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Severe winds produced by thunderstorm downbursts pose a serious risk to the structural integrity of wind turbines. However, guidelines for wind turbine design (such as the International Electrotechnical Commission Standard, IEC 61400-1) do not describe the key physical characteristics of such events realistically. In this study, a large-eddy simulation model is employed to generate several idealized downburst events during contrasting atmospheric stability conditions that range from convective through neutral to stable. Wind and turbulence fields generated from this dataset are then used as inflow for a 5-MW land-based wind turbine model; associated turbine loads are estimated and compared for the different inflow conditions. We first discuss time-varying characteristics of the turbine-scale flow fields during the downbursts; next, we investigate the relationship between the velocity time series and turbine loads as well as the influence and effectiveness of turbine control systems (for blade pitch and nacelle yaw). Finally, a statistical analysis is conducted to assess the distinct influences of the contrasting stability regimes on extreme and fatigue loads on the wind turbine. 

Downburst events initialized at various hours during the evening transition (ET) period are simulated to determine the effects of ambient stability on the outflow of downburst winds. The simulations are performed using a pseudo-spectral large eddy simulation model at high resolution to capture both the large-scale flow and turbulence characteristics of downburst winds. First, a simulation of the ET is performed to generate realistic initial and boundary conditions for the subsequent downburst simulations. At each hour in the ET, an ensemble of downburst simulations is initialized separately from the ET simulation in which an elevated cooling source within the model domain generates negatively buoyant air to mimic downburst formation.

The simulations show that while the stability regime changes, the ensemble mean of the peak wind speed remains fairly constant (between 35 and 38 m s⁻¹) and occurs at the lowest model level for each simulation. However, there is a slight increase in intensity and decrease in the spread of the maximum outflow winds as stability increases as well as an increase in the duration over which these strongest winds persist. This appears to be due to the enhanced maintenance of the ring vortex that results from the low-level temperature inversion, increased ambient shear, and a lack of turbulence within the stable cases. Coherent turbulent kinetic energy and wavelet spectral analysis generally show increased energy in the convective cases and that energy increases across all scales as the downburst passes. 

Over the last few years, the concept of incorporating aerial vehicles into the urban environment for diverse purposes has attracted ample interest and investment. These purposes cover a broad spectrum of applications, from larger vehicles designed for passenger transport, to package delivery and inspection/surveillance missions performed by small unmanned drones. While these Advanced Air Mobility (AAM) operations have the potential to alleviate bottlenecks arising from saturated surface transportation networks, there are a number of challenges that need to be addressed to make these operations safe and viable. One challenge is predicting weather effects within the urban environment with the required level of spatiotemporal fidelity, which current operational weather models fail to provide due to the use of coarse grid spacings (a few kilometers) constrained by the predictive performance limitations of traditional computer architectures. Herein, we demonstrate how FastEddy®, a microscale model that exploits the accelerated nature of graphics processing units for high‐performance computing, can be used to understand and predict urban weather impacts from seasonal, day‐to‐day, diurnal, and sub‐hourly scales. To that end, we efficiently perform more than 50 telescoped simulations of microscale urban effects at street‐scale (5 m grid spacing) driven by realistic weather over a 20 km²region centered at the downtown area of Dallas, Texas. Our analyses demonstrate that urban‐weather interactions at the street‐scale are complex and tightly connected, which is of utmost relevance to AAM operations. These demonstrations reveal the capability of such models to provide real‐time weather hazard avoidance products tailored to capture microscale urban effects.