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-ε-fP based 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.
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机译：本文提出了一种将多个保真度级别的唤醒模型组合在一起的方法，该方法仅用少量的高保真度样本即可给出准确的预测。基于G. C. Larsen和k-ε-fP的RANS模型分别被用作低保真度模型和高保真度模型的集合成员。基于单变量和多变量的替代模型都是通过将局部风速和风向作为风电场功率效率函数的变量来建立的。比较了各种多保真代理模型，并讨论了不同的采样方案。分析表明，多保真度唤醒模型可以极大地减少构建精确代理所需的高保真度模型评估。
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