Efficient Mann turbulence generation for offshore wind farms with applications in fatigue load surrogate modelling

Abstract

Abstract Surrogate models are vital for offshore wind farm optimisation, or digital twin for rapid execution. Reliable surrogates are difficult to make, especially for large wind farms and for fatigue loads. One limitation is the high resolution of turbulence needed for fatigue calculations and the vast size of a wind farm. We present code optimisations for implementing the Mann model for farm-sized flows, which are infeasible otherwise. Additionally, we present a case study of a full load surrogate for the Lillgrund offshore wind farm using large turbulence box generation in aeroelastic wind farm simulations using HAWC2Farm. Various mappings between the wind field in front of a turbine and the corresponding turbine structural fatigue loads are presented. The best-performing surrogate model, using a proper orthogonal decomposition of the input space with an artificial neural network, is able to significantly reduce the error in fatigue load estimates by as much as a factor of 5 compared to conventional methods. The presented recommendations are crucial to generating reliable wind farm load surrogates for wind farm optimisation purposes.