Microscale simulations of extreme events in complex terrain driven by mesoscalar budget components. Issue 2 (1st May 2022) - Avila, Matias; Lehmkuhl, O.; Navarro, J.; González-Rouco, J.F.; Paredes, D.; Diaz-Marta, G.; Owen, H.
Abstract: In this work, we apply a downscaling strategy to analyze extreme weather events that may impact wind farm operation. The coupling applies mesoscale momentum budget components (tendencies) from the WRF model as forcing terms to the governing microscale equations. Our study focuses on flow over complex terrain during specific days to reproduce extreme weather events that produced wind turbine damage. The interaction of the meso- and micro-scale features are relevant in the simulation of extreme conditions. The simulation results are compared with observations from nacelle anemometers of the wind turbines in two different wind farms by analyzing time series and wind profiles. The microscale code Alya, developed at the Barcelona Supercomputing Center (BSC), is closed with URANS and LES closures to solve the momentum and energy equations. Both closures use the same mesoscalar to microscalar coupling methodology and are used in this work to simulate the wind flow. We present the implementation of the mesoscalar coupling to the microscale solver when using URANS and LES closures. We show that the coupling via tendencies has excellent potential for understanding transient events under extreme weather conditions in very complex terrain. The wind industry can use such simulations to enhance forensic analysis in cases of wind turbine accidents or any other event that may impact turbine operation, such as high turbulence phenomena. We test the ability of the meso- to microscale coupling model to reproduce extreme events with regard to quantities of interest in wind energy. Simulation results using URANS and LES closures agree reasonably well with observations. In some scenarios, the LES provides results that are closer to measurements. LES models have the advantage of providing wind gusts. We compare the accuracy and performance (CPU-time) of the URANS vs. LES approaches.