Large-eddy simulations (LES) can provide accurate predictions of wind loads on buildings, but their high computational cost, and the need to explore all wind directions with a 10° resolution, limits their use in the design process. Reynolds-averaged Navier–Stokes (RANS) have a low computational cost, but their accuracy can be compromised by the turbulence model and by the model required to retrieve the pressure fluctuations, that ultimately determine the design loads. This study proposes a multi-fidelity machine learning framework that combines computationally efficient RANS, for a large number of wind directions, with more expensive LES, for a small number of wind directions, to provide accurate predictions of the root mean square pressure coefficient at a reasonable computational cost. The training set includes 5 wind directions with a 20° resolution; the test set contains the 5 intermediate wind directions. A bootstrap algorithm, used to generate an ensemble of models, provides confidence intervals that encompass the majority of the LES data for the test directions. These results demonstrate that multi-fidelity machine learning frameworks provide a route to balancing accuracy and computational cost in the prediction of complex turbulent flow quantities.

大涡模拟（LES）可以提供建筑物风荷载的准确预测，但是其高昂的计算成本以及对以10°分辨率探索所有风向的需求，限制了它们在设计过程中的使用。雷诺平均的Navier-Stokes（RANS）的计算成本较低，但是其精度可能会受到湍流模型和检索压力波动（最终确定设计载荷）所需的模型的影响。这项研究提出了一种多保真度的机器学习框架，该框架结合了针对大量风向的高效计算RANS和针对少数风向的较昂贵LES的组合，从而提供了对最大均方根压力系数的准确预测。合理的计算成本。训练集包括5个20°分辨率的风向；测试集包含5个中间风向。用来生成模型整体的自举算法提供了置信区间，该置信区间涵盖了测试方向的大多数LES数据。这些结果表明，多保真机器学习框架为预测复杂的湍流量提供了一种在准确性和计算成本之间取得平衡的途径。