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At least 415 records · Page 23
All sourcesNASA NTRSDOE OSTI
NASA NTRS2001

Solar Cycle Variations and Equatorial Oscillations: Modeling Study

Solar cycle activity effects (SCAE) in the lower and middle atmosphere, reported in several studies, are difficult to explain on the basis of the small changes in solar radiation that accompany the 11-year cycle, It is therefore natural to speculate that dynamical processes may come into play to produce a leverage. Such a leverage may be provided by the Quasi-Biennial Oscillation (QBO) in the zonal circulation of the stratosphere, which has been linked to solar activity variations. Driven primarily by wave mean flow interaction, the QBO period and its amplitude are variable but are also strongly influenced by the seasonal cycle in the solar radiation. This influence extends to low altitudes referred to as "downward control". Relatively small changes in solar radiative forcing can produce small changes in the period and phase of the QBO, but this in turn can produce measurable differences in the wind field. Thus, the QBO may be an amplifier of solar activity variations and a natural conduit of these variations to lower altitudes. To test this hypothesis, we conducted experiments with a 2D (two-dimensional) version of our Numerical Spectral Model that incorporates Hines' Doppler Spread Parameterization for small-scale gravity waves (GW). Solar cycle radiance variations (SCRV) are accounted for by changing the radiative heating rate on a logarithmic scale from 0.1 % at the surface to 1 % at 50 km to 10% at 100 km. With and without SCRV, but with the same GW flux, we then conduct numerical experiments to evaluate the magnitude of the SCAE in the zonal circulation. The numerical results indicate that, under certain conditions, the SCAE is significant and can extend to lower altitudes where the SCRV is inconsequential. At 20-km the differences in the modeled wind velocities are as large as 5 m/s. For a modeled QBO period of 30 months, we find that the seasonal cycle in the solar forcing (through the Semi-annual Oscillation (SAO)) acts as a strong pacemaker to lockup the phase and period of the QBO. The SCAE then shows up primarily as a distinct but relatively weak amplitude modulation. But with the QBO period between 30 and 34 (or less than 30, presumably) months, the seasonal phase lock is weak. Solar flux radiance variations in the seasonal cycle then cause variations in the QBO period and phase that amplify the SCAE to produce relatively large variations in the wind field. These variations also extend to mid latitudes.

Mayr, H. G.↗
NASA NTRS1980

Modelling of equatorial phase and amplitude scintillations from OGO-6 and AE irregularity data

The technique of modelling equatorial phase and amplitude scintillations from in-situ measurements of irregularity amplitude, ambient ionization density and irregularity power spectrum is outlined. The in-situ measurements do not directly provide information on irregularity layer thickness for which radar backscatter observations are utilized. The equatorial scintillation models developed from OGO-6 and AE-C in-situ measurements during the two solstices are presented. Considerable differences in the longitude variation of scintillations are predicted during the two solstices. The model estimates are in good agreement with the available ground based phase and amplitude measurements. Problems associated with the use of bottomside spread-F data for transionospheric propagation modelling at VHF/UHF are also discussed.

Basu, S.↗
DOE OSTI2022

Comprehensive compartmental model and calibration algorithm for the study of clinical implications of the population-level spread of COVID-19: a study protocol

The complex dynamics of the coronavirus disease 2019 (COVID-19) pandemic has made obtaining reliable long-term forecasts of the disease progression difficult. Simple mechanistic models with deterministic parameters are useful for short-term predictions but have ultimately been unsuccessful in extrapolating the trajectory of the pandemic because of unmodelled dynamics and the unrealistic level of certainty that is assumed in the predictions. We propose a 22-compartment epidemiological model that includes compartments not previously considered concurrently, to account for the effects of vaccination, asymptomatic individuals, inadequate access to hospital care, post-acute COVID-19 and recovery with long-term health complications. Additionally, new connections between compartments introduce new dynamics to the system and provide a framework to study the sensitivity of model outputs to several concurrent effects, including temporary immunity, vaccination rate and vaccine effectiveness. Subject to data availability for a given region, we discuss a means by which population demographics (age, comorbidity, socioeconomic status, sex and geographical location) and clinically relevant information (different variants, different vaccines) can be incorporated within the 22-compartment framework. Considering a probabilistic interpretation of the parameters allows the model's predictions to reflect the current state of uncertainty about the model parameters and model states. We propose the use of a sparse Bayesian learning algorithm for parameter calibration and model selection. This methodology considers a combination of prescribed parameter prior distributions for parameters that are known to be essential to the modelled dynamics and automatic relevance determination priors for parameters whose relevance is questionable. This is useful as it helps prevent overfitting the available epidemiological data when calibrating the parameters of the proposed model. Population-level administrative health data will serve as partial observations of the model states.

59 BASIC BIOLOGICAL SCIENCES↗
NASA NTRS1997

Diurnal Forcing of Planetary Atmospheres

Much progress has been made on calculations of the Martian seasonal water cycle using the Mars Climate Model developed for this purpose. Two papers, documenting the model and the water transport results obtained with it have been published in the Journal of Geophysical Research - Planets. An additional paper describing results related to the evolution of the seasonal water cycle as a result of orbital changes was published in Advances in Space Research. Since that time, further studies have concentrated on the consequences of the soil adsorption required to match the observed water cycle and its relation to the stability of ground ice and other potential water reservoirs. Earth-related studies have concentrated on incorporating an efficient and realistic microphysical model into the Ames Stratospheric General Circulation Model used to simulate the spread of the ML Pinatubo and other volcanic clouds in the stratosphere. In addition, visualizations of the simulations are being incorporated into a video describing the UARS mission. A paper describing the new stratospheric aerosol microphysics package (and its consequences for volcanic cloud evolution) will be submitted in the near future. The paper will discuss the relative importance of condensation and coagulation to early particle growth and the separation of the cloud by sedimentation of the larger particles. A more general paper which highlights the observation that particle number densities did not increase dramatically after the ML Pinatubo eruption is planned. Simulations of atmospheric transport will be extended to include studies of terrestrial tropospheric tracers using the Fifth-Generation Penn State/NCAR Mesoscale Model.

Houben, Howard C.↗
NASA NTRSDate not supplied

TPSAS-NF1676L-29139-DND

The Arctic has been warming at a rate outpacing globally-averaged warming by 2-3 times, a phenomenon known as "Arctic Amplification" (AA). AA is evident in observations of Arctic surface temperature over the last century as well as in model predictions. The accuracy of model predictions is crucial to determine our response to climate change, as Arctic changes have consequences for the atmospheric circulation, sea level rise, and the carbon cycle. Unfortunately, models show a wide range of possible futures and intermodel spread in projections of Arctic temperature is larger than for any other region: a 2°C global temperature increase- the limit set by the Paris Climate Agreement- would lead to an Arctic temperature change between 3-7°C according to Coupled Model Intercomparison 5 (CMIP5) models. This intermodel spread represents a key deficiency in the understanding of Arctic feedback processes and how changes in various physical parameters (e.g. sea ice cover, clouds, heat transport and surface fluxes) affect the temperature response. Using a process-oriented decomposition of the simulate Arctic surface energy budget, we attribute the largest contribution to the CMIP5 intermodel spead in projected Arctic warming to the Barents-Kara Sea region. Our results reveal that the increasing seasonal fluxing of ocean heat content is a significant part of the intermodel spread in AA and relates strongly to change in surface turbulent fluxes. Our results indicate that if we are to significantly reduce the intermodel spread in projected AA we must focus on understanding the behavior and physical processes that drive surface energy flows in the B-K sea region.

Patrick Taylor↗
NASA NTRS1978

Shear flow beneath oceanic plates - Local nonsimilarity boundary layers for olivine rheology

The principle of local similarity, which has been used to model the two-dimensional boundary layers in the oceanic upper mantle, permits calculation of the temperature, velocity, and stress fields with essentially analytic techniques. Finite difference numerical methods are hard pressed to resolve the detail required by the large variation of viscosity between the lithosphere and the asthenosphere. In this paper the local similarity approximation has been justified by quantitatively evaluating the effect of nonsimilarity due to viscous heating, nonlinear temperature- and pressure-dependent rheology, buoyancy, adiabatic cooling, etc. Nonsimilar effects produce only small modifications of the locally similar boundary layers; important geophysical observables such as surface heat flux and ocean floor topography are given to better than 10 percent by the locally similar solution. A posteriori evaluations of the terms neglected in the boundary layer simplification of the complete equations have been conducted on the locally similar temperature and velocity profiles close to the spreading ridge. The boundary layer models are valid to depths of 100 km at 3 m.y. and 10 km at 0.3 m.y.

Yuen, D. A.↗
NASA NTRSDate not supplied

Better Constraining Supercooled Clouds Could Reduce Projected Warming Spread

The increase of climate sensitivity to rising greenhouse gas concentrations in the coupled model intercomparison project phase 6 (CMIP6) Earth system models (ESMs) compared to CMIP5 ESMs is primarily attributed to a larger extratropical cloud response to climate change, referred to as cloud feedback. The ratio of supercooled liquid cloud water relative to all cloud water, termed liquid phase ratio (LPR), which has also notably increased in many recent ESMs, is thought to be a primary driver of the extratropical cloud feedback increase. Unlike the preponderance of previous studies that compare native model LPR directly with observations, here we evaluate LPR over three ESM generations against Cloud–Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) using an instrument simulator approach, which mimics instrument limitations and uses consistent cloud definitions and resolutions. We find that current coupled model intercomparison project (CMIP) ESMs collectively simulate greater LPR than previous CMIP generations and overestimate LPR compared to observations, contrary to previous findings, likely driven by past inconsistent comparisons of ESM outputs with CALIPSO observations. We further show that greater LPR in ESM present-day climate is unexpectedly correlated with a smaller extratropical cloud feedback, attributable to a decrease of cloud amount exceeding the increase of cloud optical depth. Finally, our results suggest that improving constraints on model LPR using our evaluation framework would likely reduce the spread in CMIP6 climate sensitivities, owing to its effect on extratropical cloud feedback from supercooled clouds.

greenhouse gas concentrations↗
NASA NTRS2022

Premature Deaths in Africa Due to Particulate Matter Under High and Low Warming Scenarios

Sustainable development and climate change mitigation can provide enormous public health benefits via improved air quality, especially in polluted areas. We use the latest state-of-the-art composition-climate model simulations to contrast human exposure to fine particulate matter in Africa under a “baseline” scenario with high material consumption, population growth, and warming to that projected under a sustainability scenario with lower consumption, population growth, and warming. Evaluating the mortality impacts of these exposures, we find that under the low warming scenario annual premature deaths due to PM2.5 are reduced by roughly 515,000 by 2050 relative to the high warming scenario (100,000, 175,000, 55,000, 140,000, and 45,000 in Northern, West, Central, East, and Southern Africa, respectively). This reduction rises to ∼800,000 by the 2090s, though by that time much of the difference is attributable to the projected differences in population. By contrast, during the first half of the century benefits are driven predominantly by emissions changes. Depending on the region, we find large intermodel spreads of ∼25%–50% in projected future exposures owing to different physics across the ensemble of 6 global models. The spread of projected deaths attributable to exposure to fine particulate matter, including uncertainty in the exposure-response function, are reduced in every region to ∼20%–35% by the non-linear exposure-response function. Differences between the scenarios have an even narrower spread of ∼5%–25% and are highly statistically significant in all regions for all models. These results provide valuable information for policy-makers to consider when working toward climate change mitigation and sustainable development goals.

PM2.5↗
NASA NTRS2001

Shock Formation of Slow Magnetosonic Waves in Coronal Plumes

We investigate the height of shock formation in coroner plumes for slow magnetosonic waves. The models take into account plume geometric spreading, heat conduction and radiative damping. The wave parameters as well as the spreading functions of the plumes and the base magnetic field strengths are given by empirical constraints mostly from Solar and Heliospheric Observatory/Ultraviolet Coronagraph Spectrometer (SOHO/UVCS). Our models show that shock formation occurs at low coronal heights, i.e., within 1.3 solar radius, depending on the model parameters. The shock formation is calculated using the well-established wave breaking condition given by the intersection of C+ characteristics in the space-time plane. Our models show that shock heating by slow magnetosonic waves is expected to be relevant at most heights in solar coronal plumes, although slow magnetosonic waves are most likely not a solely operating energy supply mechanism.

Cuntz, Manfred↗
NASA NTRS1995

Solid surface combustion experiment flame spread in a quiescent, microgravity environment implications of spread rate and flame structure

A unique environment in which flame spreading, a phenomenon of fundamental, scientific interest, has importance to fire safety is that of spacecraft in which the gravitational acceleration is low compared with that of the Earth, i.e., microgravity. Experiments aboard eight Space Shuttle missions between October 1990 and February 1995 were conducted using the Solid Surface Combustion Experiment (SSCE) payload apparatus in an effort to determine the mechanisms of gas-phase flame spread over solid fuel surfaces in the absence of any buoyancy induced or externally imposed oxidizer flow. The overall SSCE effort began in December of 1984. The SSCE apparatus consists of a sealed container, approximately 0.039 cu m, that is filled with a specified O2/N2 mixture at a prescribed pressure. Five of the experiments used a thin cellulosic fuel, ashless filter paper, 3 cm wide x 10 cm long, 0.00825 cm half-thickness, ignited in five different ambient conditions. Three of the experiments, the most recent, used thick polymethylmethacrylate (PMMA) samples 0.635 cm wide x 2 cm long, 0.32 cm half-thickness. Three experiments, STS 41, 40 and 43, were designed to evaluate the effect of ambient pressure on flame spread over the thin cellulosic fuel while flights STS 50 and 47 were at the same pressure as two of the earlier flights but at a lower oxygen concentration in order to evaluate the effect of ambient oxygen level on the flame spread process at microgravity. For the PMMA flights, two experiments, STS 54 and 63, were at the same pressure but different oxygen concentrations while STS 64 was at the same oxygen concentration as STS 63 but at a higher pressure. Two orthogonal views of the experiments were recorded on 16 mm cine-cameras operating at 24 frames/s. In addition to filmed images of the side view of the flames and surface view of the burning samples, solid- and gas-phase temperatures were recorded using thermocouples. The experiment is battery powered and follows an automated sequence upon activation by the Shuttle Crew. In this study we separate the SSCE data into two groups according to the fuel type: (1) thin cellulose; and (2) thick PMMA. The experimental spread rates are compared with prediction from a number of models in an effort to uncover the important physics that characterize microgravity flame spread. Both steady and unsteady solutions are employed to explore the flame evolution, especially for thick fuels. Finally, the flame structure in downward spread is compared with the microgravity flame structure and modeling results to delineate the difference between the two configurations and the influence of normal gravity.

Bundy, Matthew↗
NASA NTRS2001

Modeling and Prediction of Wildfire Hazard in Southern California, Integration of Models with Imaging Spectrometry

Large urban wildfires throughout southern California have caused billions of dollars of damage and significant loss of life over the last few decades. Rapid urban growth along the wildland interface, high fuel loads and a potential increase in the frequency of large fires due to climatic change suggest that the problem will worsen in the future. Improved fire spread prediction and reduced uncertainty in assessing fire hazard would be significant, both economically and socially. Current problems in the modeling of fire spread include the role of plant community differences, spatial heterogeneity in fuels and spatio-temporal changes in fuels. In this research, we evaluated the potential of Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) and Airborne Synthetic Aperture Radar (AIRSAR) data for providing improved maps of wildfire fuel properties. Analysis concentrated in two areas of Southern California, the Santa Monica Mountains and Santa Barbara Front Range. Wildfire fuel information can be divided into four basic categories: fuel type, fuel load (live green and woody biomass), fuel moisture and fuel condition (live vs senesced fuels). To map fuel type, AVIRIS data were used to map vegetation species using Multiple Endmember Spectral Mixture Analysis (MESMA) and Binary Decision Trees. Green live biomass and canopy moisture were mapped using AVIRIS through analysis of the 980 nm liquid water absorption feature and compared to alternate measures of moisture and field measurements. Woody biomass was mapped using L and P band cross polarimetric data acquired in 1998 and 1999. Fuel condition was mapped using spectral mixture analysis to map green vegetation (green leaves), nonphotosynthetic vegetation (NPV; stems, wood and litter), shade and soil. Summaries describing the potential of hyperspectral and SAR data for fuel mapping are provided by Roberts et al. and Dennison et al. To utilize remotely sensed data to assess fire hazard, fuel-type maps were translated into standard fuel models accessible to the FARSITE fire spread simulator. The FARSITE model and BEHAVE are considered industry standards for fire behavior analysis. Anderson level fuels map, generated using a binary decision tree classifier are available for multiple dates in the Santa Monica Mountains and at least one date for Santa Barbara. Fuel maps that will fill in the areas between Santa Barbara and the Santa Monica Mountains study sites are in progress, as part of a NASA Regional Earth Science Application Center, the Southern California Wildfire Hazard Center. Species-level maps, were supplied to fire managing agencies (Los Angeles County Fire, California Department of Forestry). Research results were published extensively in the refereed and non-refereed literature. Educational outreach included funding of several graduate students, undergraduate intern training and an article featured in the California Alliance for Minorities Program (CAMP) Quarterly Journal.

Roberts, Dar A.↗
NASA NTRS2002

Stagnation Flow in Streamer Boundaries

It is possible for flow tubes adjacent to closed magnetic field lines on the boundaries of streamers to have spreading factors that change rapidly according to height. Mathematical models in this thin layer are prone to uncertainties. In this paper we use an analytical model of magnetically closed and adjacent open regions to calculate the spreading factor close to the closed field lines and near the flow on the open field lines. We recovered the shape of the closed field region, or helmet, and show the evolution of the helmet under increasing temperature. We also show why and when stagnation flow can occur in the area of the cusp at the top of the helmet.

Suess, S. T.↗
DOE OSTI2023

A spatially regularized detector for emergent/re-emergent disease outbreaks

Early detection of outbreaks caused by emergent pathogens, using epidemiological surveillance data i.e., daily case counts, is difficult. This is because the data tend to be noisy during the early epoch of the outbreak. In contrast, the spread-rate of the disease tends to be well-behaved, as it depends only on the mixing patterns of the population and the characteristics of the pathogen, neither of which behave erratically in space-time. In this report, we explore whether the spread-rate can be used for epidemiological surveillance, conditional on case count data. Estimating the spread-rate from case count data allows us to exploit exogenous information, e.g., incubation period distributions etc., which can considerably smooth out any erratic temporal behavior. Further, epidemiological dynamics are spatially correlated, and if case counts are available for multiple areal units e.g., counties, these correlations could potentially be used to suppress noise in the early epoch data. These exogenous information and structure are not exploited by conventional syndromic surveillance detectors to extract a well-behaved latent variable for monitoring purposes. The technical challenge lies in the estimation of the spread-rate field jointly over a collection of areal units; further, the spread-rate varies over time. We develop a method based on mean-field variational inference to approximately estimate the spread-rate field, using a Gaussian Random Field Model for spatial regularization. The method is tested on the estimation of spread-rate in the thirty-three counties of New Mexico and detect the arrival of the Fall 2020 COVID-19 wave in September 2020. We find that the method is scalable, but underestimates the uncertainty in the estimated spread-rate field. We detect the arrival of the Fall 2020 wave a week ahead of conventional syndromic surveillance algorithms, but our simplistic detection algorithm, based on simple anomaly detection, suffers from a high false positive rate, similar to conventional detectors.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2019

Computational Investigation of Melt Pool Process Dynamics and Pore Formation in Laser Powder Bed Fusion

In the laser powder bed fusion additive manufacturing process, the presence of porosity may result in cracks and significantly affects the part performance. A comprehensive understanding of the melt pool process dynamics and porosity evolution can help to improve build quality. In this study, a novel multi-physics computational fluid dynamics (CFD) model has been applied to investigate the fluid dynamics in melt pools and resultant pore defects. To accurately capture the melting and solidification process, major process physics, such as the surface tension, evaporation as well as laser multi-reflection, have been considered in the model. A discrete element method is utilized to model the generation of powder spreading upon build plate by additional numerical simulations. Multiple single track experiments have been performed to obtain the melt pool shape and cross-sectional dimension information. The predicted melt pool dimensions were found to have a reasonable agreement with experimental measurements, e.g., the errors are in the range of 1.3 to 10.6% for melt pool width, while they are between 1.4 and 15.9% for melt depth. Pores are captured by both CFD simulation and x-ray computed tomography measurement for the case with a laser power of 350 W and laser speed of 100 mm/s. The formation of keyholes maybe related to the melt pool front wall angle, and it is found that the front wall angle increases with the increase in laser line energy density. In addition, a larger laser power or smaller scanning speed can help to generate keyhole-induced pores; they also contribute to produce larger sized pores.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Benchmark calculations of pure neutron matter with realistic nucleon-nucleon interactions

Here, we report benchmark calculations of the energy per particle of pure neutron matter as a function of the baryon density using three independent many-body methods: Brueckner–Bethe–Goldstone, Fermi hypernetted chain/single-operator chain, and auxiliary-field diffusion Monte Carlo. Significant technical improvements are implemented in the latter two methods. The calculations are made for two distinct families of realistic coordinate-space nucleon-nucleon potentials fit to scattering data, including the standard Argonne v 18 interaction and two of its simplified versions, and four of the new Norfolk Δ -full chiral effective field theory potentials. Primarily because of the advancements in the auxiliary-field diffusion Monte Carlo, we observe good agreement among the three many-body techniques up to nuclear saturation density—the maximum difference in the energy per particle is within 1.5 MeV for all the potentials we consider. At higher densities, the divergences become more important, and are mainly driven by the Fermi hypernetted chain/single-operator calculations. We also study the connection between nucleon-nucleon scattering data and the energy per particle of pure neutron matter. Our results suggest that fitting to higher-energy nucleon-nucleon scattering helps reduce the spread of energies among the models.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2024

Guided Mode Evolution and Ionization Injection in Meter-Scale Multi-GeV Laser Wakefield Accelerators

Here, we show that multi-GeV laser wakefield electron accelerators in meter-scale, low density hydrodynamic plasma waveguides operate in a new nonlinear propagation regime dominated by sustained beating of lowest order modes of the ponderomotively modified channel; this occurs whether or not the injected pulse is linearly matched to the guide. For a continuously doped gas jet, this emergent mode beating effect leads to axially modulated enhancement of ionization injection and a multi-GeV energy spectrum of multiple quasimonoenergetic peaks; the same process in a locally doped jet produces single multi-GeV peaks with <10% energy spread. A three-stage model of drive laser pulse evolution and ionization injection characterizes the beating effect and explains our experimental results.

43 PARTICLE ACCELERATORS↗
DOE OSTI2020

Sandia's Research in Support of COVID-19 Pandemic Response: Computing and Information Sciences

This report summarizes the goals and findings of eight research projects conducted under the Computing and Information Sciences (CIS) Research Foundation and related to the COVID- 19 pandemic. The projects were all formulated in response to Sandia's call for proposals for rapid-response research with the potential to have a positive impact on the global health emergency. Six of the projects in the CIS portfolio focused on modeling various facets of disease spread, resource requirements, testing programs, and economic impact. The two remaining projects examined the use of web-crawlers and text analytics to allow rapid identification of articles relevant to specific technical questions, and categorization of the reliability of content. The portfolio has collectively produced methods and findings that are being applied by a range of state, regional, and national entities to support enhanced understanding and prediction of the pandemic's spread and its impacts.

59 BASIC BIOLOGICAL SCIENCES↗