Impact of rotor size on aeroelastic uncertainty with lidar-constrained turbulence

Abstract

Nacelle-mounted lidar measurements offer the opportunity to tailor turbulence to specific conditions, reducing aeroelastic uncertainty in one-to-one wind turbine validation and opening the door for novel lidar-based control methodologies. Despite this, the use of lidar to generate constrained turbulence is not commonplace, partially due to a lack of readily available tools. Kaimal-based constrained turbulence methods - such as the one implemented in the open-source constrained-turbulence generator PyConTurb - can be used to easily generate turbulence constrained to measurements in a matter of minutes. Unfortunately, the limitations of the Kaimal-based methods prevent the direct use of the lidar data as constraints. This paper therefore presents a preprocessing methodology to convert lidar data to PyConTurb-ready constraints. The method is demonstrated by quantifying the aeroelastic uncertainty for the DTU 10 MW and the NREL 5 MW without constraints and comparing it to the corresponding value with lidar constraints. The results show an excellent reduction in the one-to-one aeroelastic uncertainty and an adequate reduction on fatigue loads when lidar-constrained turbulence is used as inflow. The NREL 5 MW is found to have better reduction in uncertainty due to a rotor size that is better suited to the lidar geometry.