Sanjiva K. Lele, Aeronautics and Astronautics, and Mechanical Engineering; John Weyant, Management Science and Engineering
Large wind farms typically produce about 25% less power than the cumulative output that would be expected by the windmills cited separately. This is due, in part, to rear windmills operating in the turbulent wakes of those up front. These wakes also increase strain on the downwind blades and, thus, raise operating costs. This project, funded jointly with the Precourt Institute for Energy, is testing whether positioning smaller mixing turbines among the primary turbines, in conjunction with other new management approaches, will significantly increase output and cut costs. The researchers are developing and testing software for designing more efficient large-scale wind farms.
This project has shown that wake skewing is a viable technique for mitigating wake loss at farm scale. Researchers developed a novel approach to stochastically simulate the effect of stratified atmospheric turbulence on unsteady loading of wind turbine blades. Specifically, the Kinematic Simulation approach for farm scale performance assessment (including fatigue) is two orders of magnitude cheaper than conventional Actuator-Line/Large Eddy Simulation. The KS model predicts planetary boundary layer turbulence with correct space-time statistics, as was validated using high fidelity data.
Publications and media:
“A modeling framework for wind farm analysis: Wind turbine wake interactions” 33rd Wind Energy Symposium/AIAA SCITECH Forum(2015): 1-15.
“Sweeping based kinematic simulation for the stably stratified surface layer” (abstract submitted for the 67th Annual Meeting of the APS Division of Fluid Dynamics) Bulletin of the American Physical Society 59 (2014).