Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “Spreading models”

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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 217 records · Page 12
All sourcesNASA NTRSDOE OSTI
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↗
DOE OSTI2024

Sea Ice and Cloud Processes Mediating Compensation between Atmospheric and Oceanic Meridional Heat Transports across the CMIP6 Preindustrial Control Experiment

Abstract Bjerknes compensation (BJC) refers to the anticorrelation observed between atmospheric and oceanic heat transport (AHT/OHT) variability, particularly on decadal to longer time scales that may be important to the predictability of the climate system. This study investigates the spread in BJC across fully coupled simulations of phase 6 of the Coupled Model Intercomparison Project (CMIP6) and critical processes (particularly related to sea ice and clouds) that may contribute to that spread. BJC on decadal to longer time scales is confirmed across all the simulations evaluated, and it is strongest in the Northern Hemisphere (NH) between 60° and 70°N. At these latitudes, BJC appears to be primarily driven by the exchange of turbulent fluxes (sensible and latent) in the Greenland, Iceland, and Barents Seas. Metrics to break down how sea ice and clouds uniquely modify the radiative balance of the polar atmosphere during anomalous OHT events are presented. These metrics quantify the impacts of sea ice and clouds on surface and top of atmosphere (latent, sensible, longwave, and shortwave radiative) energy fluxes. Cloud responses tend to counter the clear sky impacts over the Marginal Ice Zone (MIZ). It is further shown that the degree of BJC present in a simulation at high latitudes is heavily influenced by the sensitivity of the sea ice to OHT, which is most influential over the MIZ. These results are qualitatively robust across models and explain the intermodel spread in NH BJC in the preindustrial control experiment.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Going Forward with the Nancy Grace Roman Space Telescope Transient Survey: Validation of Precision Forward-modeling Photometry for Undersampled Imaging

The Nancy Grace Roman Space Telescope (Roman) is an observatory for both wide-field observations and coronagraphy that is scheduled for launch in the mid-2020s. Part of the planned survey is a deep, cadenced field or fields that enable cosmological measurements with type Ia supernovae (SNe Ia). With a pixel scale of 0$_.^"$11, the Wide Field Instrument will be undersampled, presenting a difficulty for precisely subtracting the galaxy light underneath the SNe. We use simulated data to validate the ability of a forward-model code (such codes are frequently also called “scene-modeling” codes) to perform precision supernova photometry for the Roman SN survey. Our simulation includes over 760,000 image cutouts around SNe Ia or host galaxies (~10% of a full-scale survey). To have a realistic 2D distribution of underlying galaxy light, we use the VELA simulated high-resolution images of galaxies. We run each set of cutouts through our forward-modeling code which automatically measures time-dependent SN fluxes. Given our assumed inputs of a perfect model of the instrument point-spread functions and calibration, we find biases at the millimagnitude level from this method in four red filters (Y106, J129, H158, and F184), easily meeting the 0.5% Roman inter-filter calibration requirement for a cutting-edge measurement of cosmological parameters using SNe Ia. Simulated data in the bluer Z087 filter shows larger ~ 2–3 mmg biases, also meeting this requirement, but with more room for improvement. Our forward-model code has been released on Zenodo.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2022

Inferring the spread of COVID-19: the role of time-varying reporting rate in epidemiological modelling

The role of epidemiological models is crucial for informing public health officials during a public health emergency, such as the COVID-19 pandemic. However, traditional epidemiological models fail to capture the time-varying effects of mitigation strategies and do not account for under-reporting of active cases, thus introducing bias in the estimation of model parameters. To infer more accurate parameter estimates and to reduce the uncertainty of these estimates, we extend the SIR and SEIR epidemiological models with two time-varying parameters that capture the transmission rate and the rate at which active cases are reported to health officials. Using two real data sets of COVID-19 cases, we perform Bayesian inference via our SIR and SEIR models with time-varying transmission and reporting rates and via their standard counterparts with constant rates; our approach provides parameter estimates with more realistic interpretation, and 1-week ahead predictions with reduced uncertainty. Furthermore, we find consistent under-reporting in the number of active cases in the data that we consider, suggesting that the initial phase of the pandemic was more widespread than previously reported.

42 ENGINEERING↗
DOE OSTI2023

A Generalization of Threshold-Based and Probability-Based Models of Information Diffusion

Diffusion of information through complex networks is of interest in studies such as propagation prediction and influence maximization, both of which have applications in viral marketing and rumor controlling. There are a variety of information diffusion models, all of which simulate the adoption and spread of information over time. However, there is a lack of understanding of whether, despite their conceptual differences, these models represent the same underlying generative structures. For instance, if two different models utilize different conceptual mechanisms, but generate the same results, does the choice of model matter? A classification of diffusion of information models is developed based on the neighbor knowledge of the model infection requirement and the stochasticity of the model. This classification allows for the identification of models that fall into each respective category. The study involves the analysis of the following agent-based models on directed scale-free networks: (1) a linear absolute threshold model (LATM), (2) a linear fractional threshold model (LTFM), (3) the independent cascade model (ICM), (4) Bass-Rand-Rust model (BRRM) (5) a stochastic linear absolute threshold model (SLATM) (6) a stochastic fractional threshold model (SLFTM), and (7) Dodds–Watts model (DWM). Through the execution of simulations and analysis of the experimental results, the distinctive properties of each model are identified. Our analysis reveals that similarity in conceptual design does not imply similarity in behavior concerning speed, final state of nodes and edges, and sensitivity to parameters. Therefore, we highlight the importance of considering the unique behavioral characteristics of each model when selecting a suitable information diffusion model for a particular application.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

Use of an Alternative Conceptual Model of Vadose Zone Heterogeneity to Evaluate Past Tank Leaks and Other Unplanned Releases within a Tank Farm at the Hanford Site - 20061

Washington State Department of Ecology (Ecology) requested that U.S. Department of Energy (DOE)-Office of River Protection (ORP) consider an evaluation of effects of fine-grained thin sediment layers on transport with a separate alternative conceptual model in its evaluation of the potential impact of tank leaks and other unplanned releases within the tank farm at the Hanford Site. For this alternative model, Ecology recommended that the model be developed based on the general framework of fine-grained units identified by a stakeholder group in their interpretations of variability in moisture content data collected in the vicinity of Waste Management Area (WMA) C. In discussions with DOE-ORP, Ecology acknowledged that the underlying data and interpretations of the occurrence and lateral continuity of the fine-grained thin layers identified by a stakeholder group are uncertain. However, DOE-ORP agreed to Ecology's recommendation and has provided support for the requested evaluation that involved development of an alternative model based on the general framework of the unpublished report by the stakeholder. DOE-ORP considered this evaluation to be a hypothetical evaluation of vadose zone heterogeneities at WMA C. General observations from the range of simulation cases examined in the evaluation of the effects of hypothetical vadose zone heterogeneities at WMA C are as follows. - The movement of the center of mass of the simulated plumes was generally vertically downward below the source for all simulations, including those that incorporated the hypothetical fine-grained units. - All simulations that incorporated hypothetical heterogeneity produced additional plume spreading over what was produced in simulations using Equivalent Homogeneous Media (EHM) model(s)a. The spreading resulted in a broadening of the fringes of the plume, resulting in a wider region of low concentration, but lower peak concentrations associated with the center of mass of the plume. - Simulations that used the silty-sand hydraulic properties, suggested by Ecology for the hypothetical fine-grained units, generally produced similar spreading and slightly lower peak mass flux at the water table when compared to the EHM modeling results. a The EHM-based models used at WMA C do not explicitly include small-scale fine-grained heterogeneities used in the model advocated by Ecology. - Simulations that used the silty-sand hydraulic properties, suggested by Ecology for the hypothetical fine-grained units, generally produced less spreading and an earlier arrival of mass flux at the water table when compared to the use of another set of hydraulic properties from a silty-sand sample collected at a nearby disposal facility. - The EHM representation of the vadose zone generally produced higher peak mass flux and an earlier occurrence of peak fluxes at the water table compared to all analyses incorporating additional hypothetical heterogeneity. Results of this alternative model evaluation of past leaks provided some insight into the transport effects of vadose zone heterogeneities at WMA C that helped resolve Ecology's comments and issues related to the effects of vadose zone heterogeneities on past leaks and losses from the WMA C tank farms area. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2021

Impact of confined geometries on hopping and trapping of motile bacteria in porous media

We use a random walk particle-tracking (RWPT) approach to elucidate the impact of porous media confinement and cell-cell interactions on bacterial transport. The model employs stochastic alternating motility states consisting of hopping movement and trapping reorientation. The stochastic motility patterns are defined based on direct visualization of individual trajectory data. We validate our model against experimental data, at single-cell resolution, of bacterial E. coli motion in three-dimensional confined porous media. Results show that the model is able to efficiently simulate the spreading dynamics of motile bacteria as it captures the impact of cell-cell interaction and pore confinement, which marks the transition to a late-time subdiffusive regime. Furthermore, the model is able to qualitatively reproduce the observed directional persistence. Lastly, our RWPT model constitutes a meshless simple method which is easy to implement and does not invoke ad hoc assumptions but represents the basis for a multiscale approach to the study of bacterial dispersal in porous systems.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Dynamics of Gas Bubbles From a Submarine Hydrocarbon Seep Within the Hydrate Stability Zone

We validate a new model for mass transfer and bubble transport for natural seeps on the continental margins using an integrated observation of a seep at 883 m in the Northern Gulf of Mexico. In the model, mass transfer is assumed to transition from clean to dirty bubble mass transfer rates following a characteristic hydrate formation time that depends on the initial bubble surface area and the hydrate subcooling. We show that buoyancy-induced upwelling is negligible for the bubble stream. We initialize the model using precise data observed at the seafloor and validate the model results to optical and acoustic measurements within the bubble stream. The model accurately predicts the acoustic attenuation of the bubble flare by lateral spreading and bubble shrinkage throughout the water column. In conclusion, the model shows that up to 99.4% by mass of the released gases from this seep dissolves into the ocean within the hydrate stability zone.

03 NATURAL GAS↗
DOE OSTI2021

Identifiability and predictability of integer- and fractional-order epidemiological models using physics-informed neural networks

Here we analyze a plurality of epidemiological models through the lens of physics-informed neural networks (PINNs) that enable us to identify time-dependent parameters and data-driven fractional differential operators. In particular, we consider several variations of the classical susceptible-infectious-removed (SIR) model by introducing more compartments and fractional-order and time-delay models. We report the results for the spread of COVID-19 in New York City, Rhode Island and Michigan states and Italy, by simultaneously inferring the unknown parameters and the unobserved dynamics. For integer-order and time-delay models, we fit the available data by identifying time-dependent parameters, which are represented by neural networks. In contrast, for fractional differential models, we fit the data by determining different time-dependent derivative orders for each compartment, which we represent by neural networks. We investigate the structural and practical identifiability of these unknown functions for different datasets, and quantify the uncertainty associated with neural networks and with control measures in forecasting the pandemic.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2020

Validation of two-layer model for underexpanded hydrogen jets

Previous studies have shown that the two-layer model more accurately predicts hydrogen dispersion than the conventional notional nozzle models without significantly increasing the computational expense. However, the model was only validated for predicting the concentration distribution and has not been adequately validated for predicting the velocity distributions. In the present study, particle imaging velocimetry (PIV) was used to measure the velocity field of an underexpanded hydrogen jet released at 10 bar from a 1.5 mm diameter orifice. The two-layer model was the used to calculate the inlet conditions for a two-dimensional axisymmetric CFD model to simulate the hydrogen jet downstream of the Mach disk. The predicted velocity spreading and centerline decay rates agreed well with the PIV measurements. The predicted concentration distribution was consistent with data from previous planar Rayleigh scattering measurements used to verify the concentration distribution predictions in an earlier study. The jet spreading was also simulated using several widely used notional nozzle models combined with the integral plume model for comparison. These results show that the velocity and concentration distributions are both better predicted by the two-layer model than the notional nozzle models to complement previous studies verifying only the predicted concentration profiles. Thus, this study shows that the two-layer model can accurately predict the jet velocity distributions as well as the concentration distributions as verified earlier. Though more validation studies are needed to improve confidence in the model and increase the range of validity, the present work indicates that the two-layer model is a promising tool for fast, accurate predictions of the flow fields of underexpanded hydrogen jets.

08 HYDROGEN↗
DOE OSTI2022

The United States COVID-19 Forecast Hub dataset

Academic researchers, government agencies, industry groups, and individuals have produced forecasts at an unprecedented scale during the COVID-19 pandemic. To leverage these forecasts, the United States Centers for Disease Control and Prevention (CDC) partnered with an academic research lab at the University of Massachusetts Amherst to create the US COVID-19 Forecast Hub. Launched in April 2020, the Forecast Hub is a dataset with point and probabilistic forecasts of incident cases, incident hospitalizations, incident deaths, and cumulative deaths due to COVID-19 at county, state, and national, levels in the United States. Included forecasts represent a variety of modeling approaches, data sources, and assumptions regarding the spread of COVID-19. The goal of this dataset is to establish a standardized and comparable set of short-term forecasts from modeling teams. These data can be used to develop ensemble models, communicate forecasts to the public, create visualizations, compare models, and inform policies regarding COVID-19 mitigation. These open-source data are available via download from GitHub, through an online API, and through R packages.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2024

Relationship Between Tropical Cloud Feedback and Climatological Bias in Clouds

Abstract Global climate model (GCM) projections of future climate are uncertain largely due to a persistent spread in cloud feedback. This is despite efforts to reduce this model uncertainty through a variety of emergent constraints (ECs); with several studies suggesting an important role for present‐day biases in clouds. Here, we use three generations of GCMs to assess the value of climatological cloud metrics for constraining uncertainty in cloud feedback. We find that shortwave cloud radiative properties across the Southern Hemisphere extratropics are most robustly correlated with tropical cloud feedback (TCF). Using this relationship in conjunction with observations, we produce an EC that yields a TCF value of 0.52 ± 0.34 W/m 2 /K, which equates to a 34% reduction in uncertainty. Thus, we show that climatological cloud properties can be used to reduce uncertainty in how clouds will respond to future warming.

54 ENVIRONMENTAL SCIENCES↗