATSE team. Recent studies of possible observational biases, un-triggered GRBs and threshold calculations for BATSE will also be described.

Fishman, Gerald J.↗

NASA NTRS1999

Observational Review of Gamma-Ray Bursts

Fishman, Gerald J.↗

NASA NTRS2000

Joint Discussion/Mini-Workshop: Gamma-Ray Bursts and their Hosts

Fishman, Gerald J.↗

NASA NTRS2001

A Robust Retrieval of Water Vapor Column In Dry Arctic Conditions Using the Rotating Shadowband Spectroradiometer

A method to retrieve water vapor column using the 940-nm water vapor absorption band in dry Arctic conditions is presented. The retrievals with this method are stable with respect to uncertainties in instrument radiometric calibration, air pressure, solar source function, and aerosols. The water vapor column was retrieved with this method using spectra obtained with the rotating shadowband spectroradiometer (RSS) that was deployed during an intensive observation period near Barrow, Alaska, in March 1999. A line-by-line radiative transfer model was used to compute water vapor transmittance. The retrievals with this method are compared with retrievals obtained from three independent measurements with microwave radiometers. All four measurements show the same pattern of temporal variations. The RSS results agree most closely with retrievals obtained with the millimeter-wave imaging radiometer (MIR) at its 183 GHz +/- 7 double-side band channel. Their correlation over a period of 7 days when water vapor column varied between 0.75 mm and 3.6 mm (according to RSS) is 0.968 with MIR readings 0.12 mm higher on average.

Kiedron, P.↗

DOE OSTI2015

Validation of the National Solar Radiation Database (NSRDB) (2005-2012): Preprint

Publicly accessible, high-quality, long-term, satellite-based solar resource data is foundational and critical to solar technologies to quantify system output predictions and deploy solar energy technologies in grid-tied systems. Solar radiation models have been in development for more than three decades. For many years, the National Renewable Energy Laboratory (NREL) developed and/or updated such models through the National Solar Radiation Data Base (NSRDB). There are two widely used approaches to derive solar resource data from models: (a) an empirical approach that relates ground-based observations to satellite measurements and (b) a physics-based approach that considers the radiation received at the satellite and creates retrievals to estimate clouds and surface radiation. Although empirical methods have been traditionally used for computing surface radiation, the advent of faster computing has made operational physical models viable. The Global Solar Insolation Project (GSIP) is an operational physical model from the National Oceanic and Atmospheric Administration (NOAA) that computes global horizontal irradiance (GHI) using the visible and infrared channel measurements from the Geostationary Operational Environmental Satellites (GOES) system. GSIP uses a two-stage scheme that first retrieves cloud properties and then uses those properties in the Satellite Algorithm for Surface Radiation Budget (SASRAB) model to calculate surface radiation. NREL, the University of Wisconsin, and NOAA have recently collaborated to adapt GSIP to create a high temporal and spatial resolution data set. The product initially generates the cloud properties using the AVHRR Pathfinder Atmospheres-Extended (PATMOS-x) algorithms [3], whereas the GHI is calculated using SASRAB. Then NREL implements accurate and high-resolution input parameters such as aerosol optical depth (AOD) and precipitable water vapor (PWV) to compute direct normal irradiance (DNI) using the DISC model. The AOD and PWV, temperature, and pressure data are also combined with the MMAC model to simulate solar radiation under clear-sky conditions. The current NSRDB update is based on a 4-km x 4-km resolution at a 30-minute time interval, which has a higher temporal and spatial resolution. This paper demonstrates the evaluation of the data set using ground-measured data and detailed evaluation statistics. The result of the comparison shows a good correlation to the NSRDB data set. Further, an outline of the new version of the NSRDB and future plans for enhancement and improvement are provided.

14 SOLAR ENERGY↗

DOE OSTI2022

Quantifying Dynamic Signal Spread in Real-Time High-Energy X-ray Diffraction

Abstract Measured intensity in high-energy monochromatic X-ray diffraction (HEXD) experiments provides information regarding the microstructure of the crystalline material under study. The location of intensity on an areal detector is determined by the lattice spacing and orientation of crystals so that changes in the heterogeneity of these quantities are reflected in the spreading of diffraction peaks over time. High temporal resolution of such dynamics can now be experimentally observed using technologies such as the mixed-mode pixel array detector (MM-PAD) which facilitates in situ dynamic HEXD experiments to study plasticity and its underlying mechanisms. In this paper, we define and demonstrate a feature computed directly from such diffraction time series data quantifying signal spread in a manner that is correlated with plastic deformation of the sample. A distinguishing characteristic of the analysis is the capability to describe the evolution from the distinct diffraction peaks of an undeformed alloy sample through to the non-uniform Debye–Scherrer rings developed upon significant plastic deformation. We build on our previous work modeling data using an overcomplete dictionary by treating temporal measurements jointly to improve signal spread recovery. We demonstrate our approach in simulations and on experimental HEXD measurements captured using the MM-PAD. Our method for characterizing the temporal evolution of signal spread is shown to provide an informative means of data analysis that adds to the capabilities of existing methods. Our work draws on ideas from convolutional sparse coding and requires solving a coupled convex optimization problem based on the alternating direction method of multipliers.

Engineering↗

DOE OSTI2017

Revealing the reaction mechanisms of Li–O2 batteries using environmental transmission electron microscopy

The capacity, Coulombic efficiency, rate, and cyclability of a Li-O2 battery critically depend on the electrode reaction mechanism and the structure/morphology of the reaction product as well as their spatial and temporal evolution1-8, which are all further complicated by the choice of different electrolyte. For the case of aprotic cell, the discharge product, Li2O2, is formed through solution and surface mechanisms9,10, but little is known on the formation mechanism of the perplexing morphology of the reaction product11-15. For the case of Li-O2 battery using solid electrolyte, neither electrode reaction mechanism nor the nature of the reaction production is known. Herein, we reveal the full cycle reaction pathway for Li-O2 batteries and its correlation with the nature of the reaction product. Using an aberration-corrected environmental TEM under oxygen environment, we captured, for the first time, the morphology and phase evolution on the carbon nanotube (CNT) cathode of a working solid-state Li-O2 nano-battery16 and directly correlated these features with electrochemical reaction. We found that the oxygen reduction reaction on CNTs initially produces LiO2, which subsequently evolves to Li2O2 and O2 through disproportionation reaction. Surprisingly it is just the releasing of O2 that inflates the particles to a hollow structure with a Li2O outer surface layer and Li2O2 inner-shell, demonstrating that, in general, accommodation of the released O2 coupled with the Li+ ion diffusion and electron transport paths across both spatial and temporal scales critically governs the morphology of the discharging/charging product in Li-O2 system. We anticipate that the direct observation of Li-O2 reaction mechanisms and their correlation with the morphology of the reaction product set foundation for quantitative understanding/modeling of the electrochemical processes in the Li-O2 system, enabling rational design of both solid-state and aprotic Li-O2 batteries.

25 ENERGY STORAGE↗

NASA NTRS2016

Implications of L1 Observations for Slow Solar Wind Formation by Solar Reconnection

While the source of the fast solar wind is known to be coronal holes, the source of the slow solar wind has remained a mystery. Long time scale trends in the composition and charge states show strong correlations between solar wind velocity and plasma parameters, yet these correlations have proved ineffective in determining the slow wind source. We take advantage of new high time resolution (12 min) measurements of solar wind composition and charge state abundances at L1 and previously identified 90 min quasi periodic structures to probe the fundamental timescales of slow wind variability. The combination of new high temporal resolution composition measurements and the clearly identified boundaries of the periodic structures allows us to utilize these distinct solar wind parcels as tracers of slowwind origin and acceleration. We find that each 90 min (2000 Mm) parcel of slow wind has near-constant speed yet exhibits repeatable, systematic charge state and composition variations that span the entire range of statistically determined slow solar wind values. The classic composition-velocity correlations do not hold on short, approximately hour long, time scales. Furthermore, the data demonstrate that these structures were created by magnetic reconnection. Our results impose severe new constraints on slow solar wind origin and provide new, compelling evidence that the slow wind results from the sporadic release of closed field plasma via magnetic reconnection at the boundary between open and closed flux in the Sun's atmosphere.

Kepko, L.↗

DOE OSTI2021

Incorporating space and time into random forest models for analyzing geospatial patterns of drug-related crime incidents in a major U.S. metropolitan area

The opioid crisis has hit American cities hard, and research on spatial and temporal patterns of drug-related activities including detecting and predicting clusters of crime incidents involving particular types of drugs is useful for distinguishing hot zones where drugs are present that in turn can further provide a basis for assessing and providing related treatment services. In this study, we investigated spatiotemporal patterns of more than 52,000 reported incidents of drug-related crime at block group granularity in Chicago, IL between 2016 and 2019. We applied a space-time analysis framework and machine learning approaches to build a model using training data that identified whether certain locations and built environment and sociodemographic factors were correlated with drug-related crime incident patterns, and establish the top contributing factors that underlaid the trends. Space and time, together with multiple driving factors, were incorporated into a random forest model to analyze these changing patterns. We accommodated both spatial and temporal autocorrelation in the model learning process to assist with capturing the changes over time and tested the capabilities of the space-time random forest model by predicting drug-related activity hot zones. Overall, we focused particularly on crime incidents that involved heroin and synthetic drugs as these have been key drug types that have highly impacted cities during the opioid crisis in the U.S.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2026

Deployment Pathways for Long Duration Energy Storage

We apply a least-cost generation expansion model of the continental United States to assess how optimal investments in long-duration energy storage (LDES) technologies are impacted by changes in system generation portfolios and technology costs, assessing 369 capacity expansion scenarios in total. The expansion model considers 8,760 h of chronological operations for the entire target year, 2040. We find that low-cost LDES technologies can reduce generation investments and system costs. Specifically, once the costs for 24- and 100-h storage reach $38/kWh and $14/kWh, respectively, substantial deployments are observed. The distribution of storage investments across durations is strongly influenced by the system generation portfolio. We also demonstrate that a high-fidelity temporal representation is required to capture the value of LDES in generation expansion. Finally, we conduct a regression analysis of our capacity expansion results and find that LDES deployments are positively correlated with the combined wind and solar capacity share and negatively correlated with peaking and baseload shares.

Levin, Todd↗

DOE OSTI2019

In Operando Monitoring of Dynamic Recovery in 3D-Printed Thermoset Nanocomposites by XPCS

Extrusion-based additive manufacturing methods, such as direct-write of carbon fiber-reinforced epoxy inks, have become an attractive route toward development of structural composites in recent years, because of emerging techniques such as big area additive manufacturing. The development of improved materials for these methods has been a major focus area; however, an understanding of the effects of the printing process on the structural and dynamic recovery in printed materials remains largely unexplored. Here, our goal is to capture multiscale and temporal morphology and dynamics within thermosetting composite inks to determine the parameters during the printing process that influence the recovery of the printed material. Herein, we use X-ray photon correlation spectroscopy in small-angle scattering geometry to reveal both morphology and recovery dynamics of a nanoparticle (layered-silicate Cloisite 30B) in a thermoset epoxy resin (EPON 826) during the printing process in real time. Our results show that the dynamics of the layered silicate particles during recovery are anisotropic and slow down to behavior which is characteristic of aging in colloidal clay suspensions around tage ≈ 12 s. The dynamics and alignment of the particles during recovery were tempo-spatially mapped, and the recovery post printing was shown to be strongly influenced by the deposition onto the build plate in addition to the extrusion through the print head. Finally, our in operando results provide insight into the parameters that must be considered when optimizing materials and methods for precisely tailored local properties during 3D printing.

36 MATERIALS SCIENCE↗

DOE OSTI2015

Multivariate moment closure techniques for stochastic kinetic models

Stochastic effects dominate many chemical and biochemical processes. Their analysis, however, can be computationally prohibitively expensive and a range of approximation schemes have been proposed to lighten the computational burden. These, notably the increasingly popular linear noise approximation and the more general moment expansion methods, perform well for many dynamical regimes, especially linear systems. At higher levels of nonlinearity, it comes to an interplay between the nonlinearities and the stochastic dynamics, which is much harder to capture correctly by such approximations to the true stochastic processes. Moment-closure approaches promise to address this problem by capturing higher-order terms of the temporally evolving probability distribution. Here, we develop a set of multivariate moment-closures that allows us to describe the stochastic dynamics of nonlinear systems. Multivariate closure captures the way that correlations between different molecular species, induced by the reaction dynamics, interact with stochastic effects. We use multivariate Gaussian, gamma, and lognormal closure and illustrate their use in the context of two models that have proved challenging to the previous attempts at approximating stochastic dynamics: oscillations in p53 and Hes1. In addition, we consider a larger system, Erk-mediated mitogen-activated protein kinases signalling, where conventional stochastic simulation approaches incur unacceptably high computational costs.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

DOE OSTI2018

Spiky electric and magnetic field structures in flux rope experiments

Magnetic flux ropes are structures that are common in the corona of the sun and presumably all stars. They can be thought of as the building blocks of solar structures. They have been observed in Earth’s magnetotail and near Mars and Venus. When multiple flux ropes are present magnetic field line reconnection, which converts magnetic energy to other forms, can occur when they collide. The structure of multiple magnetic ropes, the interactions between multiple ropes, and their topological properties such as helicity and writhing have been studied theoretically and in laboratory experiments. In this paper, we report on spiky potential and magnetic fields associated with the ropes. We show that the potential structures are chaotic for a range of their temporal half-widths and the probability density function (PDF) of their widths resembles the statistical distribution of crumpled paper. The spatial structure of the magnetic spikes is revealed using a correlation counting method. Computer simulation suggests that the potential structures are the nonlinear end result of an instability involving relative drift between ions and electrons.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗