Computational urban flow predictions with Bayesian inference: Validation with field data
Computational urban flow predictions with Bayesian inference: Validation with field data
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DOI：
10.1016/j.buildenv.2019.02.028
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发表时间：
2019-05
影响因子：
7.4
通讯作者：
Jorge Sousa;C. Gorlé
中科院分区：
文献类型：
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作者：
Jorge Sousa;C. Gorlé
城市地区预计将快速扩张。在支持城市和建筑的可持续设计的背景下，计算流体动力学（CFD）可用于提供城市流场的详细信息。然而，大气边界层流的复杂性和自然变化会限制 CFD 的预测性能。在本文中，我们提出了贝叶斯推理方法的验证研究，该方法通过同化城市传感器测量数据来估计城市流雷诺平均纳维斯托克斯 (RANS) 模拟的流入边界条件。该方法采用集合卡尔曼滤波器迭代估计来风的概率密度函数并改进后续的RANS预测。本研究中使用的测量结果是在斯坦福大学校园进行的全面实验活动中获得的。在屋顶和行人层部署了六个声波风速计；传感器的子集用于数据同化，而其余传感器用于验证。将所提出的推理方法的准确性与基于气象站数据定义边界条件的传统方法进行了比较。使用推理方法时，命中率增加了两倍，并且预测平均值在实验数据的 95% 置信区间内的可能性增加了约 20%。对传感器数量及其位置影响的分析表明，只要可以从传感器测量中识别入口流量特性，同化方法就可以持续改进预测。
Urban areas are projected to expand at a rapid pace. In the context of supporting sustainable design of cities and buildings, computational fluid dynamics (CFD) can be used to provide detailed information on the urban flow field. However, the complexity and natural variability of atmospheric boundary layer flows can limit the predictive performance of CFD. In this paper, we present a validation study for a Bayesian inference method that estimates the inflow boundary conditions for Reynolds-averaged Navier-Stokes (RANS) simulations of urban flow by assimilating data from urban sensor measurements. The method employs the ensemble Kalman filter to iteratively estimate the probability density functions of the incoming wind and improve the subsequent RANS prediction. The measurements used in this study were obtained during a full-scale experimental campaign on Stanfords campus. Six sonic anemometers were deployed at roof and pedestrian level; a subset of the sensors was used for data assimilation while the remaining ones were used for validation. The accuracy of the proposed inference method is compared to the conventional approach that defines the boundary conditions based on weather station data. The hit rates increased by a factor of two when using the inference method, and the predicted mean values were ∼ 20% more likely to be within the 95% confidence interval of the experimental data. An analysis of the impact of the number of sensors and their location indicates that the assimilation approach can consistently improve the predictions, as long as the inlet flow properties are identifiable from the sensor measurements.