Meet our Featured Researcher, Dr. Xiaolei Yang
Dr. Yang’s research is focused on developing computational methods for high Reynolds number turbulent flows and investigating the mechanism of complex flow phenomena in environmental and industrial applications including wind energy, marine and hydrokinetic energy, and urban environments.
Wind energy, yielding reduced carbon emissions, improved air quality and reduced water consumption by offsetting the use of fossil energy, has become a key player in the global energy markets. In wind farms, wind turbines interact with each other through turbine wakes, which decrease the power extraction and increase the dynamic loads of downwind turbines. To reduce the cost and increase the competitiveness of wind energy, it is of great significance to understand the dynamics of turbine wakes and their interaction with the atmospheric turbulence. The objective of the current research, which is funded by the industry, is to develop an advanced wind farm simulation tool with turbine control and aeroelastic models for simulating turbine wakes under site-specific wind and terrain conditions. In the year of 2018, we developed actuator surface models for turbine blades and nacelle (Wind Energy, 21(5), 285-302, 2018), which can take into account their geometry effects on turbine wakes in farm-scale simulations. We investigated the turbine wake dynamics under different operating conditions (Physical Review Fluids, 3(5), 054607, 2018). We studied the wake meandering for different turbine designs (Journal of Fluid Mechanics, 842, 5-25, 2018). For wind farm studies, we successfully applied the developed code to the Pleasant Valley wind farm owned by XCEL Energy and the Horns Rev wind farm in Denmark. In collaboration with Invenergy, for the first time, large-eddy simulation is successfully applied to a wind farm in complex terrain and validated with the field data (Figure 1; Applied energy, 229, 767-777, 2018).
In the next step of this project, we will investigate the effects of advanced turbine control on the dynamics of turbine wakes and wake interactions. We will also develop reduced-order wind farm models based on the physics of turbine wakes and the computed and measured data of full-scale wind farms.
