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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.

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At least 325 records · Page 18
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Evaluation of Monin‐Obukhov Similarity Theory Wind Profiles in Convective Storm Environments and Cold Pools at the ARM Southern Great Plains Atmospheric Observatory

Monin-Obukhov similarity theory (MOST) is widely used in numerical weather prediction to model surface fluxes of momentum, heat, and water vapor. However, MOST is based on assumptions of steady state and horizontally homogeneous turbulence that can lead to prediction errors in and around convective storms. To understand the nature of these errors, we used wind and eddy covariance flux measurements from the Atmospheric Radiation Measurement Southern Great Plains Atmospheric Observatory to evaluate MOST wind profiles in fair-weather and convective storm environments, specifically those of mesoscale convective systems and ordinary thunderstorms. MOST wind profiles agreed well with observations in fair-weather cases, while in convective storm cases the theory systematically overestimated shear in cold pools after gust front passage. Surface layer stability was found to be important in assessing MOST within convective storm environments. The overestimation of wind shear in cold pools suggests the role of non-local fluxes in transferring momentum downward. We discuss reasons for differences and agreement with past studies, and conclude with recommendations to improve prediction of surface winds and fluxes in convective storm simulations.

58 GEOSCIENCES↗
DOE OSTI2023

Validation of turbulence intensity as simulated by the Weather Research and Forecasting model off the US northeast coast

We report turbulence intensity (TI) is often used to quantify the strength of turbulence in wind energy applications and serves as the basis of standards in wind turbine design. Thus, accurately characterizing the spatiotemporal variability in TI should lead to improved predictions of power production. Nevertheless, turbulence measurements over the ocean are far less prevalent than over land due to challenges in instrumental deployment, maintenance, and operation. Atmospheric models such as mesoscale (weather prediction) and large-eddy simulation (LES) models are commonly used in the wind energy industry to assess the spatial variability of a given site. However, the TI derivation from atmospheric models has not been well examined. An algorithm is proposed in this study to realize online calculation of TI in the Weather Research and Forecasting (WRF) model. Simulated TI is divided into two components depending on scale, including sub-grid (parameterized based on turbulence kinetic energy (TKE)) and grid resolved. The sensitivity of sea surface temperature (SST) on simulated TI is also tested. An assessment is performed by using observations collected during a field campaign conducted from February to June 2020 near the Woods Hole Oceanographic Institution Martha's Vineyard Coastal Observatory. Results show that while simulated TKE is generally smaller than the lidar-observed value, wind speed bias is usually small. Overall, this leads to a slight underestimation in sub-grid-scale estimated TI. Improved SST representation subsequently reduces model biases in atmospheric stability as well as wind speed and sub-grid TI near the hub height. Large TI events in conjunction with mesoscale weather systems observed during the studied period pose a challenge to accurately estimating TI from models. Due to notable uncertainty in accurately simulating those events, this suggests summing up sub-grid and resolved TI may not be an ideal solution. Efforts in further improving skills in simulating mesoscale flow and cloud systems are necessary as the next steps.

17 WIND ENERGY↗
DOE OSTI2021

Linking Deep and Shallow Convective Mass Fluxes via an Assumed Entrainment Distribution in CAM5‐CLUBB: Parameterization and Simulated Precipitation Variability

Abstract We modify the Zhang‐McFarlane (ZM) deep convection scheme in the Community Atmosphere Model version 5 to couple it with a unified parameterization for boundary‐layer turbulence and shallow convection, that is, Cloud Layers Unified by Binormals (CLUBB). By assuming a lognormal distribution of entrainment rate across the entire moist convective regimes, we link mass fluxes between shallow and deep convection, which are partitioned by the entrainment rate of the shallowest deep convective plume. Hence, a new deep convective closure is established which is coupled to the sub‐grid vertical motion variability in CLUBB. The convection feedback (or memory) effects are also considered to decrease the entrainment spectrum width and enhance the vertical velocity variability that further affect deep convection. Results show that the revised scheme improves the precipitation simulations in terms of the mean state and variability at various timescales, such as the alleviated double‐intertropical convergence zone and more realistic simulations of the seasonal variation of monsoon precipitation over East Asia, Madden‐Julian Oscillation, and precipitation diurnal phase propagations downstream of large terrains. The improvements are still seen in many aspects such as the mean‐state precipitation when turning off the convection feedback impacts in the revised scheme, emphasizing the benefits of using the modified mass‐flux closure. However, the convection feedbacks have considerable effects on the precipitation diurnal cycle simulations over regions with late‐afternoon precipitation peaks. Overall, the revised scheme provides a unified treatment for sub‐grid vertical motions across regimes of boundary‐layer turbulence, shallow convection, and deep convection, leading to better‐simulated precipitation at various timescales.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

The Area Localized Coupled Model for Analytical Mean Flow Prediction in Arbitrary Wind Farm Geometries

This work introduces the area localized coupled (ALC) model, which extends the applicability of approaches that couple classical wake superposition models and atmospheric boundary layer models to wind farms with arbitrary layouts. Coupling wake and top–down boundary layer models is particularly challenging since the latter requires averaging over planform areas associated with turbine-specific regions of the flow that need to be specified. The ALC model uses Voronoi tessellation to define this local area around each turbine. A top–down description of a developing internal boundary layer is then applied over Voronoi cells upstream of each turbine to estimate the local mean velocity profile. Coupling between the velocity at hub-height based on this localized top–down model and a wake model is achieved by enforcing a minimum least-square-error in mean velocity in each cell. The wake model in the present implementation takes into account variations in wind farm inflow velocity and represents the wake profile behind each turbine as a super-Gaussian function that smoothly transitions between a top-hat shape in the region immediately following the turbine to a Gaussian profile downstream. Detailed comparisons to large-eddy simulation (LES) data from two different wind farms demonstrate the efficacy of the model in accurately predicting both wind farm power output and local turbine hub-height velocity for different wind farm geometries. These validations using data generated from two different LES codes demonstrate the model's versatility with respect to capturing results from different simulation setups and wind farm configurations.

49 EE - Wind and Water Power Program - Wind (EE-4W↗
DOE OSTI2024

Validation of High Resolution Atmospheric and Dispersion Simulations

Weather models typically operate on averages of 1 km or more. This averages out fine scale turbulence. This results in smooth laminar flows and rapid vertical motions are usually averaged out. In this study, those fine scaled features were simulated for a 2013 field study of transport and diffusion of a tracer gas. Parameterized subgrid turbulence was required to produce dilution representative of observations. Despite this, the simulation with the finest grid and contained parameterized subgrid turbulence produced dilution that was consistent with observations.

THOMAS, ANDREW↗
DOE OSTI2025

NPFTURBULENCE-Turbulent Layers Promoting New Particle Formation Field Campaign Report

This field campaign took place at the U.S. Department of Energy Atmospheric Radiation Measurement (ARM) use facility’s Southern Great Plains (SGP) atmospheric observatory, located near Lamont, Oklahoma. The ArcticShark uncrewed aerial system conducted 10 research flights, accumulating 40.7 flight hours over the SGP site. Each flight originated from the Blackwell-Tonkawa Airport (BKN) and followed a “racetrack” pattern around the SGP field site. During these flights, mission scientists initiated vertical profiling based on the real-time performance of the aerosol instruments aboard the ArcticShark. Figure 1 below illustrates the flight paths of all missions.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

Turbulence and Cloudiness in Cumulus-Topped Marine Boundary Layers

Turbulence and cloudiness in cumulus-topped marine boundary layers are studied using data from the Atmospheric Radiation Measurement (ARM) Eastern North Atlantic (ENA) site. The analysis includes eight periods of nonprecipitating shallow cumulus clouds, spanning 141 h and encompassing 603 individual clouds. On average, the cumulus had bases at 558 m, were 99 m thick, had a chord length of 500 m, and exhibited an hourly base-layer cloudiness of 12%. Changes in cloud fraction were primarily driven by variations in cloud number not chord length. High-resolution Doppler lidar and cloud radar observations were used to estimate updraft and downdraft mass fluxes in the cumulus base layer under both clear and cloudy regions. Cloudy updrafts contributed only ∼23% of the total updraft mass flux at cloud base, indicating that most upward mass flux originated from clear-air eddies. Updraft strength, rather than updraft fraction, was found to predominantly control both clear and cloudy updraft mass fluxes. Cloud-base cloudiness strongly correlated with cloudy updraft mass flux but showed negligible correlation with clear-air updraft mass flux. Clear-air downdraft mass flux exhibited a strong relationship with the ratio of surface buoyancy to inversion strength. Mesoscale analysis revealed that moist patches had greater cloudiness, updraft mass flux, and vertical velocity variance compared to dry patches. Additionally, mesoscale base-layer cloud fraction was highly and significantly correlated with cloudy updraft mass flux in both dry and moist environments. Results presented herein have implications for cumulus parameterization development along with climatological and model evaluation studies conducted at the ENA site.

cumulus clouds↗
DOE OSTI2023

Quantitative Characterization of Hyper-Local Atmospheric Greenhouse Gas Sources

Atmospheric greenhouse gas (GHG) emissions are often characterized using stationary, tower-based sensors. Ground based sensors reside in the turbulent boundary layer and are subject to intense concentration impulses from hyper-local (<100m) point sources of emissions. These high frequency spikes are often filtered out in broader emission flux studies, losing valuable information about how hyper-local sources influence receptors. In this study, we investigated how empirical atmospheric data can be used to locate and quantify a concurrently measured hyper-local point source in a dense urban setting. An eddy covariance style tower and a low-cost sensor tower were deployed in various locations around an urban, hyper-local CO2/CH4 emissions source (a continuously measured restaurant exhaust vent). A model using different processing and statistical techniques was built to examine the most effective procedures for source isolation, directional location, and emission quantification. Using excess concentrations above a minimum baseline, we identify the source using bivariate polar plots and quantify the relationship between source size, receptor distance, and statistical proxies. Furthermore, we find that varying statistical thresholds allows for identification of less influential sources which are drowned out by larger or closer sources. Finally, we show that large sources can be effectively characterized using low-cost sensors, a valuable outcome informing how networks for monitoring larger areas could be implemented. This work may provide a basis for source identification and monitoring protocols for networks that feature sensors influenced by hyper-local point sources, subject to site-specific assumptions.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Fog Intermittency and Critical Behavior

The intermittency of fog occurrence (the switching between fog and no-fog) is a key stochastic feature that plays a role in its duration and the amount of moisture available. Here, fog intermittency is studied by using the visibility time series collected during the month of July 2022 on Sable Island, Canada. In addition to the visibility, time series of air relative humidity and turbulent kinetic energy, putative variables akin to the formation and breakup conditions of fog, respectively, are also analyzed in the same framework to establish links between fog intermittency and the underlying atmospheric variables. Intermittency in the time series is quantified with their binary telegraph approximations to isolate clustering behavior from amplitude variations. It is shown that relative humidity and turbulent kinetic energy bound many stochastic features of visibility, including its spectral exponent, clustering exponent, and the growth of its block entropy slope. Although not diagnostic, the visibility time series displays features consistent with Pomeau–Manneville Type-III intermittency in its quiescent phase duration PDF scaling (−3/2), power spectrum scaling (−1/2), and signal amplitude PDF scaling (−2). The binary fog time series exhibits properties of self-organized criticality in the relation between its power spectrum scaling and quiescent phase duration distribution.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Simulation of radioactive plume transport in the atmosphere including dynamics of particle aggregation and breakup

Accurate prediction of the atmospheric transport of debris particles relies heavily on our knowledge of the size distribution of the particles within a debris cloud. Assuming a fixed particle size during simulations is not always viable since the size distribution of the debris can change during transport. Various microphysical processes, such as aggregation and breakup, influence debris particles and dictate any changes to the size distribution. To track those changes that can occur, a population balance model can be adopted and instituted within a model framework. Nonetheless, many of the models that simulate the transport of radioactive debris following a device-driven fission incident have historically neglected to consider these processes. As such, this work describes our effort to develop a modeling framework capable of simulating the transport and deposition of a radioactive plume generated from a fission incident with a dynamic population balance including particle aggregation and breakup. Herein the impact of aggregation and breakup, individually and collectively, on the particle size distribution is explored using the developed framework. When simulating aggregation, for example, six mechanisms, including Brownian coagulation, the convective enhancement to Brownian coagulation, van der Waals-viscous force correction for Brownian coagulation, gravitational collection, turbulent inertial motion, and turbulent shear, are considered. Brownian coagulation and its corrections have, as one would expect, a large impact on relatively small aggregates. Aggregates with a diameter that is less than or equal to 1.0 μm, for instance, comprise 50.6 vol % of all aggregates in the absence of aggregation and 31.2 vol % when Brownian coagulation and its corrections are considered. Gravitational collection and, to a much lesser extent, turbulent shear and turbulent inertial motion are, conversely, of great importance to relatively large aggregates (i.e., diameter greater than 3.0 μm). Additionally, the individual effects of atmospheric and particle parameters, such as wind speed and particle density, are examined. Of the parameters examined, turbulent energy dissipation and aggregate fractal dimension (i.e., aggregate shape with lower values representing more irregular particles) were of substantial importance since both terms directly impact aggregate stability and, by extension, the breakup rate. Large-scale transport and deposition simulations in a dry atmosphere are also presented and discussed as a proof of concept.

98 NUCLEAR DISARMAMENT, SAFEGUARDS, AND PHYSICAL P↗
DOE OSTI2025

A Data Library of Liquid Clouds Modelled With a Large Eddy Simulation Framework

We describe a library of atmospheric large eddy simulations (LES) of liquid-phase boundary layer clouds constructed to enable aerosol–cloud–turbulence interaction studies, support parameterization evaluation and development, and provide training data for machine learning applications. The simulations use a modern LES framework designed for high numerical accuracy, coupled to a detailed spectral bin microphysical scheme. Case studies are configured to represent observed conditions in four key global cloud regions—the Northeastern Atlantic, Northeastern Pacific, Continental United States and Southern Ocean—following a semi-idealised approach. The library also includes aerosol concentration halving and doubling experiments to expose the sensitivities of the case studies to aerosol perturbations. Simulation results are compared to observations on a case-by-case basis, then the library's coverage is evaluated in terms of spreads in meteorological factors and atmospheric boundary layer attributes.

aerosol↗
DOE OSTI2025

Operational wind plants increase planetary boundary layer height: an observational study

As wind energy deployment grows, interactions between wind plants and the surrounding environment become more prevalent. The current investigation seeks to understand these interactions by characterizing the impact of wind plants on the planetary boundary layer height (PBLH), utilizing observations from the American WAKE experimeNt (AWAKEN) campaign. Given the ambiguity of the definition of PBLH under stable atmospheric conditions, where the impact of wind plants is expected to be strongest, a comparison of different methods for identifying PBLH is first conducted using data collected by multiple types of instruments. The Heffter method is selected as the thermodynamic method because it generates the most consistent results for the radiosonde and infrared spectrometer. A minimum vertical velocity variance method is used for a turbulence-based definition. Using both of these methods, the values of PBLH measured at spatially distributed sites are compared under a range of atmospheric conditions. Both methods show a clear increase in PBLH downstream of a wind plant for stable conditions. These impacts are strongest when the upstream PBLH is shallow (less than 0.25 km), with the thermodynamic method showing a PBLH increase of 35 %–39 % and the turbulence-based method showing a 143 % increase. At a site 20 km downstream of the wind plant, these effects are no longer observed, suggesting PBLH is not influenced by the wind plant at this distance. The results of this investigation show that wind plants can modify PBLH in their vicinity. As PBLH is a key parameter for numerical models, this insight into wind plant–atmosphere interactions can be used to validate and improve the accuracy of wind energy forecasts.

17 WIND ENERGY↗
DOE OSTI2020

Impact of boundary layer simulation on predicting radioactive pollutant dispersion: a case study for HANARO Research Reactor using the WRF-MMIF-CALPUFF modeling system

Wind plays an important role in cases of unexpected radioactive pollutant dispersion, deciding distribution and concentration of the leaked substance. The accurate prediction of wind has been challenging in numerical weather prediction models, especially near the surface because of the complex interaction between turbulent flow and topographic effect. As such, in this study, we investigated the characteristics of atmospheric dispersion of radioactive material (i.e. 137 Cs) according to the simulated boundary layer around the HANARO research nuclear reactor in Korea using the Weather Research and Forecasting (WRF)-Mesoscale Model Interface (MMIF)-California Puff (CALPUFF) model system. We examined the impacts of orographic drag on wind field, stability calculation methods, and planetary boundary layer parameterizations on the dispersion of radioactive material under a radioactive leaking scenario. We found that inclusion of the orographic drag effect in the WRF model improved the wind prediction most significantly over the complex terrain area, leading the model system to estimate the radioactive concentration near the reactor more conservatively. We also emphasized the importance of the stability calculation method and employing the skillful boundary layer parameterization to ensure more accurate low atmospheric conditions, in order to simulate more feasible spatial distribution of the radioactive dispersion in leaking scenarios.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Unsupervised discovery of extreme weather events using universal representations of emergent organization

Spontaneous self-organization is ubiquitous in systems far from thermodynamic equilibrium. While organized structures that emerge dominate transport properties, universal representations that identify and describe these key objects remain elusive. Here, we introduce a theoretically grounded framework for describing emergent organization that, via data-driven algorithms, is constructive in practice. Its building blocks are spacetime lightcones that embody how information propagates across a system through local interactions. We show that predictive equivalence classes of lightcones—local causal states—capture organized behaviors in complex spatiotemporal systems. Employing an unsupervised physics-informed machine learning algorithm and a high-performance computing implementation, we demonstrate automatically discovering organized structures in two real-world domain science problems. We show that local causal states identify vortices and track their power-law decay behavior in two-dimensional fluid turbulence. We then show how to detect and track familiar extreme weather events—hurricanes and atmospheric rivers—and discover other novel structures associated with precipitation extremes in high-resolution climate data at the grid-cell level.

Rupe, Adam [Pacific Northwest National Laboratory ↗
DOE OSTI2022

Rain-Induced Stratification of the Equatorial Indian Ocean and Its Potential Feedback to the Atmosphere

Surface freshening through precipitation can act to stably stratify the upper ocean, forming a rain layer (RL). RLs inhibit subsurface vertical mixing, isolating deeper ocean layers from the atmosphere. This process has been studied using observations and idealized simulations. The present ocean modeling study builds upon this body of work by incorporating spatially resolved and realistic atmospheric forcing. Fine-scale observations of the upper ocean collected during the Dynamics of the Madden-Julian Oscillation field campaign are used to verify the General Ocean Turbulence Model (GOTM). Spatiotemporal characteristics of equatorial Indian Ocean RLs are then investigated by forcing a 2D array of GOTM columns with realistic and well-resolved output from an existing regional atmospheric simulation. RL influence on the ocean-atmosphere system is evaluated through analysis of RL-induced modification to surface fluxes and sea surface temperature (SST). This analysis demonstrates that RLs cool the ocean surface on time scales longer than the associated precipitation event. A second simulation with identical atmospheric forcing to that in the first, but with rainfall set to zero, is performed to investigate the role of rain temperature and salinity stratification in maintaining cold SST anomalies within RLs. Approximately one third, or 0.1°C, of the SST reduction within RLs can be attributed to rain effects, while the remainder is attributed to changes in atmospheric temperature and humidity. The prolonged RL-induced SST anomalies enhance SST gradients that have been shown to favor the initiation of atmospheric convection. These findings encourage continued research of RL feedbacks to the atmosphere.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

The effect of dynamic near-wake modulation on utility-scale wind turbine wake development

High-resolution field-scale experiments using flow visualization with natural snowfall and high-fidelity large eddy simulations are combined to investigate the effect of dynamic turbine operation and atmospheric conditions on wind turbine wake mixing and recovery in the wake of a 2.5 MW wind turbine. Instantaneous near-wake expansion and deflection in response to changes in blade pitch and wind direction, termed dynamic wake modulation, is quantified using both techniques, demonstrating excellent agreement. The simulations are used to extend these results by calculating the energy flux into the wake 7 rotor diameters downstream, showing that dynamic turbine-atmospheric interactions enhance mixing in the far-wake. This finding is exhibited under both uniform and turbulent inflow conditions. Under turbulent flow, a synergistic relationship is also observed between dynamic wake modulation and wake meandering, as wake recovery can be further accelerated when the two phenomena occur together. The results of this study have implications for the development of more realistic far-wake models that include the significant impact of dynamic wake modulation on wake mixing and development. Additionally, the findings from the current study can be used to develop advanced control algorithms to speed up wake breakdown and recovery, further improving wind farm efficiency.

17 WIND ENERGY↗
DOE OSTI2020

Detection of Extreme Phenomena in the Stable Boundary Layer over the Amazonian Forest

We apply different methods for detection of extreme phenomena (EP) in air-turbulent time series measured in the nocturnal boundary layer above the Amazon forest. The methods used were: (a) a Morlet complex wavelet transform, which is often used in analysis of non-linear application processes. Through the use of the wavelet, it is possible to observe a phase singularity that involves a strong interaction between an extensive range of scales; (b) recurrence plot tests, which were used to identify a sudden change between different stable atmospheric states. (c) statistical analysis of early-warning signals, which verify simultaneous increases in the autocorrelation function and in the variance in the state variable; and (d) analysis of wind speed versus turbulent kinetic energy to identify different turbulent regimes in the stable boundary layer. We found it is adequate to use a threshold to classify the cases of strong turbulence regime, as a result of the occurrence of EP in the tropical atmosphere. All methods used corroborate and indicate synergy between events that culminate in what we classify as EP of the stable boundary layer above the tropical forest.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

A Package of Momentum and Heat Transfer Coefficients for the Stable Surface Layer Extended by New Coefficients over Sea Ice

The bulk transfer coefficients of momentum, heat, and humidity belong to the main ingredients of numerical weather prediction and climate models. They are needed for the calculation of turbulent fluxes in the surface layer and often rely on the Monin–Obukhov similarity theory requiring universal stability functions. The problem of a derivation of transfer coefficients based on different stability functions has been considered by many researchers over the years but it remains to this day. In this work, dedicated to the memory of S.S. Zilitinkevich, we also address this task, and obtain transfer coefficients from three pairs of theoretically derived stability functions suggested by Zilitinkevich and co-authors for stable conditions. Additionally, we construct non-iterative parametrizations of these transfer coefficients based on earlier work. Results are compared with state-of-the-art coefficients for land, ocean, and sea ice. The combined parametrizations form a package in a universal framework relying on a semi-analytical solution of the Monin-Obukhov similarity theory equations. A comparison with data of the Surface Heat Budget of the Arctic Ocean campaign (SHEBA) over sea ice reveals large differences between the coefficients for land conditions and the measurements over sea ice. However, two schemes of Zilitinkevich and co-authors show, after slight modification, good agreement with SHEBA although they had not been especially developed for sea ice. One pair of the modified transfer coefficients is superior and is compatible to earlier SHEBA-based parametrizations. Finally, an algorithm for practical use of all transfer coefficients in climate models is given.

54 ENVIRONMENTAL SCIENCES↗