In this study, we performed a suite of flow simulations for a 12-wind-turbine array with varying inflow conditions and lateral spacings, and compared the impacts of the flow on velocity deficit and wake recovery. We imposed both laminar inflow and turbulent inflows, which contain turbulence for the Ekman layer and a low-level jet (LLJ) in the stable boundary layer. To solve the flow through the wind turbines and their wakes, we used a large-eddy simulation technique with an actuator-line method. We compared the time series for the velocity deficit at the first and rear columns to observe the temporal change in velocity deficit for the entire wind farm. The velocity deficit at the first column for LLJ inflow was similar to that for laminar inflow. However, the magnitude of velocity deficit at the rear columns for the case with LLJ inflow was 11.9% greater because of strong wake recovery, which was enhanced by the vertical flux of kinetic energy associated with the LLJ. To observe the spatial transition and characteristics of wake recovery, we performed statistical analyses of the velocity at different locations for both the laminar and LLJ inflows. These studies indicated that strong wake recovery was present, and a kurtosis analysis showed that the probability density function for the streamwise velocity followed a Gaussian distribution. In a quadrant analysis of the Reynolds stress, we found that the ejection and sweep motions for the LLJ inflow case were greater than those for the laminar inflow case.
Bibliographical noteFunding Information:
Ministry of Trade, Industry, and Energy (MOTIE), Grant/Award Number: No. 20163030024420; National Research Foundation of Korea (NRF), Grant/Award Number: NRF‐2015R1A2A1A15056182
This work was supported by the Korea Institute of Energy Technology Evaluation and Planning (KETEP) and the Ministry of Trade, Industry, and Energy (MOTIE) of the Republic of Korea (No. 20163030024420). This work was also supported by a grant from the Midcareer Researcher Program of the National Research Foundation of Korea (NRF), funded by the Ministry of Science, ICT and Future Planning (NRF‐ 2015R1A2A1A15056182). This work was also supported by the Brain Korea 21 Plus Project in 2017. LANL Institutional Computing (IC) provided high‐performance computing resources under w17_atmo_turbulence project.
All Science Journal Classification (ASJC) codes
- Renewable Energy, Sustainability and the Environment