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This article summarizes the results of a fifth Blind test workshop, which was held in Visby, Sweden, in May 2017.This study compares the numerical predictions of the wake flow behind a model wind turbine operated in yaw to experimental wind tunnel results. Prior to the work shop, research groups were invited to predict the turbines' performances and wake flow properties using computational fluid dynamics (CFD) methods. For this purpose, the power, thrust and yaw moments for a 30 • yawed model turbine as well as the wake's mean and turbulent streamwise and vertical flow components were measured 5 in the wind tunnel at the Norwegian University of Science and Technology (NTNU). In order to increase the complexity, a non-yawed downstream turbine was added in a second test case, while a third test case challenged the modelers with a new rotor and turbine geometry. Four participants submitted predictions using different flow solvers, three of which were based on Large Eddy Simulations (LES) while another one used an Improved Delayed Detached Eddy Simulation (IDDES) model. The performance of a single 10 yawed turbine was fairly well predicted by all simulations, both in the first and third test case. The scatter in the downstream turbine's performance predictions in the second test case, however, was found to be significantly larger. The complex asymmetric shape of the mean streamwise and vertical velocity was generally well predicted by all the simulations for all test cases.The largest improvement with respect to previous Blind tests is the good prediction of the levels of turbulent kinetic energy in the wake, even for the complex case of yaw misalignment. These very promising results confirm the mature development 15 stage of LES/DES simulations for wind turbine wake modeling, while competitive advantages might be obtained by faster computational methods.

No abstract

Abstract. This article summarizes the results of the “Blind test 5” workshop, which was held in Visby, Sweden, in May 2017. This study compares the numerical predictions of the wake flow behind a model wind turbine operated in yaw to experimental wind tunnel results. Prior to the workshop, research groups were invited to predict the turbine performance and wake flow properties using computational fluid dynamics (CFD) methods. For this purpose, the power, thrust, and yaw moments for a 30∘ yawed model turbine, as well as the wake's mean and turbulent streamwise and vertical flow components, were measured in the wind tunnel at the Norwegian University of Science and Technology (NTNU). In order to increase the complexity, a non-yawed downstream turbine was added in a second test case, while a third test case challenged the modelers with a new rotor and turbine geometry. Four participants submitted predictions using different flow solvers, three of which were based on large eddy simulations (LES) while another one used an improved delayed detached eddy simulation (IDDES) model. The performance of a single yawed turbine was fairly well predicted by all simulations, both in the first and third test cases. The scatter in the downstream turbine performance predictions in the second test case, however, was found to be significantly larger. The complex asymmetric shape of the mean streamwise and vertical velocities was generally well predicted by all the simulations for all test cases. The largest improvement with respect to previous blind tests is the good prediction of the levels of TKE in the wake, even for the complex case of yaw misalignment. These very promising results confirm the mature development stage of LES/DES simulations for wind turbine wake modeling, while competitive advantages might be obtained by faster computational methods.

As the size of wind turbines increases and their hub heights become higher, which partially explains the vertiginous increase of wind power worldwide in the last decade, the interaction of wind turbines with the atmospheric boundary layer (ABL) and between each other is becoming more complex. There are different approaches to model and compute the aerodynamic loads, and hence the power production, on wind turbines subject to ABL inflow conditions ranging from the classical Blade Element Momentum (BEM) method to Computational Fluid Dynamic (CFD) approaches. Also, modern multi-MW wind turbines have a torque controller and a collective pitch controller to manage power output, particularly in maximizing power production or when it is required to down-regulate their production. In this work the results of a validated numerical method, based on a Large Eddy Simulation-Actuator Line Model framework, was applied to simulate a real 7.7 MNW onshore wind farm on Uruguay under different wind conditions, and hence operational situations are shown with the aim to assess the capability of this approach to model actual wind farm dynamics. A description of the implementation of these controllers in the CFD solver Caffa3d, presenting the methodology applied to obtain the controller parameters, is included. For validation, the simulation results were compared with 1 Hz data obtained from the Supervisory Control and Data Acquisition System of the wind farm, focusing on the temporal evolution of the following variables: Wind velocity, rotor angular speed, pitch angle, and electric power. In addition to this, simulations applying active power control at the wind turbine level are presented under different de-rate signals, both constant and time-varying, and were subject to different wind speed profiles and wind directions where there was interaction between wind turbines and their wakes.