Investigations on offshore wind turbine inflow modelling using numerical weather prediction coupled with local-scale computational fluid dynamics

Alessio Castorrini, Sabrina Gentile, Edoardo Geraldi and Aldo Bonfiglioli

Renewable and Sustainable Energy Reviews, 2023, vol. 171, issue C

Abstract: The computational power available nowadays to industry and research paves the way to increasingly more accurate systems for the wind resource prediction. A promising approach is to support the mesoscale numerical weather prediction (NWP) with high fidelity computational fluid dynamics (CFD). This approach aims at increasing the spatial resolution of the wind prediction by not only accounting for the complex and multiphysics aspects of the atmosphere over a large geographical region, but also including the effects of the fine scale turbulence and the interaction of the wind flow with the sea surface. In this work, we test a set of model setups for both the mesoscale (NWP) and local scale (CFD) simulations employed in a multi-scale modelling framework. The method comprises a one-way coupling interface to define boundary conditions for the local scale simulation (based on the Reynolds Averaged Navier–Stokes equations) using the mesoscale wind given by the NWP system. The wind prediction in an offshore site is compared with LiDAR measurements, testing a set of mesoscale planetary boundary layer schemes, and different model choices for the local scale simulation, which include steady and unsteady approaches for simulation and boundary conditions, different turbulence closure constants, and the effect of the wave motion of the sea surface. The resulting wind is then used for the simulation of a large wind turbine, showing how a realistic wind profile and an ideal exponential law profile lead to different predictions of wind turbine rotor performance and loads.

Keywords: Wind; Mesoscale; RANS; WRF; NWP; PBL; Wind energy; Wind turbine (search for similar items in EconPapers)
Date: 2023
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Citations: View citations in EconPapers (3)

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DOI: 10.1016/j.rser.2022.113008

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