Wind Energy (May 2023)
Using observational mean‐flow data to drive large‐eddy simulations of a diurnal cycle at the SWiFT site
Abstract
Abstract Reproducing realistic date‐ and site‐specific unsteady wind conditions in large‐eddy simulations is becoming increasingly useful in wind energy. How to run a large‐eddy simulation to match observed conditions, however, remains an open research question. One approach that has received considerable attention is mesoscale‐to‐microscale coupling, in which information about the mesoscale weather, most commonly acquired from a mesoscale numerical weather model, is passed on to a microscale model. In this paper, we demonstrate how the recently developed profile‐assimilation technique, a form of mesoscale‐to‐microscale coupling, can be used to drive large‐eddy simulations solely based on observed mean‐flow profiles at a single location, bypassing the need for auxiliary mesoscale simulations. The new approach is evaluated for a diurnal cycle at the Scaled Wind Farm Technology site. Observed mean‐flow profiles from the ground up to a height of 2 km are reconstructed by aggregating measurements from multiple instruments, and gaps in the data are infilled with natural neighbor interpolation. We perform nine simulations using various forcing approaches to deal with data limitations. The results show that it is indeed possible to drive microscale large‐eddy simulation with observations using the profile‐assimilation technique, notwithstanding large gaps in virtual potential temperature measurements. However, profile assimilation with vertical smoothing of the error between the desired and actual profiles is required. Without that smoothing, the microscale simulations develop unrealistically high turbulence levels under many situations. Finally, we show that simulated mesoscale data can account for missing observations, although care is needed as both data sources are not necessarily compatible.
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