A data-informed analytic model for turbine power prediction with anisotropic local blockage effects - Juniper, MCR; Nishino, T (IOP Publishing, 2022)
This paper presents a new analytic approach for estimating the local power coefficient of a turbine experiencing anisotropic local blockage effects. Data-driven methods are employed first to approximately obtain a known analytic expression for isotropic local blockage effects, and then deployed to find candidate expressions for anisotropic local blockage effects. The dataset for the analysis of anisotropic local blockage is collected from 3D Reynolds-averaged Navier-Stokes (RANS) simulations of an infinitely wide array of actuator disks for nearly 2,000 different blockage configurations. This study builds upon previous work in array optimisation of both tidal and wind turbines, where the local power coefficient may increase substantially for optimal array configuration. A brief discussion on the relationship between the local blockage and wind farm blockage is also provided. Other theoretical approaches and possible refinements to the presented analytic model are also discussed.