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At least 451 records · Page 25
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

GeoAI for Public Health

Infectious disease spread within the human population can be conceptualized as a complex system composed of individuals who interact and transmit viruses through spatio-temporal processes that manifest across and between scales. The complexity of this system ultimately means that the spread of infectious diseases is difficult to understand, predict, and respond to effectively. Research interest in GeoAI for public health has been fueled by the increased availability of rich data sources such as human mobility data, OpenStreetMap data, contact tracing data, symptomatic online surveys, retail and commerce data, genomics data, and more. This data availability has resulted in a wide variety of data-driven solutions for infectious disease spread prediction which show potential in enhancing our forecasting capabilities. This chapter (1) motivates the need for AI-based solutions in public health by showing the heterogeneity of human behavior related to health, (2) provides a brief survey of current state-of-the-art solutions using AI for infectious disease spread prediction, (3) describes a use-case of using large-scale human mobility data to inform AI models for the prediction of infectious disease spread in a city, and (4) provides future research directions and ideas.

Zufle, Andreas↗
NASA NTRS2012

Prevention of Over-Pressurization During Combustion in a Sealed Chamber

The combustion of flammable material in a sealed chamber invariably leads to an initial pressure rise in the volume. The pressure rise is due to the increase in the total number of gaseous moles (condensed fuel plus chamber oxygen combining to form gaseous carbon dioxide and water vapor) and, most importantly, the temperature rise of the gas in the chamber. Though the rise in temperature and pressure would reduce with time after flame extinguishment due to the absorption of heat by the walls and contents of the sealed spacecraft, the initial pressure rise from a fire, if large enough, could lead to a vehicle over-pressure and the release of gas through the pressure relief valve. This paper presents a simple lumped-parameter model of the pressure rise in a sealed chamber resulting from the heat release during combustion. The transient model considers the increase in gaseous moles due to combustion, and heat transfer to the chamber walls by convection and radiation and to the fuel-sample holder by conduction, as a function of the burning rate of the material. The results of the model are compared to the pressure rise in an experimental chamber during flame spread tests as well as to the pressure falloff after flame extinguishment. The experiments involve flame spread over thin solid fuel samples. Estimates of the heat release rate profiles for input to the model come from the assumed stoichiometric burning of the fuel along with the observed flame spread behavior. The sensitivity of the model to predict maximum chamber pressure is determined with respect to the uncertainties in input parameters. Model predictions are also presented for the pressure profile anticipated in the Fire Safety-1 experiment, a material flammability and fire safety experiment proposed for the European Space Agency (ESA) Automated Transfer Vehicle (ATV). Computations are done for a range of scenarios including various initial pressures and sample sizes. Based on these results, various mitigation approaches are suggested to prevent vehicle over-pressurization and help guide the definition of the space experiment.

Gokoglu, Suleyman A.↗
DOE OSTI2023

Biological carbon pump estimate based on multidecadal hydrographic data

The transfer of photosynthetically produced organic carbon from surface to mesopelagic waters draws carbon dioxide from the atmosphere. However, current observation-based estimates disagree on the strength of this biological carbon pump (BCP). Earth system models (ESMs) also exhibit a large spread of BCP estimates, indicating limited representations of the known carbon export pathways. Here we use several decades of hydrographic observations to produce a top-down estimate of the strength of the BCP with an inverse biogeochemical model that implicitly accounts for all known export pathways. Our estimate of total organic carbon (TOC) export at 73.4 m (model euphotic zone depth) is 15.00 ± 1.12 Pg C year –1 , with only two-thirds reaching 100 m depth owing to rapid remineralization of organic matter in the upper water column. Partitioned by sequestration time below the euphotic zone, τ, the globally integrated organic carbon production rate with τ > 3 months is 11.09 ± 1.02 Pg C year –1 , dropping to 8.25 ± 0.30 Pg C year –1 for τ > 1 year, with 81% contributed by the non-advective-diffusive vertical flux owing to sinking particles and vertically migrating zooplankton. Nevertheless, export of organic carbon by mixing and other fluid transport of dissolved matter and suspended particles remains regionally important for meeting the respiratory carbon demand. Here, the temperature dependence of the sequestration efficiency inferred from our inversion suggests that future global warming may intensify the recycling of organic matter in the upper ocean, potentially weakening the BCP.

58 GEOSCIENCES↗
NASA NTRS2001

Three-Dimensional Upward Flame Spreading in Partial-Gravity Buoyant Flows

Reduced-gravity environments have been used to establish low-speed, purely forced flows for both opposed- and concurrent-flow flame spread studies. Altenkirch's group obtained spacebased experimental results and developed unsteady, two-dimensional numerical simulations of opposed-flow flame spread including gas-phase radiation, primarily away from the flammability limit for thin fuels, but including observations of thick fuel quenching in quiescent environments. T'ien's group contributed some early flame spreading results for thin fuels both in opposed flow and concurrent flow regimes, with more focus on near-limit conditions. T'ien's group also developed two- and three-dimensional numerical simulations of concurrent-flow flame spread incorporating gas-phase radiative models, including predictions of a radiatively-induced quenching limit reached in very low-speed air flows. Radiative quenching has been subsequently observed in other studies of combustion in very low-speed flows including other flame spread investigations, droplet combustion and homogeneous diffusion flames, and is the subject of several contemporary studies reported in this workshop. Using NASA aircraft flying partial-gravity "parabolic" trajectories, flame spreading in purely buoyant, opposed-flow (downward burning) has been studied. These results indicated increases in flame spread rates and enhanced flammability (lower limiting atmospheric oxygen content) as gravity levels were reduced from normal Earth gravity, and were consistent with earlier data obtained by Altenkirch using a centrifuge. In this work, experimental results and a three-dimensional numerical simulation of upward flame spreading in variable partial-gravity environments were obtained including some effects of reduced pressure and variable sample width. The simulation provides physical insight for interpreting the experimental results and shows the intrinsic 3-D nature of buoyant, upward flame spreading. This study is intended to link the evolving understanding of flame spreading in purely-forced flows to the purely-buoyant flow environment, particularly in the concurrent flow regime; provide additional insight into the existence of steady flame spread in concurrent flows; and stimulate direct comparisons between opposed- and concurrent-flow flame spread. Additionally, this effort is intended to provide direct practical understanding applicable to fire protection planning for the habitable facilities in partial gravity environments of anticipated Lunar and Martian explorations.

Sacksteder, Kurt R.↗
DOE OSTI2025

Opportunities in multiscale modeling of mosquito-borne flaviviruses

Mosquito-borne flaviviruses, such as Zika, dengue, West Nile, and yellow fever virus, represent a growing public health concern due to their widespread distribution and the severe diseases they cause. These viruses are difficult to control as climate change and urbanization help mosquitoes expand into new areas, increasing the risk of outbreaks. Mathematical models play a key role in understanding their spread, providing insights at every level—from how the virus multiplies inside cells to how it circulates through entire populations. This review examines various approaches used in modeling arboviruses, including microscale models that focus on cellular and molecular dynamics, mesoscale models that address within-host processes, and macroscale models that capture population-level transmission. We briefly summarize the methodology used for models at each scale, which primarily consists of sets of differential equations with parameters that represent physical rates of change for different subprocesses. We particularly highlight how temperature affects virus transmission, which is key to understanding the impact of climate change. We also show how multiscale models can connect viral replication, immune response, and the spread of infection at a larger scale. This is essential for developing better vaccines and treatments, evaluating disease control measures, predicting the impact of climate change, and improving public health responses to outbreaks.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2021

Machine Learning Emulation of Spatial Deposition from a Multi-Physics Ensemble of Weather and Atmospheric Transport Models

In the event of an accidental or intentional hazardous material release in the atmosphere, researchers often run physics-based atmospheric transport and dispersion models to predict the extent and variation of the contaminant spread. These predictions are imperfect due to propagated uncertainty from atmospheric model physics (or parameterizations) and weather data initial conditions. Ensembles of simulations can be used to estimate uncertainty, but running large ensembles is often very time consuming and resource intensive, even using large supercomputers. In this paper, we present a machine-learning-based method which can be used to quickly emulate spatial deposition patterns from a multi-physics ensemble of dispersion simulations. We use a hybrid linear and logistic regression method that can predict deposition in more than 100,000 grid cells with as few as fifty training examples. Logistic regression provides probabilistic predictions of the presence or absence of hazardous materials, while linear regression predicts the quantity of hazardous materials. The coefficients of the linear regressions also open avenues of exploration regarding interpretability—the presented model can be used to find which physics schemes are most important over different spatial areas. A single regression prediction is on the order of 10,000 times faster than running a weather and dispersion simulation. However, considering the number of weather and dispersion simulations needed to train the regressions, the speed-up achieved when considering the whole ensemble is about 24 times. Ultimately, this work will allow atmospheric researchers to produce potential contamination scenarios with uncertainty estimates faster than previously possible, aiding public servants and first responders.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS1996

Parafoveal Target Detectability Reversal Predicted by Local Luminance and Contrast Gain Control

This project is part of a program to develop image discrimination models for the prediction of the detectability of objects in a range of backgrounds. We wanted to see if the models could predict parafoveal object detection as well as they predict detection in foveal vision. We also wanted to make our simplified models more general by local computation of luminance and contrast gain control. A signal image (0.78 x 0.17 deg) was made by subtracting a simulated airport runway scene background image (2.7 deg square) from the same scene containing an obstructing aircraft. Signal visibility contrast thresholds were measured in a fully crossed factorial design with three factors: eccentricity (0 deg or 4 deg), background (uniform or runway scene background), and fixed-pattern white noise contrast (0%, 5%, or 10%). Three experienced observers responded to three repetitions of 60 2IFC trials in each condition and thresholds were estimated by maximum likelihood probit analysis. In the fovea the average detection contrast threshold was 4 dB lower for the runway background than for the uniform background, but in the parafovea, the average threshold was 6 dB higher for the runway background than for the uniform background. This interaction was similar across the different noise levels and for all three observers. A likely reason for the runway background giving a lower threshold in the fovea is the low luminance near the signal in that scene. In our model, the local luminance computation is controlled by a spatial spread parameter. When this parameter and a corresponding parameter for the spatial spread of contrast gain were increased for the parafoveal predictions, the model predicts the interaction of background with eccentricity.

Ahumada, Albert J., Jr.↗
NASA NTRS2016

Mapping Past, Present, and Future Climatic Suitability for Invasive Aedes Aegypti and Aedes Albopictus in the United States: A Process-Based Modeling Approach Using CMIP5 Downscaled Climate Scenarios

The ongoing spread of the mosquitoes, Aedes aegypti and Aedes albopictus, in the continental United States leaves new areas at risk for local transmission of dengue, chikungunya, and Zika viruses. All three viruses have caused major disease outbreaks in the Americas with infected travelers returning regularly to the U.S. The expanding range of these mosquitoes raises questions about whether recent spread has been enabled by climate change or other anthropogenic influences. In this analysis, we used downscaled climate scenarios from the NASA Earth Exchange Global Daily Downscaled Projections (NEX GDDP) dataset to model Ae. aegypti and Ae. albopictus population growth rates across the United States. We used a stage-structured matrix population model to understand past and present climatic suitability for these vectors, and to project future suitability under CMIP5 climate change scenarios. Our results indicate that much of the southern U.S. is suitable for both Ae. aegypti and Ae. albopictus year-round. In addition, a large proportion of the U.S. is seasonally suitable for mosquito population growth, creating the potential for periodic incursions into new areas. Changes in climatic suitability in recent decades for Ae. aegypti and Ae. albopictus have occurred already in many regions of the U.S., and model projections of future climate suggest that climate change will continue to reshape the range of Ae. aegypti and Ae. albopictus in the U.S., and potentially the risk of the viruses they transmit.

process-based modeling↗
NASA NTRS2000

Shock Formation of Slow Magnetosonic Waves in Coronal Plumes

We investigate the height of shock formation in coronal 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 (SOHO)/ Ultraviolet Coronograph Spectrometer (UVCS), Extreme Ultraviolet Imaging Telescope (EIT), Michelson Doppler Imager (MDI), and Large Angle Spectrometric Coronagraph (LASCO). Our models show that shock formation occurs at relatively low coronal heights, typically within 1.2 RsuN, depending on the model parameters. The shock formation is calculated using the well-established wave breaking criterion 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 such waves are probably not the main energy supply mechanism.

Cuntz, Manfred↗
NASA NTRS2001

Momentum and Energy Assessments with NASA and Other Model and Data Assimilation Systems

Aspects of the angular momentum cycle, energetics, and related diagnostics from a number of models, including some from the Goddard Laboratory for Atmospheres, and from the Atmospheric Model Intercomparison Project (AMIP) are examined. Torques that dynamically excite changes in angular momentum, including strong torques at mountains were studied. The measure of how atmospheric mass from a strong weather signal can notably change the angular momentum is studied. For AMIP, there is a spread in the angular momentum amongst models, while the GLA model does reasonably well compared to the other models in the diagnostics examined, namely angular momentum and water vapor. Trends and interannual variability in water vapor over a lengthy period was examined. The role of the diabatic heating components, especially latent heating, in the energy cycle and the terms converting available potential energy to kinetic energy, among other parts of the energy cycle, are studied. Modes of climate of the atmosphere, especially the Arctic and North Atlantic Oscillations, are analyzed as well.

Salstein, David↗
NASA NTRS1999

Modeling the QBO and SAO Driven by Gravity Waves

Hines' Doppler spread parameterization (DSP) for small scale gravity waves (GW) is applied in a global scale numerical spectral model (NSM) to describe the semi-annual and quasi-biennial oscillations (SAO and QBO) as well as the long term interannual variations that are driven by wave mean flow interactions. This model has been successful in simulating the salient features observed near the equator at altitudes above 20 km, including the QBO extension into the upper mesosphere inferred from UARS measurements. The model has now been extended to describe also the mean zonal and meridional circulations of the upper troposphere and lower stratosphere that affect the equatorial QBO and its global scale extension. This is accomplished in part through tuning of the GW parameterization, and preliminary results lead to the following conclusions: (1) To reproduce the upwelling at equatorial latitudes associated with the Brewer/Dobson circulation that in part is modulated in the model by the vertical component of the Coriolis force, the eddy diffusivity in the lower stratosphere had to be enhanced and the related GW spectrum modified to bring it in closer agreement with the form recommended for the DSP. (2) To compensate for the required increase in the diffusivity, the observed QBO requires a larger GW source that is closer to the middle of the range recommended for the DSP. (3) Through global scale momentum redistribution, the above developments are conducive to extending the QBO and SAO oscillations to higher latitudes. Multi-year interannual oscillations are generated through wave filtering by the solar driven annual oscillation in the zonal circulation. (4) In a 3D version of the model, wave momentum is absorbed and dissipated by tides and planetary waves. Thus, a somewhat larger GW source is required to generate realistic amplitudes for the QBO and SAO.

Mayr, H. G.↗
DOE OSTI2025

A Large-Eddy Simulation Study in WRF on Wind over Broadband Waves of Different Directions and Spreading Widths

We use the Weather Research and Forecasting (WRF) Model coupled with moving waves to conduct large-eddy simulations (LESs) of wind over broadband waves with different propagating directions and spreading widths. Our results show that wind-opposing waves can double the form drag, and the wave propagating direction affects the mean wind velocity, velocity variances, and pressure stress. On the other hand, waves with wider spread tend to reduce the form drag in the streamwise direction. Results further indicate that the wave direction can impact the bulk drag coefficient by as much as 25%, while the wave directional spreading width can change it by 5%. Based on our wave-phase-resolved simulations, we demonstrate that the parameterization of sea surface roughness is significantly influenced by the direction of wave propagation relative to the wind, a factor that the commonly used Charnock relation does not account for.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Afterglow Polarization from Off-axis Gamma-Ray Burst Jets

As we further our studies on gamma-ray bursts (GRBs), via both theoretical models and observational tools, more and more options begin to open for exploration of its physical properties. As GRBs are transient events primarily dominated by synchrotron radiation, it is expected that the synchrotron photons emitted by GRBs should present some degree of polarization throughout the evolution of the burst. Whereas observing this polarization can still be challenging due to the constraints on observational tools, especially for short GRBs, it is paramount that the groundwork is laid for the day we have abundant data. In this work, we present a polarization model linked with an off-axis spreading top-hat jet synchrotron scenario in a stratified environment with a density profile n(r) ∝ r ⁻k . We present this model's expected temporal polarization evolution for a realistic set of afterglow parameters constrained within the values observed in the GRB literature for four degrees of stratification k = 0, 1, 1.5, and 2 and two magnetic field configurations with high extreme anisotropy. We apply this model and predict polarization from a set of GRBs exhibiting off-axis afterglow emission. In particular, for GRB 170817A, we use the available polarimetric upper limits to rule out the possibility of an extremely anisotropic configuration for the magnetic field.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2022

MSR Salt Spill Accident Testing Using Eutectic NaCl-UCl 3

Assessing the potential consequences of identified accident scenarios is an essential part of the licensing process for a new nuclear reactor and is achieved by using accident progression models. A likely accident scenario that will be evaluated by developers of molten salt reactors (MSRs) is a spill of radionuclide-bearing fuel salt onto the reactor containment floor (i.e., a salt spill accident). Models to determine the consequences of a molten salt spill accident need to be parameterized and validated using experimental data, but little experimental effort has been dedicated to fill these data gaps to date. Specifically, data is needed to enable model development for individual processes (e.g., spreading, heat transfer, corrosion, radionuclide vaporization, and aerosol generation) that quantifies the sensitivities towards the initial conditions of the spill, the ambient environment, and the features of the containment. In addition, integrated experiments that simulate salt spill accidents will need to be conducted to provide insight into coupled processes and data for model validation, but these experiments will require the use of proven methods to quantify the processes under evaluation.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2023

HeatMap

SAND2021-14230 O HeatMap generates geospatial wildfire fuel models by applying machine learning algorithms to satellite imagery and weather station data. The code then leverages wildfire behavior software FlamMap to determine fire spread. It also uses PSLF software to model grid impacts and display data analytics for grid components that have been impacted by wildfire. Sandia National Laboratories is a multimission laboratory managed and operated by National Technology & Engineering Solutions of Sandia, LLC, a wholly owned subsidiary of Honeywell International Inc., for the U.S. Department of Energy’s National Nuclear Security Administration under contract DE-NA0003525.

Danford, Forest↗
DOE OSTI2022

PetroFit: A Python Package for Computing Petrosian Radii and Fitting Galaxy Light Profiles

PetroFit is an open-source Python package based on Astropy and Photutils that can calculate Petrosian profiles and fit galaxy images. It offers end-to-end tools for making accurate photometric measurements, estimating morphological properties, and fitting 2D models to galaxy images. Petrosian metric radii can be used for model parameter estimation and aperture photometry to provide accurate total fluxes. Correction tools are provided for improving Petrosian radii estimates affected by galaxy morphology. PetroFit also provides tools for sampling Astropy-based models (including custom profiles and multicomponent models) onto image grids and enables point-spread function convolution to account for the effects of seeing. These capabilities provide a robust means of modeling and fitting galaxy light profiles. We have made the PetroFit package publicly available on GitHub (PetroFit/petrofit) and PyPi (pip install petrofit).

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2021

Forecasting influenza activity using machine-learned mobility map

Human mobility is a primary driver of infectious disease spread. However, existing data is limited in availability, coverage, granularity, and timeliness. Data-driven forecasts of disease dynamics are crucial for decision-making by health officials and private citizens alike. In this work, we focus on a machine-learned anonymized mobility map (hereon referred to as AMM) aggregated over hundreds of millions of smartphones and evaluate its utility in forecasting epidemics. We factor AMM into a metapopulation model to retrospectively forecast influenza in the USA and Australia. We show that the AMM model performs on-par with those based on commuter surveys, which are sparsely available and expensive. We also compare it with gravity and radiation based models of mobility, and find that the radiation model’s performance is quite similar to AMM and commuter flows. Additionally, we demonstrate our model’s ability to predict disease spread even across state boundaries. Our work contributes towards developing timely infectious disease forecasting at a global scale using human mobility datasets expanding their applications in the area of infectious disease epidemiology.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2022

Alternative SIAR models for infectious diseases and applications in the study of non-compliance

In this paper, we use modified versions of the SIAR model for epidemics to propose two ways of understanding and quantifying the effect of non-compliance to non-pharmaceutical intervention measures on the spread of an infectious disease. The SIAR model distinguishes between symptomatic infected (I) and asymptomatic infected (A) populations. One modification, which is simpler, assumes a known proportion of the population does not comply with government mandates such as quarantining and social-distancing. In a more sophisticated approach, the modified model treats non-compliant behavior as a social contagion. We theoretically explore different scenarios such as the occurrence of multiple waves of infections. Local and asymptotic analyses for both models are also provided.

Mathematics↗