Multi-fidelity wake modelling based on Co-Kriging method

The Science of Making Torque from Wind

Wang, Y.M.^1 ; Réthoré, P.-E.^2 ; Van Der Laan, M.P.^2 ; Leon, J P Murcia^2 ; Liu, Y.Q.^1 ; Li, L.^1

State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources, North China Electric Power University, Beijing

102206, China^1

Department of Wind Energy, Technical University of Denmark, Risø Campus, Roskilde

4000, Denmark^2

关键词: Accurate prediction;    Ensemble members;    High fidelity models;    Multi fidelities;    Multiple levels;    Power efficiency;    Sampling schemes;    Wind directions;   

Others  :  https://iopscience.iop.org/article/10.1088/1742-6596/753/3/032065/pdf
DOI  :  10.1088/1742-6596/753/3/032065

来源: IOP

【 摘 要 】

The article presents an approach to combine wake models of multiple levels of fidelity, which is capable of giving accurate predictions with only a small number of high fidelity samples. The G. C. Larsen and k--fPbased RANS models are adopted as ensemble members of low fidelity and high fidelity models, respectively. Both the univariate and multivariate based surrogate models are established by taking the local wind speed and wind direction as variables of the wind farm power efficiency function. Various multi-fidelity surrogate models are compared and different sampling schemes are discussed. The analysis shows that the multi-fidelity wake models could tremendously reduce the high fidelity model evaluations needed in building an accurate surrogate.