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At least 145 records · Page 8
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Physics of turbulence spreading and explicit nonlocality

In this paper, we systematically derive a model for turbulence spreading from the basic kinetic equation. The model contains explicit nonlocal nonlinear diffusion and nonlocal growth. When the nonlocality scale parameter δ b (banana width) vanishes, this model reduces to the usual turbulence spreading model. We elucidate the mechanisms of nonlinear saturation and nonlocal growth. Results show that nonlocal effects, especially the nonlocal growth, thicken the turbulence spreading front and increase the speed of front propagation. More turbulence intensity penetrates the stable region when δ b increases. The penetration depth Δ p is proportional to $ \delta_b/L_T $, therefore the fraction of turbulence in the unstable region scales as 1 – δ b* . Lastly, the transport scales the same way.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2025

Cloud Feedback Uncertainty in the Equatorial Pacific Across CMIP6 Models

Cloud feedback is the largest uncertainty in estimating Equilibrium Climate Sensitivity. In this study we focus on the equatorial Pacific, where CMIP6 model cloud feedback spread is notably large. Cloud radiative effects in this region are relevant for the global climate. Our findings show that models predict a consistent shift towards the ascent regime in response to El Nino-like sea surface warming. Models diverge in terms of the radiative impact due to differences in cloud characteristics in ascent and subsidence regimes. Using the observed relationship between circulation regime and cloud radiative effect, we find a reduction in the regional mean cloud feedback estimate from 0.77 to 0.22 W m -2 K -1 , though this does not substantially lessen the model spread in total feedback. Pathways to reduce this spread include: improving confidence in the regional ocean warming pattern, and using observations and models to understand cloud type and circulation interactions.

CMIP6↗
NASA NTRS1977

A comparison between the current models of mid-latitude spread F and data from the Arecibo Observatory

The current models of mid-latitude F sub s are studied. The assumptions and derivations of the Reid model, the Scannapieco model, and the Perkins model are presented in detail. Incoherent-scatter data of the density profiles and velocity profiles were obtained in order that the models could be evaluated on the basis of experimental data. Initial studies indicated that the Perkins model was most representative of the data from Arecibo, so a detailed comparison of the predictions of the Perkins model and the data was made. Two of four nights studied are nights with F sub s. The Perkins model is derived in a frame of reference moving with the velocity of the neutral wind; the model is transformed to the rest frame to facilitate comparison with data. Several data handling techniques are introduced. In particular, an integration interval that remains constant in length, but follows the vertical motion of the peak of the F layer is used to obtain the field integrated quantities of the Perkins model.

Imel, G.↗
NASA NTRS2006

Models of Wake-Vortex Spreading Mechanisms and Their Estimated Uncertainties

One of the primary constraints on the capacity of the nation's air transportation system is the landing capacity at its busiest airports. Many airports with nearly-simultaneous operations on closely-spaced parallel runways (i.e., as close as 750 ft (246m)) suffer a severe decrease in runway acceptance rate when weather conditions do not allow full utilization. The objective of a research program at NASA Ames Research Center is to develop the technologies needed for traffic management in the airport environment so that operations now allowed on closely-spaced parallel runways under Visual Meteorological Conditions can also be carried out under Instrument Meteorological Conditions. As part of this overall research objective, the study reported here has developed improved models for the various aerodynamic mechanisms that spread and transport wake vortices. The purpose of the study is to continue the development of relationships that increase the accuracy of estimates for the along-trail separation distances available before the vortex wake of a leading aircraft intrudes into the airspace of a following aircraft. Details of the models used and their uncertainties are presented in the appendices to the paper. Suggestions are made as to the theoretical and experimental research needed to increase the accuracy of and confidence level in the models presented and instrumentation required or more precise estimates of the motion and spread of vortex wakes. The improved wake models indicate that, if the following aircraft is upwind of the leading aircraft, the vortex wakes of the leading aircraft will not intrude into the airspace of the following aircraft for about 7s (based on pessimistic assumptions) for most atmospheric conditions. The wake-spreading models also indicate that longer time intervals before wake intrusion are available when atmospheric turbulence levels are mild or moderate. However, if the estimates for those time intervals are to be reliable, further study is necessary to develop the instrumentation and procedures needed to accurately define when the more benign atmospheric conditions exist.

Rossow, Vernon J.↗
NASA NTRS1984

A model for equatorial explosive spread F

Explosive spread F was first described by Woodman and LaHoz (1976). In such events the 50 MHz radar signal rises at Jicamarca to 10-20 dB above the noise level within a few milliseconds but then disappears within 100 ms or less. It has been suggested that electric fields from thunderstorms might drive a plasma instability which could cause these echoes, and recent observations (Woodman and Kudeki, 1984) provide convincing evidence for this triggering. Here, the instability mechanism is discussed and it is shown that transient thunderstorm electric fields comparable to those recently observed by rockets in the midlatitude ionosphere could excite a rapidly growing two-stream plasma instability that is driven by the ExB drift of the F region electrons in the short period (less than the ion gyroperiod) before the ions reach the same velocity.

Kelley, M. C.↗
DOE OSTI2023

Recurrent Convolutional Deep Neural Networks for Modeling Time-Resolved Wildfire Spread Behavior

The increasing incidence and severity of wildfires underscores the necessity of accurately predicting their behavior. While high-fidelity models derived from first principles offer physical accuracy, they are too computationally expensive for use in real-time fire response. Low-fidelity models sacrifice some physical accuracy and generalizability via the integration of empirical measurements, but enable real-time simulations for operational use in fire response. Machine learning techniques have demonstrated the ability to bridge these objectives by learning first-principles physics while achieving computational speedups. While deep learning approaches have demonstrated the ability to predict wildfire propagation over large time periods, time-resolved fire-spread predictions are needed for active fire management. Here, in this work, we evaluate the ability of deep learning approaches in accurately modeling the time-resolved dynamics of wildfires. We use an autoregressive process in which a convolutional recurrent deep learning model makes predictions that propagate a wildfire over 15 min increments. We apply the model to four simulated datasets of increasing complexity, containing both field fires with homogeneous fuel distribution as well as real-world topologies sampled from the California region of the United States. We show that even after 100 autoregressive predictions representing more than 24 h of simulated fire spread, the resulting models generate stable and realistic propagation dynamics, achieving a Jaccard score between 0.89 and 0.94 when predicting the resulting fire scar. The inference time of the deep learning models are examined and compared, and directions for future work are discussed.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

Modeling and Experimental Demonstration of Flux Spreading in Light Trapping Planar-Cavity Solar Enclosed Particle Receivers

This study experimentally validates and numerically models the flux-spreading effect in a light-trapping planar-cavity solar receiver) for particle-based concentrating solar power systems. The receiver's shallow cavity with vertical planar walls redistributes concentrated solar flux, reducing peak intensity and achieving uniform heat flux. On-sun tests at National Renewable Energy Laboratory's High-Flux Solar Furnace under flux up to 1500 kW/m2 measured cavity wall temperatures, which were compared with Monte Carlo ray-tracing (SolTrace) and computational numerical simulations. Three angular absorptance models were evaluated: constant absorptance, a Pyromark-based directional model, and a Fresnel-based Cr2O3 model. The Fresnel-derived model showed the best agreement with experiments, achieving high correlation (PC > 0.85), structural similarity (SSIM > 0.98), and signal-to-noise ratios (PSNR > 40 dB), with temperature prediction errors of 1-11%. Results confirm that flux spreading mitigates local overheating and validate the integrated modeling approach, supporting the solar receiver scalability for high-efficiency, high-temperature concentrating solar power applications.

14 SOLAR ENERGY↗
NASA NTRS1998

Models of Plumes: Their Flow, Their Geometric Spreading, and Their Mixing with Interplume Flow

There are two types of plume flow models: (1) 1D models using ad hoc spreading functions, f(r); (2) MagnetoHydroDynamics (MHD) models. 1D models can be multifluid, time dependent, and incorporate very general descriptions of the energetics. They confirm empirical results that plume flow is slow relative to requirements for high speed wind. But, no published 1 D model incorporates the rapid local spreading at the base (fl(r)) which has an important effect on mass flux. The one published MHD model is isothermal, but confirms that if b=8*pi*p/absolute value(B)2<<l then the field is nearly potential below -70,000 km. Building on the MHD result, we apply a two scale approximation to calculate fl(r). We also compute the global spreading (fg(r)) out to 5.0 RSUN imposed by coronal hole geometry. Global MHD models provide a potent method of calculating fg(r). Unambiguous plume signatures have not yet been found in the solar wind. This is probably due to strong mixing of plume and interplume flows near the Sun. We describe a physical source for strong mixing due to the observed flows being unstable to shear instabilities that lead to rapid disruption.

Suess, Steven T.↗
DOE OSTI2023

A Pacific Tropical Decadal Variability Challenge for Climate Models

Understanding and forecasting Tropical Pacific Decadal-scale Variability (TPDV) strongly rely on climate model simulations. Using a Linear Inverse Modeling (LIM) diagnostic approach, we reveal Coupled Model Intercomparison Project Phase 6 models have significant challenges in reproducing the spatial structure and dominant mechanisms of TPDV. Specifically, while the models' ensemble mean pattern of TPDV resembles that of observations, the spread across models is very large and most models show significant differences from observations. In observations, removing the coupling between extratropics and tropics reduces TPDV by ~60%–70%, and removing the tropical thermocline variability makes the central tropical Pacific a key center of action for TPDV and El Niño Southern Oscillation variability. These characteristics are only confirmed in a subset of models. Differences between observations and simulations are outside the range of natural internal TPDV noise and pose important questions regarding our ability to model the impacts of natural internal low-frequency variability superimposed on long-term climate change.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

On how structures convey non-diffusive turbulence spreading

Abstract We report on comprehensive experimental studies of turbulence spreading in edge plasmas. These studies demonstrate the relation of turbulence spreading and entrainment to intermittent convective density fluctuation events or bursts (i.e. blobs and holes). The non-diffusive character of turbulence spreading is thus elucidated. The turbulence spreading velocity (or mean jet velocity) manifests a linear correlation with the skewness of density fluctuations, and increases with the auto-correlation time of density fluctuations. Turbulence spreading by positive density fluctuations is outward, while spreading by negative density fluctuations is inward. The degree of symmetry breaking between outward propagating blobs and inward propagating holes increases with the amplitude of density fluctuations. Thus, blob-hole asymmetry emerges as crucial to turbulence spreading. These results highlight the important role of intermittent convective events in conveying the spreading of turbulence, and constitute a fundamental challenge to existing diffusive models of spreading.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
NASA NTRS2002

Insights into Corona Formation through Statistical Analyses

Statistical analysis of an expanded database of coronae on Venus indicates that the populations of Type 1 (with fracture annuli) and 2 (without fracture annuli) corona diameters are statistically indistinguishable, and therefore we have no basis for assuming different formation mechanisms. Analysis of the topography and diameters of coronae shows that coronae that are depressions, rimmed depressions, and domes tend to be significantly smaller than those that are plateaus, rimmed plateaus, or domes with surrounding rims. This is consistent with the model of Smrekar and Stofan and inconsistent with predictions of the spreading drop model of Koch and Manga. The diameter range for domes, the initial stage of corona formation, provides a broad constraint on the buoyancy of corona-forming plumes. Coronae are only slightly more likely to be topographically raised than depressions, with Type 1 coronae most frequently occurring as rimmed depressions and Type 2 coronae most frequently occuring with flat interiors and raised rims. Most Type 1 coronae are located along chasmata systems or fracture belts, while Type 2 coronas are found predominantly as isolated features in the plains. Coronae at hotspot rises tend to be significantly larger than coronae in other settings, consistent with a hotter upper mantle at hotspot rises and their active state.

Glaze, L. S.↗
NASA NTRS2002

Insights into Corona Formation Through Statistical Analyses

Statistical analysis of an expanded database of coronae on Venus indicates that the populations of Type 1 (with fracture annuli) and 2 (without fracture annuli) corona diameters are statistically indistinguishable, and therefore we have no basis for assuming different formation mechanisms. Analysis of the topography and diameters of coronae shows that coronae that are depressions, rimmed depressions, and domes tend to be significantly smaller than those that are plateaus, rimmed plateaus, or domes with surrounding rims. This is consistent with the model of Smrekar and Stofan and inconsistent with predictions of the spreading drop model of Koch and Munga. The diameter range for domes, the initial stage of corona formation, provides a broad constraint on the buoyancy of corona-forming plumes. Coronae are only slightly more likely to be topographically raised than depressions, with Type 1 coronae most frequently occurring as rimmed depressions and Type 2 coronae most frequently occurring with flat interiors and raised rims. Most Type 1 coronae are located along chasmata systems or fracture belts, while Type 2 coronae are found predominantly as isolated features in the plains. Coronae at hot spot rises tend to be significantly lager than coronae in other settings, consistent with a hotter upper mantle at hot spot rises and their active state.

Glaze, L. S.↗
NASA NTRS2019

Investigating Sources of Variability and Error in Simulations of Carbon Dioxide in an Urban Region

Greenhouse gas (GHG) emissions estimation methods that use atmospheric trace gas observations, including inverse modeling techniques, perform better when carbon dioxide (CO2) fluxes are more accurately transported and dispersed in the atmosphere by a numerical model. In urban areas, transport and dispersion is particularly difficult to simulate using current mesoscale meteorological models due, in part, to added complexity from surface heterogeneity and fine spatial/temporal scales. It is generally assumed that the errors in GHG estimation methods in urban areas are dominated by errors in transport and dispersion. Other significant errors include, but are not limited to, those from assumed emissions magnitude and spatial distribution. To assess the predictability of simulated trace gas mole fractions in urban observing systems using a numerical weather prediction model, we employ an Eulerian model that combines traditional meteorological variables with multiple passive tracers of atmospheric CO2 from anthropogenic inventories and a biospheric model. The predictability of the Eulerian model is assessed by comparing simulated atmospheric CO2 mole fractions to observations from four in situ tower sites (three urban and one rural) in the Washington DC/Baltimore, MD area for February 2016. Four different gridded fossil fuel emissions inventories along with a biospheric flux model are used to create an ensemble of simulated atmospheric CO2 observations within the model. These ensembles help to evaluate whether the modeled observations are impacted more by the underlying emissions or transport. The spread of modeled observations using the four emission fields indicates the model's ability to distinguish between the different inventories under various meteorological conditions. Overall, the Eulerian model performs well; simulated and observed average CO2 mole fractions agree within 1% when averaged at the three urban sites across the month. However, there can be differences greater than 10% at any given hour, which are attributed to complex meteorological conditions rather than differences in the inventories themselves. On average, the mean absolute error of the simulated compared to actual observations is generally twice as large as the standard deviation of the modeled mole fractions across the four emission inventories. This result supports the assumption, in urban domains, that the predicted mole fraction error relative to observations is dominated by errors in model meteorology rather than errors in the underlying fluxes in winter months. As such, minimizing errors associated with atmospheric transport and dispersion may help improve the performance of GHG estimation models more so than improving flux priors in the winter months. We also find that the errors associated with atmospheric transport in urban domains are not restricted to certain times of day. This suggests that atmospheric inversions should use CO2 observations that have been filtered using meteorological observations rather than assuming that meteorological modeling is most accurate at certain times of day (such as using only mid-afternoon observations).

CO2↗
DOE OSTI2021

Bayesian inference of heterogeneous epidemic models: Application to COVID-19 spread accounting for long-term care facilities

Here we propose a high dimensional Bayesian inference framework for learning heterogeneous dynamics of a COVID-19 model, with a specific application to the dynamics and severity of COVID-19 inside and outside long-term care (LTC) facilities. We develop a heterogeneous compartmental model that accounts for the heterogeneity of the time-varying spread and severity of COVID-19 inside and outside LTC facilities, which is characterized by time-dependent stochastic processes and time-independent parameters in ~ 1500 dimensions after discretization. To infer these parameters, we use reported data on the number of confirmed, hospitalized, and deceased cases with suitable post-processing in both a deterministic inversion approach with appropriate regularization as a first step, followed by Bayesian inversion with proper prior distributions. To address the curse of dimensionality and the ill-posedness of the high-dimensional inference problem, we propose use of a dimension-independent projected Stein variational gradient descent method, and demonstrate the intrinsic low-dimensionality of the inverse problem. We present inference results with quantified uncertainties for both New Jersey and Texas, which experienced different epidemic phases and patterns. Moreover, we also present forecasting and validation results based on the empirical posterior samples of our inference for the future trajectory of COVID-19.

42 ENGINEERING↗
DOE OSTI2023

Ensemble Spread Behavior in Coupled Climate Models: Insights From the Energy Exascale Earth System Model Version 1 Large Ensemble

AbstractAssessing uncertainty in future climate projections requires understanding both internal climate variability and external forcing. For this reason, single‐model initial condition large ensembles (SMILEs) run with Earth System Models (ESMs) have recently become popular. Here we present a new 20‐member SMILE with the Energy Exascale Earth System Model version 1 (E3SMv1‐LE), which uses a “macro” initialization strategy choosing coupled atmosphere/ocean states based on inter‐basin contrasts in ocean heat content (OHC). The E3SMv1‐LE simulates tropical climate variability well, albeit with a muted warming trend over the twentieth century due to overly strong aerosol forcing. The E3SMv1‐LE's initial climate spread is comparable to other (larger) SMILEs, suggesting that maximizing inter‐basin ocean heat contrasts may be an efficient method of generating ensemble spread. We also compare different ensemble spread across multiple SMILEs, using surface air temperature and OHC. The Community Earth system Model version 1, the only ensemble which utilizes a “micro” initialization approach perturbing only atmospheric initial conditions, yields lower spread in the first ∼30 years. The E3SMv1‐LE exhibits a relatively large spread, with some evidence for anthropogenic forcing influencing spread in the late twentieth century. However, systematic effects of differing “macro” initialization strategies are difficult to detect, possibly resulting from differing model physics or responses to external forcing. Notably, the method of standardizing results affects ensemble spread: control simulations for most models have either large background trends or multi‐centennial variability in OHC. This spurious disequlibrium behavior is a substantial roadblock to understanding both internal climate variability and its response to forcing.

Stevenson, Samantha↗
NASA NTRS1987

Unusual characteristics of electromagnetic waves excited by cometary newborn ions with large perpendicular energies

The characteristics of electromagnetic waves excited by cometary newborn ions with large perpendicular energies are examined using a model of solar wind permeated by dilute drifting ring distributions of electrons and oxygen ions with finite thermal spreads. The model has parameters compatible with the ICE observations at the Giacobini-Zinner comet. It is shown that cometary newborn ions with large perpendicular energies can excite a wave mode with rest frame frequencies in the order of the heavy ion cyclotron frequency, Omega(i), and unusual propagation characteristics at small obliquity angles. For parallel propagation, the mode is left-hand circularly polarized, might be unstable in a frequency range containing Omega(i), and moves in the direction of the newborn ion drift along the static magnetic field.

Brinca, A. L.↗
DOE OSTI2020

Contrasting Regional Carbon Cycle Responses to Seasonal Climate Anomalies Across the East-West Divide of Temperate North America

Across temperate North America, interannual variability (IAV) in gross primary production (GPP) and net ecosystem exchange (NEE) and their relationship with environmental drivers are poorly understood. Here, we examine IAV in GPP and NEE and their relationship to environmental drivers using two state-of-the-science flux products: NEE constrained by surface and space-based atmospheric CO 2 measurements over 2010-2015 and satellite up-scaled GPP from FluxSat over 2001-2017. We show that the arid western half of temperate North America provides a larger contribution to IAV in GPP (104% of east) and NEE (127% of east) than the eastern half, in spite of smaller magnitude of annual mean GPP and NEE. This occurs because anomalies in western ecosystems are temporally coherent across the growing season leading to an amplification of GPP and NEE. In contrast, IAV in GPP and NEE in eastern ecosystems is dominated by seasonal compensation effects, associated with opposite responses to temperature anomalies in spring and summer. Terrestrial biosphere models in the MsTMIP ensemble generally capture these differences between eastern and western temperate North America, although there is considerable spread between models.

54 ENVIRONMENTAL SCIENCES↗