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NASA NTRS2012

Stochastic Convection Parameterizations

computational fluid dynamics, radiation, clouds, turbulence, convection, gravity waves, surface interaction, radiation interaction, cloud and aerosol microphysics, complexity (vegetation, biogeochemistry, radiation versus turbulence/convection stochastic approach, non-linearities, Monte Carlo, high resolutions, large-Eddy Simulations, cloud structure, plumes, saturation in tropics, forecasting, parameterizations, stochastic, radiation-clod interaction, hurricane forecasts

atmospheric models↗

DOE OSTI2024

A Solution Method for the Filtered Lifting Line Theory

The filtered lifting line theory presents a continuous form of the inviscid momentum equations of flow over a lifting device, such as a wing or rotor blade, using body forces without mathematical singularities. This theory is also consistent with an actuator line representation of a lifting device. In this work, we present a reformulation of the equations in terms of the local flow angle along the line, which allows solving the stand-alone equations using multivariate root-finding algorithms. This approach can be used to obtain a fast, computationally inexpensive solution of the loading distribution along a wing without the need to perform computational fluid dynamic simulations. We study the requirements in terms of resolution in the spanwise direction and establish the criteria for spacing and minimum amount of points required along the blade to obtain converged solutions. The solutions are compared to results from large-eddy simulations, and we observed excellent agreement with less than a percent difference in quantities along the blade between the methods.

17 WIND ENERGY↗

DOE OSTI2018

Data-Driven Machine Learning for Wind Plant Flow Modeling

In this study, we introduce a data-driven machine learning framework for improving the accuracy of wind plant flow models by learning turbulence model corrections based on data from higher-fidelity simulations. First, a high-dimensional PDE-constrained optimization problem is solved using gradient-based optimization with adjoints to determine optimal eddy viscosity fields that improve the agreement of a medium-fidelity Reynolds-Averaged Navier Stokes (RANS) model with large eddy simulations (LES). A supervised learning problem is then constructed to find general, predictive representations of the optimal turbulence closure. A machine learning technique using Gaussian process regression is trained to predict the eddy viscosity field based on local RANS flow field information like velocities, pressures, and their gradients. The Gaussian process is trained on LES simulations of a single turbine and implemented in a wind plant simulation with 36 turbines. We show improvement over the baseline RANS model with the machine learning correction, and demonstrate the ability to provide accurate confidence levels for the corrections that enable future uncertainty quantification studies.

17 WIND ENERGY↗

DOE OSTI2026

A Multiscale Simulation Study of the Effects of Nantucket Island on Atmospheric Boundary Layer Flow

Multiscale atmospheric simulations were conducted to investigate how Nantucket Island modifies the marine atmospheric boundary layer (ABL) flow, how the mechanisms responsible are represented in mesoscale versus large-eddy simulation (LES) domains, and potential impacts on wind energy production downstream, in moderate and high wind speed cases. Here, to isolate the effects of the island, each case study was simulated (i) with and without island topography included and (ii) with observed versus spatially uniform sea surface temperature (SST) values, the latter removing the influence of spatial variability in SST, while also enabling the examination of how the island effects vary with near-surface thermodynamic stability over the surrounding ocean. Island effects were found to be strongest during daytime heating, which generates convective rolls that redistribute momentum vertically over the island and downstream. Colder SSTs and weaker winds strengthen these rolls and extend their influence. Flow statistics within a representative wind turbine rotor-swept area downstream show that while vertical momentum redistribution enhances near-surface wind speeds, those aloft decrease, resulting in minimal net change to rotor-equivalent wind speed and slightly reduced vertical shear. Comparisons of mesoscale and LES domains reveal that both capture the roughness-driven near-surface slowdown, but mesoscale models underestimate vertical momentum transport compared to LES, which explicitly resolves convective rolls. Increasing LES resolution beyond the grid spacing required to begin capturing the largest convective roll structures ∼ O(100) m yields minimal additional momentum transport but increases resolved turbulence kinetic energy, relevant for stress loading.

Atmosphere-land interaction↗

NASA NTRS1993

Stratified turbulence in the atmosphere and oceans - A new subgrid model

Turbulence in stably and unstably stratified media is studied, and the results are used to construct a subgrid simulation (SGS) model for use in large eddy simulation (LES). It was found that, although the assumption of inertiality of the subgrid scales is reasonable for the case of unstable stratification, it ceases to be so in the case of stable stratification, where gravity removes kinetic energy from the eddies: the generation of gravity waves becomes the dominant physical process, with dissipation relegated to higher wavenumbers. Preliminary results show that the total kinetic energy dissipation length scale increases with stability, in accordance with LES results but in disagreement with Deardorff's model that suggests a decrease of all dissipation scales in presence of stratification.

Canuto, V. M.↗

NASA NTRS1994

Simulation of Unsteady Combustion in a Ramjet Engine Using a Highly Parallel Computer

Combustion instability in ramjets is a complex phenomenon that involve nonlinear interaction between acoustic waves, vortex motion and unsteady heat release in the combustor. To numerically simulate this 3-D, transient phenomenon, enormous computer resources (time, memory and disk storage) are required. Although current generation vector supercomputers are capable of providing adequate resources for simulations of this nature, their high cost and limited availability, makes such machines less than satisfactory for routine use. The primary focus of this study is to assess the feasibility of using highly parallel computer systems as a cost-effective alternative for conducting such unsteady flow simulations. Towards this end, a large-eddy simulation model for combustion instability was implemented on the Intel iPSC/860 and a careful study was conducted to determine the benefits and the problems associated with the use of such machines for transient simulations. Details of this study along with the results obtained from the unsteady combustion simulations carried out on the iPSC/860 are discussed in this paper.

Menon, Suresh↗

NASA NTRS2021

High-Fidelity Simulation of Turbulent Flow Past a Gaussian Bump

A spanwise-periodic computation of a turbulent flow past a Gaussian bump is performed in the form of a hybrid direct numerical simulation and wall-resolved large-eddy simulation. A fourth-order spatially-accurate flow solver is employed to perform the simulation, using 10.2 billion grid points for a Reynolds number of 170000 based on the bump height. The key findings from the simulation are reported in the acceleration and deceleration flow regions associated with the bump shape. Significant anisotropy in the normal Reynolds stresses, along both the wall-normal and streamwise directions, is observed within the acceleration region. The ratio between the Reynolds shear stress and turbulent kinetic energy in that region also experiences significant deviations from the norms of a zero pressure gradient turbulent boundary layer. The chosen Reynolds number generates strong flow separation in the adverse pressure gradient region, which is in contrast with a previous simulation at half the Reynolds number that only indicated incipient separation. An internal layer generated in the acceleration region evolves into a free shear layer that develops in the deceleration region and separates. Proper modeling of this inner layer appears crucial to predict the flow separation. Surface curvature effects on the attached flow development are also discussed.

Turbulent Boundary Layer↗

DOE OSTI2024

Impact of Momentum Perturbation on Convective Boundary Layer Turbulence

Mesoscale-to-microscale coupling is an important tool for conducting turbulence-resolving multiscale simulations of realistic atmospheric flows, which are crucial for applications ranging from wind energy to wildfire spread studies. Different techniques are used to facilitate the development of realistic turbulence in the large-eddy simulation (LES) domain while minimizing computational cost. Here, we explore the impact of a simple and computationally efficient Stochastic Cell Perturbation method using momentum perturbation (SCPM-M) to accelerate turbulence generation in boundary-coupled LES simulations using the Weather Research and Forecasting model. We simulate a convective boundary layer (CBL) to characterize the production and dissipation of turbulent kinetic energy (TKE) and the variation of TKE budget terms. Furthermore, we evaluate the impact of applying momentum perturbations of three magnitudes below, up to, and above the CBL on the TKE budget terms. Momentum perturbations greatly reduce the fetch associated with turbulence generation. When applied to half the vertical extent of the boundary layer, momentum perturbations produce an adequate amount of turbulence. However, when applied above the CBL, additional structures are generated at the top of the CBL, near the inversion layer. The magnitudes of the TKE budgets produced by SCPM-M when applied at varying heights and with different perturbation amplitudes are always higher near the surface and inversion layer than those produced by No-SCPM, as are their contributions to the TKE. This study provides a better understanding of how SCPM-M reduces computational costs and how different budget terms contribute to TKE in a boundary-coupled LES simulation.

17 WIND ENERGY↗

DOE OSTI2022

Impact of Cloud-Base Turbulence on CCN Activation: Single-Size CCN

Abstract This paper examines the impact of cloud-base turbulence on activation of cloud condensation nuclei (CCN). Following our previous studies, we contrast activation within a nonturbulent adiabatic parcel and an adiabatic parcel filled with turbulence. The latter is simulated by applying a forced implicit large-eddy simulation within a triply periodic computational domain of 64 3 m 3 . We consider two monodisperse CCN. Small CCN have a dry radius of 0.01 μ m and a corresponding activation (critical) radius and critical supersaturation of 0.6 μ m and 1.3%, respectively. Large CCN have a dry radius of 0.2 μ m and feature activation radius of 5.4 μ m and critical supersaturation 0.15%. CCN are assumed in 200-cm −3 concentration in all cases. Mean cloud-base updraft velocities of 0.33, 1, and 3 m s −1 are considered. In the nonturbulent parcel, all CCN are activated and lead to a monodisperse droplet size distribution above the cloud base, with practically the same droplet size in all simulations. In contrast, turbulence can lead to activation of only a fraction of all CCN with a nonzero spectral width above the cloud base, of the order of 1 μ m, especially in the case of small CCN and weak mean cloud-base ascent. We compare our results to studies of the turbulent single-size CCN activation in the Pi chamber. Sensitivity simulations that apply a smaller turbulence intensity, smaller computational domain, and modified initial conditions document the impact of specific modeling assumptions. The simulations call for a more realistic high-resolution modeling of turbulent cloud-base activation.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2019

Scale-Adaptive Simulation of Transient Two-Phase Flow in Continuous-Casting Mold

Scale-adaptive simulation (SAS) of the transient gas–liquid two-phase flow in a laboratory-scale continuous-casting mold is presented. The main objective is to investigate the applicability of the scale-adaptive unsteady Reynolds-averaged Navier–Stokes turbulent model (URANS SAS) for predicting the transient multiscale turbulent structures in a two-phase flow. Good quantitative agreements with the experimental data and the large eddy simulation (LES) results are obtained both for the time-averaged velocity field and for the transient turbulent characteristics. The introduction of the von Karman length-scale into the turbulence-scale equation allows the SAS model to dynamically adjust to the resolved turbulent structures. The LES-like pulsating behavior of the air gas and the large-scale liquid eddy magnitudes in the unsteady regions of flow field are captured by the SAS model. The classical − 5/3 law of power spectrum density (PSD) of the axial velocity is kept properly for the single-phase turbulent flow. For two-phase flow, the decay of PSD is too steep at the high-frequency region; the predicted PSD obtained with SAS is damped stronger than that estimated by LES. The SAS model offers an attractive alternative to the existing LES approach or to the other hybrid RANS/LES models for strongly unsteady flows.

36 MATERIALS SCIENCE↗

DOE OSTI2023

Assessing turbulence and mixing parameterizations in the gray-zone of multiscale simulations over mountainous terrain during the METEX21 field experiment

Multiscale numerical weather prediction models transition from mesoscale, where turbulence is fully parameterized, to microscale, where the majority of highly energetic scales of turbulence are resolved. The turbulence gray-zone is situated between these two regimes and multiscale models must downscale through these resolutions. Here, we compare three multiscale simulations which vary by the parameterization used for turbulence and mixing within the gray-zone. The three parameterizations analyzed are the Mellor-Yamada Nakanishi and Niino (MYNN) Level 2.5 planetary boundary layer scheme, the TKE-1.5 large eddy simulation (LES) closure scheme, and a recently developed three-dimensional planetary boundary layer scheme based on the Mellor-Yamada model. The simulation domain includes complex (i.e., mountainous) terrain in Nevada that was instrumented with meteorological towers, profiling and scanning lidars, a tethered balloon, and a surface flux tower. Simulations are compared to each other and to observations, with assessment of model skill at predicting wind speed, wind direction and TKE, and qualitative evaluations of transport and dispersion of smoke from controlled releases. This analysis demonstrates that microscale predictions of transport and dispersion can be significantly influenced by the choice of turbulence and mixing parameterization in the terra incognita , particularly over regions of complex terrain and with strong local forcing. This influence may not be apparent in the analysis of model skill, and motivates future field campaigns involving controlled tracer releases and corresponding modeling studies of the turbulence gray-zone.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2025

Advancements on Multi-Fidelity Random Fourier Neural Networks: Application to Hurricane Modeling for Wind Energy

Multi-fidelity approaches are emerging as effective strategies in computational science to handle otherwise intractable tasks like Uncertainty Quantification (UQ), training of Machine Learning (ML) models, and optimization, for expensive high-fidelity applications in which the amount of available simulations or data is limited. The main idea is simple: large datasets generated for low-fidelity approximations of the problem at hand are fused with a much sparser dataset for the target (high-fidelity) system. In this paper, we build on our recent success in designing random Fourier Neural Networks (rFNNs) [1] to target problems arising in wind energy applications and in particular problems of interest for hurricane modeling. In this context, data for the high-fidelity models are limited and lower fidelity alternatives are needed. In this work, we introduce a novel multi-fidelity training approach for our rFNNs and demonstrate its use on a simple verification problem and on a hurricane modeling problem in which high-fidelity data are generated via Large-Eddy Simulations (LES), while low-fidelity data are given by a mesoscale model. Initial results demonstrate how the multi-fidelity training approach can improve the quality of the resulting surrogate.

Fourier Neural Networks↗

NASA NTRS2003

Numerical Simulations of Wake/Boundary Layer Interactions

Direct and large-eddy simulations of the interaction between the wake of a circular cylinder and a flat-plate boundary layer are conducted. Two Reynolds numbers are examined. The simulations indicate that at the lower Reynolds number the boundary layer is buffeted by the unsteady Karman vortex street shed by the cylinder. The fluctuations, however, cannot be self-sustained due to the low Reynolds-number, and the flow does not reach a turbulent state within the computational domain. In contrast, in the higher Reynolds-number case, boundary-layer fluctuations persist after the wake has decayed (due, in part, to the higher values of the local Reynolds number Re(sub theta) achieved in this case); some evidence could be observed that a self-sustaining turbulence generation cycle was beginning to be established.

Piomelli, Ugo↗

DOE OSTI2026

Assessing the Impact of a Forest Canopy on Near-Surface Wind Statistics

Representing the forest canopy in atmospheric numerical models should improve simulated winds within and above the canopy up to a few hundred meters above the ground. Here, in this study, we implement a forest canopy parameterization into the Weather Research and Forecasting (WRF) Model in a large-eddy simulation (LES) mode by applying drag forces across multiple layers within the canopy height. We use unique observations from the Lidar Experiments for Assessing Flow over Forests (LEAFF) field campaign at the Wind River Experimental Forest (WREF) in the U.S. Pacific Northwest to evaluate model performance. In a 2-day case study, the canopy parameterization improved wind predictions both within and above the canopy, particularly during the daytime and at finer grid resolution. Without it, winds were frequently overpredicted above the canopy. Similarly, derived quantities such as the wind shear index also yielded estimates closer to observations with the canopy parameterization implemented. These findings suggest that representing the canopy using drag forces alone can improve simulated mean winds up to 200 m above the surface. Furthermore, second-order statistical moments of wind were more sensitive to canopy density than first-order moments, especially during the daytime. This increased sensitivity and the improved daytime performance in wind speed—evidenced by the lowest bias from observations (3% compared to 20% over diurnal cycle)—imply that winds above the canopy layer are strongly influenced by how well turbulence above the canopy is modeled. The results of this study can serve as a foundation for parameterizing forest canopy effects in coarser weather forecast models.

Energy - Wind↗

DOE OSTI2022

Supersaturation Variability from Scalar Mixing: Evaluation of a New Subgrid-Scale Model Using Direct Numerical Simulations of Turbulent Rayleigh–Bénard Convection

We report that supersaturation fluctuations in the atmosphere are critical for cloud processes. A nonlinear dependence on two scalars - water vapor and temperature - leads to different behavior than single scalars in turbulent convection. For modeling such multiscalar processes at subgrid scales (SGS) in large-eddy simulations (LES) or convection-permitting models, a new SGS scheme is implemented in CM1 that solves equations for SGS water vapor and temperature fluctuations and their covariance. The SGS model is evaluated using benchmark direct-numerical simulations (DNS) of turbulent Rayleigh–Bénard convection with water vapor as in the Michigan Tech Pi Cloud Chamber. This idealized setup allows thorough evaluation of the SGS model without complications from other atmospheric processes. DNS results compare favorably with measurements from the chamber. Results from LES using the new SGS model compare well with DNS, including profiles of water vapor and temperature variances, their covariance, and supersaturation variance. SGS supersaturation fluctuations scale appropriately with changes to the LES grid spacing, with the magnitude of SGS fluctuations decreasing relative to those at the resolved scale as the grid spacing is decreased. Sensitivities of covariance and supersaturation statistics to changes in water vapor flux relative to thermal flux are also investigated by modifying the sidewall conditions. Relative changes in water vapor flux substantially decrease the covariance and increase supersaturation fluctuations even away from boundaries.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2025

Tropical Cyclone Super Resolution using conditional diffusion denoising probabilistic model from mesoscale simulation to LES

Accurate modeling of tropical cyclone wind fields is essential for the design, risk assessment, and operational planning of offshore energy infrastructure. While mesoscale simulations are widely used thanks to their computational efficiency, they lack the necessary resolution to capture key features such as wind shear and veer profiles as well as the distribution turbulent kinetic energy (TKE). High-fidelity large-eddy simulation (LES) models on the other hand, can resolve turbulent structures and provide a more accurate representation of the complex wind field, albeit at a higher computational cost. To address this modeling gap, we introduce a two-part generative framework to enhance the resolution and physics-capturing ability of mesoscale simulations. First, a reduced-order model based on Karhunen–Loève (KL) decomposition is used to extract dominant spatial modes from one-dimensional mean wind profiles. A multilayer perceptron (MLP) is trained to map mesoscale mode weights to their LES counterparts, enabling accurate reconstruction of vertical velocity profiles. Second, a conditional Diffusion Denoising Probabilistic Model (DDPM) is developed to super-resolve coarse and low-fidelity mesoscale velocity fields, recovering fine-scale turbulence structures and stress distributions. The framework is evaluated across different tropical cyclone intensity categories defined by the Saffir–Simpson scale and demonstrates strong performance in both interpolation and extrapolation tasks. The generated fields accurately reproduce spatial coherence, stress distributions, and spectral energy characteristics observed in LES data. By bridging the fidelity gap between mesoscale and LES outputs, this approach offers a scalable, data-driven solution for enhancing the representation of tropical cyclone wind fields, enabling more robust offshore energy infrastructure systems design in tropical-cyclone-prone areas.

17 WIND ENERGY↗

NASA NTRS1989

Numerical simulation of pressure oscillations in a ramjet combustor

Large-eddy simulations of compressible cold flow in a ramjet combustor configuration were performed. The objectives were to investigate the mechanisms for vortex-acoustic interaction in such a device and to develop a simulation method that can be extended to cases with combustion heat release to study combustion instability. From the simulation, it was found that the separated shear layer rolls up into concentrated vortices that merge to form large coherent structures. These vortices interact with the choked nozzle downstream to produce an axial acoustic dipole. The spectrum of the pressure fluctuation at the base of the backward-facing step shows that there are two types of oscillations: an acoustic resonant mode and a vortex-acoustic coupled mode. Based on the flow physics observed in the simulations, a simple one-dimensional model for the vortex-acoustic coupled mode was proposed. The eigenvalue problem based on this model was solved to obtain the frequency of the coupled mode.

Jou, Wen-Huei↗

DOE OSTI2024

Influences of lidar scanning parameters on wind turbine wake retrievals in complex terrain

Abstract. Scanning lidars enable the collection of spatially distributed measurements of turbine wakes and the estimation of wake properties such as magnitude, extent, and trajectory. Lidar-based characterizations, however, may be subject to distortions due to the observational system. Distortions can arise from the resolution of the measurement points across the wake, the projection of the winds onto the beam, averaging along the beam probe volume, and intervening evolution of the flow over the scan duration. Using a large-eddy simulation and simulated measurements with a virtual lidar model, we assess how scanning lidar systems may influence the properties of the retrieved wake using a case study from the Perdigão campaign. We consider three lidars performing range-height indicator sweeps in complex terrain, based on the deployments of lidars from the Danish Technical University (DTU) and German Aerospace Center (DLR) at the Perdigão site. The unwaked flow, measured by the DTU lidar, is well-captured by the lidar, even without combining data into a multi-lidar retrieval. The two DLR lidars measure a waked transect from different downwind vantage points. In the region of the wake, the observation system reacts to the smaller spatial and temporal variations of the winds, allowing more significant observation distortions to arise. While the measurements largely capture the wake structure and trajectory over its 4–5 D extent, limited spatial resolution of measurement points and volume averaging lead to a quicker loss of the two lobes in the near wake, smearing of the vertical bounds of the wake (< 30 m), wake center displacements up to 10 m, and dampening of the maximum velocity deficit by up to a third. The virtual lidar tool, coupled with simulations, provides a means for assessing measurement capabilities in advance of measurement campaigns.

17 WIND ENERGY↗