USACM Energy & Earth Systems TTA Seminar - William C. Radünz скачать в хорошем качестве

USACM Energy & Earth Systems TTA Seminar - William C. Radünz

November 11, 2025: William C. Radünz, Johns Hopkins University Multiscale simulations of atmosphere–wind farm interactions and the role of spatial gradients on power performance and wake recovery Understanding the multiscale physics of atmosphere–wind farm interactions is one of the central challenges in modern wind energy science. This talk highlights two complementary studies that reveal how the existence or lack of spatial gradients, whether induced by terrain or under-resolved in simulations, govern wind farm performance and wake recovery. The first part focuses on the American WAKE ExperimeNt (AWAKEN), where multiscale large-eddy simulations with the Weather Research and Forecasting model (WRF–LES) and observations from an onshore wind farm reveal unexpected spatial variability in turbine power. Despite operating in nominally simple terrain, hub-height wind speeds vary by nearly 4 m s⁻¹ over just 5 km during nocturnal low-level jet events, resulting in downwind turbines outperforming upwind ones by 25–50 %. These streamwise gradients arise from terrain-induced vertical displacements of the jet core, revealing how even gentle orography can strongly modulate intra-farm performance. The second part examines idealized offshore wind farms simulated with the WRF model equipped with the Fitch Wind Farm Parameterization (WFP), benchmarked against large-eddy simulations. Results show that mesoscale models systematically underestimate wake recovery—not due to excessive dissipation, but because coarse grids fail to resolve the spatial wind velocity gradients that sustain turbulence via shear production. This under-resolution of gradients in the near-farm wake propagates biases downstream, limiting predictive skill for cluster-scale effects. Together, these studies demonstrate that spatial gradients, whether physical or numerical, are fundamental to understanding atmosphere–wind farm interactions. Accurately representing these gradients is essential for capturing the performance variability of onshore wind farms across both simple and complex terrain, as well as for predicting wake effects in future large wind farm clusters.