A data-driven investigation of the flow around a high-rise building is performed combining heterogeneous experimental samples and RANS CFD. The coupling is performed using techniques based on the Ensemble Kalman Filter (EnKF), including advanced manipulations such as localization and inflation. The augmented state estimation obtained via EnKF has also been employed to improve the predictive features of the model via an optimization of the five free global model constant of the $\mathcal{K}-\varepsilon$ turbulence model used to close the equations. The optimized values are very far from the classical values prescribed as general recommendations and implemented in codes, but also different from other data-driven analyses reported in the literature. The results obtained with this new optimized parametric description show a global improvement for both the velocity field and the pressure field. In addition, some topological improvement for the flow organization are observed downstream, far from the location of the sensors.

翻译：围绕高层建筑的流动开展了一项数据驱动研究，将异构实验样本与RANS CFD相结合。采用基于集合卡尔曼滤波（EnKF）的技术进行耦合，包括定位和膨胀等高级处理。通过EnKF获得的增强状态估计还被用于优化封闭方程所用的$\mathcal{K}-\varepsilon$湍流模型的五个自由全局模型常数，从而提升模型的预测能力。优化后的参数值与作为通用建议并在代码中实施的经典值相去甚远，同时也不同于文献中报道的其他数据驱动分析结果。采用这种新的优化参数描述所获得的结果显示，速度场和压力场均有整体改善。此外，在远离传感器位置的下游区域，流动组织的拓扑结构也有所改善。