Atmospheric Turbulent Flow Research Articles

Statistical Dynamics and Subgrid Modelling of Turbulence: From Isotropic to Inhomogeneous

Turbulence is the most important, ubiquitous, and difficult problem of classical physics. Feynman viewed it as essentially unsolved, without a rigorous mathematical basis to describe the statistical dynamics of this most complex of fluid motion. However, the paradigm shift came in 1959, with the formulation of the Eulerian direct interaction approximation (DIA) closure by Kraichnan. It was based on renormalized perturbation theory, like quantum electrodynamics, and is a bare vertex theory that is manifestly realizable. Here, we review some of the subsequent exciting achievements in closure theory and subgrid modelling. We also document in some detail the progress that has been made in extending statistical dynamical turbulence theory to the real world of interactions with mean flows, waves and inhomogeneities such as topography. This includes numerically efficient inhomogeneous closures, like the realizable quasi-diagonal direct interaction approximation (QDIA), and even more efficient Markovian Inhomogeneous Closures (MICs). Recent developments include the formulation and testing of an eddy-damped Markovian anisotropic closure (EDMAC) that is realizable in interactions with transient waves but is as efficient as the eddy-damped quasi-normal Markovian (EDQNM). As a similarly efficient closure, the realizable eddy-damped Markovian inhomogeneous closure (EDMIC) has been developed. Moreover, we present subgrid models that cater for the complex interactions that occur in geophysical flows. Recent progress includes the determination of complete sets of subgrid terms for skilful large-eddy simulations of baroclinic inhomogeneous turbulent atmospheric and oceanic flows interacting with Rossby waves and topography. The success of these inhomogeneous closures has also led to further applications in data assimilation and ensemble prediction and generalization to quantum fields.

The Influence of Gravity and Mass Loading on the Coalescence of Aerodynamically Interacting Droplets in Homogeneous Isotropic Turbulence

Abstract The collision–coalescence of cloud droplets in atmospheric turbulent flow is analyzed numerically using direct numerical simulation coupled to a Lagrangian particle tracking. The droplet aerodynamic interactions (AI) are represented for employing two complementary approaches. For large separations, the interaction forces are evaluated by the superposition of Stokes disturbance velocities generated by moving particles. When the distance between droplets is comparable to their mean radii, lubrication forces are additionally considered. Simulation results show that without gravitational acceleration, aerodynamic interactions decrease the kinetics of the coalescence process but do not significantly impact the size spectrum broadening. The influence of AI on the coalescence kinetics is more complex in the presence of gravity and depends on the mass loading and on droplet inertia. Long-range aerodynamic interactions reduce the coalescences in dilute suspensions but increase the collision rate in dense suspensions of high-inertia droplets. In contrast, lubrication forces decrease the collision rate regardless of the mass loading. The collision efficiency induced by aerodynamic interactions additionally is influenced by the radius ratio of colliding droplets and the mechanisms leading to raindrops formation and growth. In cloud-like conditions, both long- and short-range AI decrease the fraction of raindrops created by collisions between droplets (autoconversion) while promoting raindrops growth by accretion (collection by settling drops). In turn, aerodynamic interactions favor the growth of a limited number of droplets and promote the broadening of the droplet size spectrum. This effect is stronger in dilute suspensions of weakly inertial droplets, corresponding to the flow properties encountered in developing precipitation.

Segregation of Fast-Reactive Species in Atmospheric Turbulent Flow

Atmospheric turbulence, which produces chaotic motions in the planetary boundary layer, can inhibit mixing between fast-reacting species produced or released at different locations. This segregation process modifies the effective rate at which reactions occur between these species and is not appropriately accounted for in coarse-resolution models, since these models assume complete mixing of tracers within each grid box. Here, we present a few examples of large-eddy simulations (LES) applied to chemically reactive species in a forested area with high emissions of biogenic hydrocarbons, an urban area rich in anthropogenic emissions, and a maritime area with high emissions of reduced sulfur species.

Toward prediction of turbulent atmospheric flows over propagating oceanic waves via machine-learning augmented large-eddy simulation

Wind-wave interactions have important effects on the energy harvesting of offshore wind farms. High-fidelity large-eddy simulation (LES) is a powerful approach for investigating wind-wave interactions in turbulent oceanic environments. Due to the large scale of the flow domain and the high grid resolution required to resolve multi-scale flow motions, however, brute-force LES of wind-wave interactions is computationally very expensive. We propose augmenting brute-force LES via machine-learning data-driven modeling (ML-LES) to dramatically reduce the computational time required to obtain converged turbulence statistics when the brute-force approach is employed. Namely, we employ a convolutional neural network (CNN) autoencoder trained and validated with LES data sets to develop a highly efficient ML-LES approach for computing turbulence statistics from just a few snapshots of instantaneous LES flow fields. Our results demonstrate the accuracy and efficiency of ML-LES in predicting the mean velocity, velocity fluctuations, and turbulence kinetic energy in highly stretched computational grid systems required to carry out simulations in real-life oceanic environments.

Reduced-order modeling for parameterized large-eddy simulations of atmospheric pollutant dispersion

Mapping near-field pollutant concentration is essential to track plume dispersion in urban areas. By solving most of the turbulence spectrum, large-eddy simulations (LES) can accurately represent concentration spatial variability. Synthesizing this large amount of information to improve the accuracy of lower-fidelity operational models is particularly appealing. Due to the computational cost of LES, it becomes a challenge in multi-query contexts, to quantify how tracer dispersion changes with atmospheric and source parametric variations. We propose a data-driven reduced-order model (ROM) combining proper orthogonal decomposition (POD) and probabilistic Gaussian process regression (GPR) to efficiently predict tracer concentration field first- and second-order statistics. The novelty of this work lies in the original approach used to efficiently tune the GPR based on the available reduced-basis information and properties. In practice, the GPR hyperparameters are optimized through the hierarchy of POD modes thanks to Bayesian optimization informed by POD. The robustness of the approach is also addressed under small data constraint. A detailed analysis of our model performance is carried out in the case of a turbulent atmospheric boundary-layer flow over a surface-mounted obstacle for which the convective character of the system together with the uncertainty of the pollutant source location make it very challenging for ROM. The model is able to capture the wide range of spatial scales across the POD modes. The ROM accuracy remains acceptable for a minimal budget of about one hundred LES snapshots, providing a first estimation moving towards more realistic atmospheric dispersion applications.

Development and characterization of a passive, bio-inspired flow-tracking sensor

The effective natural transport of seeds in turbulent atmospheric flows is found across a myriad of shapes and sizes. However, to develop a sensitive passive sensor required for large-scale (in situ) flow tracking measurements, systems suffer from inertial lag due to the increased size and mass needed for optical visibility, or by carrying a sensor payload, such as an inertial measurement unit (IMU). While IMU-based flow sensing is promising for beyond visual line-of-sight applications, the size and mass of the sensor platform results in reduced flow fidelity and, hence, measurement error. Thus, to extract otherwise inaccessible flow information, a flow-physics-based tracer correction is developed through the application of a low-order unsteady aerodynamic model, inspired by the added-mass concept. The technique is evaluated using a sensor equipped with an IMU and magnetometer. A spherical sensor platform, selected for its symmetric geometry, was subject to two canonical test cases including an axial gust as well as the vortex shedding generated behind a cylinder. Using the measured sensor velocity and acceleration as inputs, an energized-mass-based dynamic model is used to back-calculate the instantaneous flow velocity from the sensor measurements. The sensor is also tracked optically via a high-speed camera while collecting the inertial data onboard. For the 1D test case (axial gust), the true (local) wind speed was estimated from the energized-mass-based model and validated against particle image velocimetry measurements, exhibiting good agreement with a maximum error of 10%. For the cylinder wake (second test case), the model-based correction enabled the extraction of the velocity oscillation amplitude and vortex-shedding frequency, which would have otherwise been inaccessible. The results of this study suggest that inertial (i.e. large and heavy) IMU-based flow sensors are viable for the extraction of Lagrangian tracking at large atmospheric scales and within highly-transient (turbulent) environments when coupled with a robust dynamic model for inertial correction.

Quasi-Steady Simulation of Glaze Ice Accretion and Heat Transfer in the Supercooled Large Droplet Regime in Atmospheric Turbulent Air Flow

Abstract To avoid the use of a computationally intensive full unsteady simulation while providing an accurate solution, a quasi-steady simulation has been performed to study glaze and mixed ice accretion in the supercooled large droplet (SLD) regime in turbulent flow of atmospheric air. We have attempted to find the minimum time-step necessary to adequately simulate the icing process on an airfoil surface. Based on node displacement, a mesh morphing scheme has been adopted in the computations to account for the moving boundaries that are caused by the continuous ice buildup. We have modeled ice accretion on an airfoil surface for a time period of 232 s using several time steps. At each time-step we solved the steady-state conservation equations for the air and droplet phases and then used the results as initial conditions for the next time position. The magnitude of the time-step ranged from a least accurate two-shot simulation (where the time-step is 116 s) to a most accurate 2320-shot simulation (where the time-step is 0.1 s). In-between these two extreme time steps, we have performed a three-shot simulation (where the time-step is 77.33 s), a four-shot simulation (where the time-step is 58 s), a six-shot simulation (where the time-step is 38.67 s), a 46-shot simulation (where the time-step is 5 s), and a 232-shot simulation (where the time-step is 1 s). We have done so to find out the degree of accuracy (or inaccuracy) of the multishot simulation approach and to find out the appropriate time-step needed for a successful and valid quasi-steady simulation. A valid quasi-steady simulation needs to use a time-step that is small enough to reproduce the full time-dependent solution within a very small error band. We have found that both the 1 s and the 0.1 s time steps produce virtually identical results. This is the primary litmus test that proves the validity of the quasi-steady-state assumption. The results adopted in this paper are thus all based on the more conservative 0.1 s time-step. In the process of performing the simulations, remeshing was required in order to maintain the grid density in zones of high curvature to be able to capture the full physics in those zones. After successfully modeling glaze ice accretion over the airfoil surface using the 0.1 s quasi-steady simulation approach, the effects of supercooled large droplets (SLDs) impacting the surface have been examined and presented in terms of the variation of the local collection efficiency, the water film thickness, and the heat transfer rate. Examination of the variation of the angle that the ice horn makes with the airfoil chord line demonstrated a 20% improvement in angle prediction when the time-step is reduced from 116 s to 0.1 s. The analysis also reveals a 12% or 8.5% increase in the maximum collection efficiency, βmax, depending on whether the value of the liquid water content (LWC) has been doubled from 0.5 g/m3 to 1 g/m3, or the value of the freestream velocity has been doubled from 75 m/s to 150 m/s, respectively. Because of the need to monitor the local collection efficiency and convective heat fluxes at each shot (regardless of the number of shots employed), the approach adopted here was found to be effective in successively and successfully reproducing the curvature of the glaze ice horn.

Atmospheric turbulence and wildland fires: a review

The behaviour of wildland fires and the dispersion of smoke from those fires can be strongly influenced by atmospheric turbulent flow. The science to support that assertion has developed and evolved over the past 100+ years, with contributions from laboratory and field observations, as well as modelling experiments. This paper provides a synthesis of the key laboratory- and field-based observational studies focused on wildland fire and atmospheric turbulence connections that have been conducted from the early 1900s through 2021. Included in the synthesis are reports of anecdotal turbulence observations, direct measurements of ambient and fire-induced turbulent flow in laboratory and wildland environments, and remote sensing measurements of fire-induced turbulent plume dynamics. Although considerable progress has been made in advancing our understanding of the connections between atmospheric turbulence and wildland fire behaviour and smoke dispersion, gaps in that understanding still exist and are discussed to conclude the synthesis.

Interactive Visualization of Large Turbulent Flow as a Cloud Service

Many scientific communities today have community datasets that are continuously created, curated, and maintained for community use. Such datasets are often hosted and shared through cloud-based data repositories. In this work, we propose a lightweight and affordable visualization cloud service that can be deployed as a companion service of a community dataset. Our target visualization use case is parallel flow visualization, which is crucial for understanding planet-scale phenomena such as the Earth’s atmosphere and ocean. As a core research topic of scientific visualization, parallel flow visualization typically uses HPC computing platforms. It is complex to implement with scalability, deploy with efficiency, and is often considered an advanced form of scientific visualization. Because of the heterogeneous nature of cloud platforms, in this work, we use a swarm-based parallel design to replace traditional HPC designs that assume homogeneity and rely upon conventional methods such as Message Passing Interface (MPI). This design enables interactive visualization of large flow fields in a way that is lightweight, efficient and easily deployable as a cloud service. We demonstrate our proposed system using NOAA’s NCEP ensemble data, which captures turbulent planet-scale atmospheric flows in observed forms, as well as in forecast forms for varying time scales. We evaluate the performance and efficacies of our system on Amazon Web Services (AWS) for three use cases, where remote users can use their laptops to (i) interactively explore global atmospheric flow patterns in general, (ii) to specifically compare how a forecast is different from the observation, and (iii) to explore flow patterns in a typical information visualization dashboard.

Superstatistical Wind Fields from Pointwise Atmospheric Turbulence Measurements

Accurate models of turbulent wind fields have become increasingly important in the atmospheric sciences, e.g., for the determination of spatiotemporal correlations in wind parks, the estimation of individual loads on turbine rotor and blades, or the modeling of particle-turbulence interaction in atmospheric clouds or pollutant distributions in urban settings. Because of the difficult task of resolving the fields across a broad range of scales, one oftentimes has to invoke stochastic wind field models that fulfill specific, empirically observed, properties. Whereas commonly used Gaussian random field models solely control second-order statistics (i.e., velocity correlation tensors or kinetic energy spectra), we explicitly show that our extended model emulates the effects of higher-order statistics as well. Most importantly, the empirically observed phenomenon of small-scale intermittency, which can be regarded as one of the key features of atmospheric turbulent flows, is reproduced with a very high level of accuracy and at considerably low computational cost. Our method is based on a multipoint statistical description of turbulent velocity fields that consists of a superposition of multivariate Gaussian statistics with fluctuating covariances. We propose a new and efficient sampling algorithm for this Gaussian scale mixture and demonstrate how such “superstatistical” wind fields can be constrained on a certain number of real-world measurement data points from a meteorological mast array.Received 12 April 2022Revised 26 July 2022Accepted 22 August 2022DOI:https://doi.org/10.1103/PRXEnergy.1.023006Published by the American Physical Society under the terms of the Creative Commons Attribution 4.0 International license. Further distribution of this work must maintain attribution to the author(s) and the published article's title, journal citation, and DOI.Published by the American Physical SocietyPhysics Subject Headings (PhySH)Research AreasAtmospheric scienceBoundary layersShear flowsStochastic processesStructure & turbulence of boundary layersSustainabilityTurbulenceWind energyEnergy Science & TechnologyNonlinear DynamicsFluid DynamicsStatistical Physics

Applications of wide-ranging PIV measurements for various turbulent statistics in artificial atmospheric turbulent flow in a wind tunnel

Particle image velocimetry (PIV) is currently the only method which can experimentally quantify the spatial distributions of velocity with high sampling frequencies without causing intrusions into the airflow of the measuring devices. To extend the use of PIV systems for determining the distributions of velocity for wind environmental and engineering purposes, this study showed various applications of wide-ranging PIV systems for an artificially created turbulent flow at the wind-tunnel scale. Various turbulent statistics, which are commonly employed for evaluating the wind structures and environments, were compared among three conditions of the measurement areas covered by PIV systems and a hot-wire anemometry (HWA). The largest measurement covering 500 × 500 mm2 can capture the turbulent motions extending to 40% of the entire boundary layer. In addition, the discrepancies on two-length scales could be discussed using spatial and temporal correlation functions owing to the wide-ranging PIV. Moreover, the limitations of the wide-ranging PIV systems were clarified. While the turbulent statistics compared among three PIV conditions and HWA demonstrated adequate agreement, the PIV with the largest measurement area failed to capture the small-scale turbulent structures due to the coarse grid resolutions. Moreover, the measurement resolutions of PIV systems demonstrated sensitivity in determining the spectrum and probability density functions, whereas a wide-ranging PIV system can cover wide-ranging measurement areas.

Evidence of Ocean Waves Signature in the Space–Time Turbulent Spectra of the Lower Marine Atmosphere Measured by a Scanning LiDAR

To achieve more accurate weather and climate forecasting, and propose efficient engineering solutions for exploiting offshore renewable energies, it is imperative to accurately describe the atmospheric turbulent flow in the offshore environment. The ocean’s dynamics raise specific challenges for the aforementioned applications, as they significantly alter the atmospheric flow through complex wind–wave interactions. These interactions are important in fairly common situations and notably in old-sea conditions, where ocean waves travel fast, under comparatively slow wind velocities. In the present study, a scanning LiDAR (sLiDAR) was deployed on the shore to study micro-scale wind–wave interactions by performing horizontal scans 18 m above the ocean, and as far as 2 km from the coast. In the proposed configuration, and in the test cases presented in old seas, the sLiDAR captures wave-induced disturbances propagating into the lower part of the marine atmospheric boundary layer. Based on measurements of high-resolution space–time maps of the Radial Wind Speed, an original two-dimensional spectral analysis of the space–time auto-correlation functions was performed. Unlike more conventional data-processing techniques, and as long as the waves travel sufficiently (∼twofold) faster than the mean wind at the measurement height, the upward transfer of motions from the waves to the wind can be clearly distinguished from the atmospheric turbulence in the wave-number–angular-frequency (k–w) turbulent spectra. These are the first space–time auto-correlation functions of the wind velocity fluctuations obtained at micro-scales above the ocean. The analyses demonstrate sLiDAR systems’ applicability in measuring k–w-dependent turbulent spectra in the coastal environment. The findings present new perspectives for the study of micro-scale wind–wave interactions.

Surrogate modelling of wind fields from point-wise atmospheric turbulence measurements

We present an advanced model for the generation of synthetic wind fields that can be understood as an extension of the well-known Mann model. In contrast to such Gaussian random field models which control second-order statistics (i.e., velocity correlation tensors or spectra), we demonstrate that our extended model incorporates the effects of higherorder statistics as well. In particular, the empirically observed phenomenon of small-scale intermittency, a key feature of atmospheric turbulent flows, can be reproduced with high accuracy and at considerably low computational cost. Our method is based on a recently developed multipoint statistical description of a turbulent velocity field [J. Friedrich et al., J. Phys. Complex. 2 045006 (2021)] and consists of a superposition of multivariate Gaussian statistics with fluctuating covariances. Furthermore, we explicitly show how such superstatistical Mann fields can be constraint on a certain number of point-wise measurement data. We give an outlook on the relevance of such surrogate wind fields in the context of fatigue loads on wind turbines.

Unique stochastic Lagrangian particle dispersion models for inhomogeneous turbulent flows

It is well established that Lagrangian particle dispersion models, for inhomogeneous turbulent flows, must satisfy the ‘well-mixed condition’ of Thomson (J. Fluid Mech., vol. 180, 1987, pp. 529–556) in order to produce physically reasonable results. In more than one dimension, however, the well-mixed condition is not sufficient to define the dispersion model uniquely. The non-uniqueness, which is related to the rotational degrees of freedom of particle trajectories, permits models with trajectory curvatures and velocity autocorrelation functions which are clearly unphysical. A spin condition is therefore introduced to constrain the models. It requires an ensemble of particles with fixed initial position and velocity to have, at short times, expected angular momentum, measured relative to the mean position and velocity of an ensemble of fluid particles with initially random velocity, equal to the relative angular momentum of the mean flow at the ensemble mean location. The resulting unique model is found explicitly for the canonical example of inhomogeneous Gaussian turbulence and is characterised by accelerations which are exponential in the particle velocity. A simpler unique model with a quadratic acceleration is obtained using a weaker version of the spin condition. Unlike previous models, the unique models defined by the spin condition lead to particles having the correct (ensemble mean) angular speed in a turbulent flow in solid-body rotation. The properties of the new models are discussed in the settings of a turbulent channel flow and an idealised turbulent atmospheric boundary-layer flow.

Spectral partition characteristics of wind turbine load response under different atmospheric stability

To study the unsteady wind turbine load response under the atmospheric boundary-layer turbulence, we use a measured spectral model from a field experiment to generate atmospheric turbulent flow under different stability and analyze the loads of a full-scale 5 MW wind turbine based on the blade element momentum theory. The results of the spectral analysis show that the measured spectral model has a −1 power law region with higher turbulent kinetic energy relative to other standard spectral models, which indicates an energy-dominant boundary-layer turbulence. By the spectral analysis of the wind-turbine loads, there are three critical frequency: energy-dominant frequency, decoupling frequency and wind-turbine rotation frequency. Below the energy-dominant frequency, power and thrust respond strongly to the energy-dominant boundary-layer turbulence. The modulation effect of the atmospheric boundary-layer turbulence on the unsteady wind-turbine response continues until the decoupling frequency. After that, the power and thrust are mainly influenced by the wind-turbine rotation frequency. The decoupling frequency is strongly correlated with the tip speed ratio, but is weakly influenced by the atmospheric stability. A mathematical model of decoupling frequency and tip speed ratio is also established, which can help to determine the range of turbulence scales that can influence the wind turbine performance.

Harnessing stratospheric diffusion barriers for enhanced climate geoengineering

Abstract. Stratospheric sulfate aerosol geoengineering is a proposed method to temporarily intervene in the climate system to increase the reflectance of shortwave radiation and reduce mean global temperature. In previous climate modeling studies, choosing injection locations for geoengineering aerosols has, thus far, only utilized the average dynamics of stratospheric wind fields instead of accounting for the essential role of time-varying material transport barriers in turbulent atmospheric flows. Here we conduct the first analysis of sulfate aerosol dispersion in the stratosphere, comparing what is now a standard fixed-injection scheme with time-varying injection locations that harness short-term stratospheric diffusion barriers. We show how diffusive transport barriers can quickly be identified, and we provide an automated injection location selection algorithm using short forecast and reanalysis data. Within the first 7 d days of transport, the dynamics-based approach is able to produce particle distributions with greater global coverage than fixed-site methods with fewer injections. Additionally, this enhanced dispersion slows aerosol microphysical growth and can reduce the effective radii of aerosols up to 200–300 d after injection. While the long-term dynamics of aerosol dispersion are accurately predicted with transport barriers calculated from short forecasts, the long-term influence on radiative forcing is more difficult to predict and warrants deeper investigation. Statistically significant changes in radiative forcing at timescales beyond the forecasting window showed mixed results, potentially increasing or decreasing forcing after 1 year when compared to fixed injections. We conclude that future feasibility studies of geoengineering should consider the cooling benefits possible by strategically injecting sulfate aerosols at optimized time-varying locations. Our method of utilizing time-varying attracting and repelling structures shows great promise for identifying optimal dispersion locations, and radiative forcing impacts can be improved by considering additional meteorological variables.

A Street Graph-Based Morphometric Characterization of Two Large Urban Areas

Urban microclimate modelling, both numerical and in the laboratory, has strong implications in many relevant health and life-style management issues e.g., in studies for assessment and forecast of air quality (for both outdoor and, as boundary conditions, indoor investigations), for thermometric trend analysis in urban zones, in cultural heritage preservation, etc. Moreover, the study of urban microclimate modelling is largely promoted and encouraged by international institutions for its implication in human health protection. In the present work, we propose and discuss an adaptive street graph-based method aimed at automatically computing the geometrical parameters adopted in atmospheric turbulent flow modelling. This method has been applied to two real cases, the Italian cities of Rome and Cagliari, and its results has been compared with the ones from traditional methods based on regular grids. Results show that the proposed method leads to a more accurate determination of the urban canyon parameters (Canyon Aspect Ratio and Building Aspect Ratio) and morphometric parameters (Planar Area Index and Frontal Area Index) compared to traditional regular grid-based methods, at least for the tested cases. Further investigations on a larger number of different urban contexts are planned to thoroughly test and validate the proposed algorithm.

Long-range Doppler lidar measurements of wind turbine wakes and their interaction with turbulent atmospheric boundary-layer flow at Perdigao 2017

As part of the Perdigão 2017 campaign, vertical RHI (range-height indicator) scans with long-range pulsed Doppler wind lidars were performed aligned with the main wind direction and a wind turbine (WT) located on a mountain ridge. The measurements are used to not only retrieve flow velocities, but also their variance and - by using the turbulent broadening of the Doppler spectrum - also turbulent kinetic energy (TKE) dissipation rate. The study shows that turbulence in the WT wake is dependent on the turbulence of the inflow, but also on atmospheric stability. In stable atmospheric conditions, wakes could be analyzed up to five rotor diameters downstream (D) and showed the maximum turbulence in the wake at 2-3 D, whereas in unstable conditions, the maximum was found at 2 D and the wake could not be detected further than 3 D. A clear dependency of wake turbulence enhancement on inflow turbulence intensity is found, which levels out to no further enhancement at turbulence intensities of 30%.

On the genesis and evolution of barchan dunes: Hydrodynamics

Barchans are an important signature of turbulent atmospheric and aquatic flows on parts of the Earth’s surface where the supply of sediments is scarce. Khosronejad and Sotiropoulos [“On the genesis and evolution of barchan dunes: Morphodynamics,” J. Fluid Mech. 815, 117–148 (2017)] demonstrated that high-fidelity hydro-morphodynamic large-eddy simulation (LES) can replicate sub-aqueous barchan fields exhibiting the geometric features and morphodynamic interactions observed in nature. Herein, we first validate the ability of our LES method to simulate mean flow and turbulence statistics over a frozen barchan dune created in a control laboratory experiment. We then analyze our high-fidelity LES datasets to elucidate the hydrodynamic phenomena that drive the previously identified morphodynamic interactions at various stages of subaqueous barchan dune evolution, from flatbed to quasi-equilibrium. We uncover and quantify the hydrodynamic mechanisms that cause (i) the initiation of crescent microfeatures on the initial flatbed; (ii) the development of three-dimensional barchan dunes; and (iii) several other phenomena that occur during the barchan field maturation, such as the birth of new small barchans and the exposure of the underlying bedrock in the wake of the barchans. Defining a dimensionless timescale, i.e., the saturation timescale, as a function of the saturation length scale, we attempt to categorize for the first time various temporal stages of barchan field development. We show that the barchan field has specific characteristics corresponding to each of the barchan stage developments in terms of both the flow field and the bed topology. Comparing the simulation results of transverse and barchan dunes, we illustrate that the two dune types have similar topographic and hydrodynamics characteristics until bedrock exposure occurs.

Adversarial super-resolution of climatological wind and solar data

Accurate and high-resolution data reflecting different climate scenarios are vital for policy makers when deciding on the development of future energy resources, electrical infrastructure, transportation networks, agriculture, and many other societally important systems. However, state-of-the-art long-term global climate simulations are unable to resolve the spatiotemporal characteristics necessary for resource assessment or operational planning. We introduce an adversarial deep learning approach to super resolve wind velocity and solar irradiance outputs from global climate models to scales sufficient for renewable energy resource assessment. Using adversarial training to improve the physical and perceptual performance of our networks, we demonstrate up to a [Formula: see text] resolution enhancement of wind and solar data. In validation studies, the inferred fields are robust to input noise, possess the correct small-scale properties of atmospheric turbulent flow and solar irradiance, and retain consistency at large scales with coarse data. An additional advantage of our fully convolutional architecture is that it allows for training on small domains and evaluation on arbitrarily-sized inputs, including global scale. We conclude with a super-resolution study of renewable energy resources based on climate scenario data from the Intergovernmental Panel on Climate Change's Fifth Assessment Report.