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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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All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Quantifying the Impacts of Grain-scale Heterogeneity on Mechanical Response

Driven by the exceedingly high computational demands of simulating mechanical response in complex engineered systems with finely resolved finite element models, there is a critical need to optimally reduce the fidelity of such simulations. The minimum required fidelity is constrained by error tolerances on the simulation results, but error bounds are often impossible to obtain a priori. One such source of error is the variability of material properties within a body due to spatially non-uniform processing conditions and inherent stochasticity in material microstructure. This study seeks to quantify the effects of microstructural heterogeneity on component- and system-scale performance to aid in the choice of an appropriate material model and spatial resolution for finite element analysis.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Real-time neutron multiplicity and source localization for criticality safety during fuel debris removal

Advancing neutron detection and analysis techniques for complex radiation environments is an ongoing focus in nuclear instrumentation and monitoring. This proposal presents research and development of a generalized real-time neutron monitoring and analysis system, applicable to any detector capable of producing time-tagged neutron count data. While the work is demonstrated using the Neutron Multiplication Analysis Detector (NoMAD), a modular 15-tube helium-3 (He-3) array, due to its availability, spatial resolution, and flexible deployment, the methods developed are extensible to other systems, including organic scintillators and fast digital detectors. This research investigates two complementary analytical techniques for real-time characterization of neutron emitting sources: neutron multiplicity estimation based on the Hage-Cifarelli formalism and spatial localization using supervised machine learning applied to spatial count rate patterns. These methods are designed to operate under dynamic, evolving conditions such as fuel debris retrieval or reactor startup, where neutron-emitting material geometries may be partially unknown or changing over time. By integrating statistical neutron emission data with spatial localization, this research aims to develop and evaluate methods for real time neutron monitoring, source characterization, and material verification. Key contributions include implementation of a low-latency data pipeline for continuous neutron multiplicity analysis, development and validation of machine learning models for spatial inference, and experimental evaluation of system performance under variable measurement conditions. The outcomes are intended to support applications in nuclear safeguards, verification, emergency response, and reactor startup.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2021

Using Particle Image Velocimetry to Determine Turbulence Model Parameters

The primary parameter of a standard k-ϵ model, C μ , was calculated from stereoscopic particle image velocimetry (PIV) data for a supersonic jet exhausting into a transonic crossflow. This required the determination of turbulent kinetic energy, turbulent eddy viscosity, and turbulent energy dissipation rate. Image interrogation was optimized, with different procedures used for mean strain rates and Reynolds stresses, to produce useful turbulent eddy viscosity fields. The eddy viscosity was calculated by a least-squares fit to all components of the three-dimensional strain-rate tensor that were available from the PIV data. This eliminated artifacts and noise observed when using a single strain component. Local dissipation rates were determined via Kolmogorov’s similarity hypotheses and the second-order structure function. The eddy viscosity and dissipation rates were then combined to determine C μ . Considerable spatial variation was observed in C μ , with the highest values found in regions where turbulent kinetic energy was relatively low but where turbulent mixing was important, e.g., along the high-strain jet edges and in the wake region. This suggests that use of a constant C μ in modeling may lead to poor Reynolds stress predictions at mixing interfaces. A data-driven modeling approach that can predict this spatial variation of C μ based on known state variables may lead to improved simulation results without the need for calibration.

42 ENGINEERING↗
DOE OSTI2026

BSEC ecohydrological and water quality fluxes from RHESSys Simulations in USGS gauged watersheds

Baltimore Environmental Social Collaborative (BSEC) Water and Water Quality Simulations from RHESSys Model The repository contains RHESSys (Tague & Band, 2004; source code) simulated ecohydrological and nutrient (nitrogen only) fluxes at daily, basin-average (RHESSys_basin_output) and monthly, grid (RHESSys_patch_output) levels. We currently simulated the following 8 watersheds in Baltimore: Dead Run Baisman Run Scotts Level Branch Moores Run Powder Mill Run Maidens Choice Run Stony Run The watershed boundaries of all studied watersheds are stored in Watershed_Boundary folder. Variables and their units are listed in the metadata. Spatial projection, NAD83 / UTM zone 18N (EPSG:26918) is used for patch-level, netCDF-format files. For more information, please contact Ruoyu Zhang (rz3jr@virginia.edu).

Baltimore MD↗
DOE OSTI2023

Modeling Wind-Hydrogen System and Analyzing Curtailment

Low-cost green hydrogen can be achieved by integrating utility-scale wind farms to an electrolyzer. The power produced by a wind farm depends on spatial and temporal changes in the wind, and this variability can affect the electrolyzer's performance. This study examines the trade-offs between two electrolyzer configurations: a large centralized electrolyzer connected to the wind farm versus several smaller distributed electrolyzers connected directly to each wind turbine. The results show that a centralized configuration generates more hydrogen because turbines that are waked and produce less power are compensated but other un-waked turbines.

electrolyzer↗
DOE OSTI2022

Dust pollution in China affected by different spatial and temporal types of El Niño

Dust is an important aerosol affecting air quality in China in the winter and spring seasons. Dust in China is potentially influenced by the interannual climate variability associated with El Niño. Here, the impacts of El Niño with different temporal and spatial types on dust pollution in boreal winter and spring in China and the potential mechanisms are investigated using a state-of-the-art Earth system model (E3SMv1). We find that the eastern Pacific (EP) and central Pacific (CP) El Niño both increase wintertime dust concentrations by 5–50 µg m –3 over central-eastern China. Due to a stronger wind and lower relative humidity, which favor dust emissions near sources, and a strengthened northwesterly and reduced precipitation, which are conducive to dust transport, dust concentrations during the CP El Niño are 5–20 µg m –3 higher in northern China than during the EP El Niño, although the changes are mostly insignificant. El Niño with a short duration (SD) increases boreal winter dust concentrations by 20–100 µg m –3 over northern China relative to the climatological mean, while there is a decrease of 5–50 µg m –3 during the long-duration (LD) El Niño, which is also related to the El Niño-induced changes in atmospheric circulation, precipitation, and relative humidity. In the following spring season, all types of El Niño events enhance dust over northern China, but only the increase during the LD El Niño is statistically significant, suggesting that the weaker intensity but longer duration of the LD El Niño events can significantly affect spring dust in China. Our results contribute to the current knowledge of the influence of El Niño on dust pollution, which has profound implications for air pollution control and dust storm prediction.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

A Large Ensemble Global Dataset for Climate Impact Assessments

We present a self-consistent, large ensemble, high-resolution global dataset of long-term future climate, which accounts for the uncertainty in climate system response to anthropogenic emissions of greenhouse gases and in geographical patterns of climate change. The dataset is developed by applying an integrated spatial disaggregation (SD) - bias-correction (BC) method to climate projections from the MIT Integrated Global System Model (IGSM). Four emission scenarios are considered that represent energy and environmental policies and commitments of potential future pathways, namely, Reference, Paris Forever, Paris 2 °C and Paris 1.5 °C. The dataset contains nine key meteorological variables on a monthly scale from 2021 to 2100 at a spatial resolution of 0.5°x 0.5°, including precipitation, air temperature (mean, minimum and maximum), near-surface wind speed, shortwave and longwave radiation, specific humidity, and relative humidity. We demonstrate the dataset’s ability to represent climate-change responses across various regions of the globe. This dataset can be used to support regional-scale climate-related impact assessments of risk across different applications that include hydropower, water resources, ecosystem, agriculture, and sustainable development.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

A double peak in the seasonality of California's photosynthesis as observed from space

Solar-induced chlorophyll fluorescence (SIF) has been shown to be a powerful proxy for photosynthesis and gross primary productivity (GPP). The recently launched TROPOspheric Monitoring Instrument (TROPOMI) features the required spectral resolution and signal-to-noise ratio to retrieve SIF from space. Here, we present a downscaling method to obtain 500 m spatial resolution SIF over California. We report daily values based on a 14 d window. TROPOMI SIF data show a strong correspondence with daily GPP estimates at AmeriFlux sites across multiple ecosystems in California. We find a linear relationship between SIF and GPP that is largely invariant across ecosystems with an intercept that is not significantly different from zero. Measurements of SIF from TROPOMI agree with MODerate Resolution Imaging Spectroradiometer (MODIS) vegetation indices – the normalized difference vegetation index (NDVI), enhanced vegetation index (EVI), and near-infrared reflectance of vegetation index (NIR$_v$) – at annual timescales but indicate different temporal dynamics at monthly and daily timescales. TROPOMI SIF data show a double peak in the seasonality of photosynthesis, a feature that is not present in the MODIS vegetation indices. The different seasonality in the vegetation indices may be due to a clear-sky bias in the vegetation indices, whereas previous work has shown SIF to have a low sensitivity to clouds and to detect the downregulation of photosynthesis even when plants appear green. We further decompose the spatiotemporal patterns in the SIF data based on land cover. The double peak in the seasonality of California's photosynthesis is due to two processes that are out of phase: grasses, chaparral, and oak savanna ecosystems show an April maximum, while evergreen forests peak in June. An empirical orthogonal function (EOF) analysis corroborates the phase offset and spatial patterns driving the double peak. The EOF analysis further indicates that two spatiotemporal patterns explain 84 % of the variability in the SIF data. Results shown here are promising for obtaining global GPP at sub-kilometer spatial scales and identifying the processes driving carbon uptake.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Investigating Thaw and Plant Productivity Constraints on Old Soil Carbon Respiration From Permafrost

Isotopic radiocarbon (Δ 14 C) signatures of ecosystem respiration (Reco) can identify old soil carbon (C) loss and serve as an early indicator of permafrost destabilization in a warming climate. Warming also stimulates plant productivity causing plant respiration to dominate Reco Δ 14 C signatures and potentially obscuring old soil C loss. Here, we investigate how a wide spatio-temporal gradient of permafrost thaw and plant productivity affects Reco Δ 14 C patterns and isotopic partitioning. Spatial gradients came from a warming experiment with doubling thaw depth and variable biomass, and a vegetation removal manipulation to eliminate plant contributions. We sampled in August and September to capture transitions from high to low plant productivity, decreased surface soil temperature, and relatively small seasonal thaw extensions. We found that surface processes dominate spatial variation in old soil C loss and a process-based partitioning approach was crucial for constraining old soil C loss. Resampling the same plots in different times of the year revealed that old soil C losses tripled with cooling surface temperature, and the largest old soil C losses were detected when the organic-to-mineral soil horizons thawed (~50–60 cm). We suggest that the measured increase in old soil respiration over the season and when the organic-to-mineral horizon thawed, may be explained by mobilization of nitrogen that stimulates microbial decomposition at depth. Our results suggest that soil C in the organic to mineral horizon may be an important source of soil C loss as the entire Arctic region warms and could lead to nonlinearities in projected permafrost climate feedbacks.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

Development of an Unbiased Future Solar Dataset for Solar Resource Adequacy Research Over CONUS

A high-resolution, long-term solar dataset is essential for capturing the variability of solar energy resources and informing strategies to ensure grid reliability and resilience in systems with high levels of solar energy integration. This study focuses on generating unbiased, high-resolution projections of solar irradiance through a statistical downscaling framework, using Earth system model (ESM) simulations obtained from the North American Coordinated Regional Climate Downscaling Experiment (NA-CORDEX). The National Solar Radiation Database (NSRDB) is used to calibrate statistical downscaling models. The newly developed dataset provides solar irradiance, surface air temperature, and surface wind speed at 4-km and hourly resolutions across the contiguous United States (CONUS), based on two future scenarios (RCP4.5 and RCP8.5). This study outlines key steps in developing the high-resolution future solar dataset, including (1) regridding ESM data to a common 20-km resolution grid, (2) correcting ESM biases using the NSRDB, and (3) applying temporal and spatial downscaling methods to generate high-resolution (4-km, hourly) solar projections. Preliminary results indicate that downscaled projections (4-km) captured reasonable spatial patterns when compared to observations across CONUS for four variables. On average across all pixels, 4-km daily-total GHI and DNI projections showed normalized bias (nBias) less than 1% and 6% for GHI and DNI against NSRDB, respectively (nBias less than 1% and 5% for daily-average surface air temperature and surface wind speed). In terms of long-term trend for GHI and DNI, there was no strong increasing or decreasing trend (when compared to surface air temperature), but it showed a very weak decreasing trend.

14 SOLAR ENERGY↗
DOE OSTI2025

Downscaled CMIP5 projections of physical fire risk understate historical trends

Reliable projections of wildfire risk are important for multi-sector impacts analysis. Statistically downscaled and bias-corrected Earth system model ensemble products are routinely used to analyze regional physical wildfire risk, but evaluations of historical observed trends and variability are lacking. Here, we evaluate physical fire risk over the western United States using the Canadian Forest Fire Weather Index (FWI) by comparing model outputs from the Coupled Model Intercomparison Project Phase 5 (CMIP5), statistically downscaled via the Multivariate Adaptive Constructed Analogs (MACA) approach, against the observational target dataset gridMET, a gridded high-resolution surface meteorological product. We analyze multidecadal trends and interannual variability in seasonal average FWI for the historical period and future projections under two emissions scenarios, and we compare MACA-CMIP5 ensemble results with a simple time series model that generates historical and future projections of seasonal FWI based on bootstrapping observed historical trends and variability. Our findings indicate that MACA-CMIP5 accurately captures the magnitude and spatial patterns of seasonally averaged FWI but tends to underestimate historical decadal trends. We show that future increases in fire risk may be underestimated relative to the simple time series model that projects historical variability into the future. We also highlight that model biases in relative humidity contribute significantly to model-data differences. Our results underscore the importance of historical hindcasting exercises for informing broader multi-sector applications.

FWI↗
DOE OSTI2022

A Review of Bayesian Networks for Spatial Data

We report Bayesian networks are a popular class of multivariate probabilistic models as they allow for the translation of prior beliefs about conditional dependencies between variables to be easily encoded into their model structure. Due to their widespread usage, they are often applied to spatial data for inferring properties of the systems under study and also generating predictions for how these systems may behave in the future. We review published research on methodologies for representing spatial data with Bayesian networks and also summarize the application areas for which Bayesian networks are employed in the modeling of spatial data. We find that a wide variety of perspectives are taken, including a GIS-centric focus on efficiently generating geospatial predictions, a statistical focus on rigorously constructing graphical models controlling for spatial correlation, as well as a range of problem-specific heuristics for mitigating the effects of spatial correlation and dependency arising in spatial data analysis. Special attention is also paid to potential future directions for integration of Bayesian networks with spatial processes.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

Reactive particle-tracking solutions to a benchmark problem on heavy metal cycling in lake sediments

Geochemical systems are known to exhibit highly variable spatiotemporal behavior. This may be observed both in non-smooth concentration curves in space for a single sampling time and also in variability between samples taken from the same location at different times. However, most models that are designed to simulate these systems provide only single-solution smooth curves and fail to capture the noise and variability seen in the data. We apply a recently developed reactive particle-tracking method to a system that displays highly complex geochemical behavior. When the method is made to most closely resemble a corresponding Eulerian method, in its unperturbed form, we see near-exact match between solutions of the two models. More importantly, we consider two approaches for perturbing the model and find that the spatially-perturbed condition is able to capture a greater degree of the variability present in the data. This method of perturbation is a task to which particle methods are uniquely suited and Eulerian models are not well-suited. Additionally, because of the nature of the algorithm, noisy spatial gradients can be highly resolved by a large number of mobile particles, and this incurs negligible computational cost, as compared to expensive chemistry calculations.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Simulating water dynamics related to pedogenesis across space and time: Implications for four-dimensional digital soil mapping

Digital soil mapping (DSM) relies on machine-learning and geostatistics to represent soil property observations across space. DSM techniques are powerful but often empirical, being limited to the quality and density of point samples. Water dynamics are closely related to soil variability, and the physics that govern water movement are well known. Hydrological properties can hence be simulated by physical models through space and time, unveiling key characteristics about soils. We propose the use of hydrologic models to map soils across the surface (2D), depth (1D), and time (1D)–which provides a 4D approach to digital soil mapping (4DSM). The Distributed Hydrology Soil Vegetation Model (DHSVM) was applied to a watershed currently under pasture. Moisture sensors and wells were installed at different depths in the watershed on summit, sideslope and toeslope positions to validate the model. DHSVM simulations of soil moisture distribution and depth to saturation were performed during the hydrological year (October 2008-September 2009). Clusters of similar pixels based on soil moisture values were determined using Dynamic Time Warping (DTW) to align temporal data and K-means. Clustering was performed both seasonally and for the entire year. Temporal patterns simulated by DHSVM matched measurements given by moisture sensors and wells. Seasonal clusters differed from the annual cluster. Distinct clusters were observed for each season and with depth, showing that spatiotemporal soil variability is lost when statically assessing soils. Spatiotemporal clusters corroborated field observations of fragipan occurrence not explicitly spatially mapped by Soil Survey Geographic Database (SSURGO). If a connection can be made between water and soils, static and dynamic soil variability can be predicted using physically based hydrologic models. Hydrologic models can benefit soil mapping by enabling reliable 4D simulation of water dynamics, which are fundamental to soil variability and soil classification and directly relate to biological, physical and chemical soil processes not captured by typical soil sampling protocols.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Parameterizing Convective Organization Effects With a Moisture-PDF Approach in Climate Models: Concept and a Regional Case Simulation

We propose a parameterization scheme of convective organization effects based on a moisture-distribution approach, which can reflect aggregation of convective cells within a model grid as well as the interaction between convection and spatial heterogeneity in free-troposphere moisture. With this concept, convective cells in an aggregated state are surrounded by air that is moister than the grid-mean condition, which provides a “shielding” effect favorable for deeper convective updrafts. Such effects are represented quantitatively via utilizing a schematic diagram about the geometric interpretation of convective cluster, dry area, and their contact area, in which both the convective cluster size and dry area fraction increase during the development of convection. Our sensitivity analyses indicate that the new scheme performs well in capturing the mean precipitation features. Excluding the convective organization effects in the model leads to a considerable reduction in the simulated precipitation magnitude. Notably, there exists an inverse relationship between background mean moisture condition and sub-grid moisture variability for a given rain rate, suggesting that with a high spatial heterogeneity in free-troposphere moisture, convection can still develop and maintain its strength under relatively dry background conditions, consistent with the convection-permitting model simulation. Overall, our new parameterization of convective organization effects can successfully reproduce the relationship between precipitation and sub-grid moisture variability, which is a missing element in traditional convection parameterization schemes but important for the simulations of precipitation variability at various scales in climate models.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

High-resolution modeling of indoor radon exposure with uncertainty quantification in Utah

Indoor radon accounts for 37% of population-level exposure to ionizing radiation in the United States. However, radon metrics are typically reported at coarse spatial scales, potentially obscuring meaningful local variation. We developed a high-resolution modeling framework to estimate indoor radon concentrations across Utah while explicitly quantifying predictive uncertainty. A total of 19,497 residential radon measurements collected between 2006 and 2017 were combined with environmental and housing characteristics and analyzed using a geospatial neural network that accommodates spatial dependence and nonlinear associations. Predictions were generated on a uniform hexagonal grid at 0.73 km2 resolution (H3 level 8). Out-of-sample predictions aggregated to the H3 level 8 grid showed good agreement with observed concentrations (Pearson r=0.64), while household-level predictions exhibited more moderate agreement (r=0.45). The model produced well-calibrated uncertainty estimates, with 24.1% of held-out observations exceeding the predicted 75th-percentile threshold. Maps of predicted radon concentrations and the probability of exceeding the U.S. EPA action level of 148 Bq/m3 (4 pCi/L) revealed substantial fine-scale spatial heterogeneity that was not apparent in conventional coarse-resolution summaries, with greater local variability observed in densely monitored urban counties than in sparsely sampled regions. High-resolution radon models that explicitly quantify uncertainty provide a useful framework for characterizing the spatial distribution of indoor radon and identifying areas of elevated exceedance risk. These findings highlight the value of fine-scale monitoring data and uncertainty-aware modeling approaches for radon exposure assessment, environmental risk characterization, and radon-related health research.

Wu, Yunhan [ORNL] (ORCID:0000000178842994)↗
DOE OSTI2025

Predicting weather impacts on corn production in a data-limited region using a transfer learning approach

The stability of food supply and prices may depend more on annual changes in yields from year-to-year variability in weather than on longer-term average changes from changing climatic conditions. However, the absence of high-quality data on crop yields at fine spatial resolutions in many regions of the world makes it challenging to statistically model their response to interannual variability in weather patterns. Therefore, there is a need for empirical methods that can project annual crop yield changes even in limited data regions. Here, we propose a transfer learning algorithm that uses high spatial resolution data from one region to project yields in another region with more limited data. The goal of our work is to understand what data types can be beneficial for transferring learning from a source region to a very different target region with more limited data. We utilize Long Short-Term Memory to develop a transfer learning model that is trained on historical county-level corn yield in the United States and predicts district-level corn yield variations in India. Even using smaller amounts of data in India, simulating a data-scarce region, we achieve an average root mean square error of 0.48 bu acre−1 in predicting interannual yield variations. Using Shapley values to interpret results, we explore the contribution of the different weather parameters to interannual yield variability and find a larger influence of precipitation-related variables. Our study demonstrates the usefulness of this method for transferring models of weather impacts on crop yields trained on a data-rich country to one with more limited data. It suggests the potential of applying the transfer learning model to mitigate the need for extensive raw data globally.

Vishwakarma, Srishti [ORNL] (ORCID:000000031674419↗
DOE OSTI2024

Profiling the molecular destruction rates of temperature and humidity as well as the turbulent kinetic energy dissipation in the convective boundary layer

A simultaneous deployment of Doppler, temperature, and water-vapor lidars is able to provide profiles of molecular destruction rates and turbulent kinetic energy (TKE) dissipation in the convective boundary layer (CBL). Horizontal wind profiles and profiles of vertical wind, temperature, and moisture fluctuations are combined, and transversal temporal autocovariance functions (ACFs) are determined for deriving the dissipation and molecular destruction rates. These are fundamental loss terms in the TKE as well as the potential temperature and mixing ratio variance equations. These ACFs are fitted to their theoretical shapes and coefficients in the inertial subrange. Error bars are estimated by a propagation of noise errors. Sophisticated analyses of the ACFs are performed in order to choose the correct range of lags of the fits for fitting their theoretical shapes in the inertial subrange as well as for minimizing systematic errors due to temporal and spatial averaging and micro- and mesoscale circulations. We demonstrate that we achieve very consistent results of the derived profiles of turbulent variables regardless of whether 1 or 10 s time resolutions are used. We also show that the temporal and spatial length scales of the fluctuations in vertical wind, moisture, and potential temperature are similar with a spatial integral scale of ≈160 m at least in the mixed layer (ML). The profiles of the molecular destruction rates show a maximum in the interfacial layer (IL) and reach values of ϵ m ≃ 7 x 10 -4 g 2 kg −2 s −1 for mixing ratio and ϵ θ ≃ 1.6 x 10 -3 K 2 s −1 for potential temperature. In contrast, the maximum of the TKE dissipation is reached in the ML and amounts to ≃ 10 -2 m 2 s −3 . We also demonstrate that the vertical wind ACF coefficient k w ∝ $\overline{w'^{2}}$ and the TKE dissipation ϵ ∝ ($\overline{w'^{2}}$) 3/2 . For the molecular destruction rates, we show that ϵ m ∝ $\overline{m'^{2}}$ ($\overline{w'^{2}}$) 1/2 and ϵ θ ∝ $\overline{θ'^{2}}$ ($\overline{w'^{2}}$) 1/2 . These equations can be used for parameterizations of ϵ, ϵ m , and ϵ θ . All noise error bars are derived by error propagation and are small enough to compare the results with previous observations and large-eddy simulations. The results agree well with previous observations but show more detailed structures in the IL. Consequently, the synergy resulting from this new combination of active remote sensors enables the profiling of turbulent variables such as integral scales, variances, TKE dissipation, and the molecular destruction rates as well as deriving relationships between them. The results can be used for the parameterization of turbulent variables, TKE budget analyses, and the verification of large-eddy simulations.

dissipation rate↗