Enhancing CFD-LES air pollution prediction accuracy using data assimilation
Enhancing CFD-LES air pollution prediction accuracy using data assimilation
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DOI：
10.1016/j.buildenv.2019.106383
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发表时间：
2019-11-01
影响因子：
7.4
通讯作者：
Guo, Yi-Ke
中科院分区：
文献类型：
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作者：
Aristodemou, Elsa;Arcucci, Rossella;Guo, Yi-Ke
全世界都认识到空气污染是每天过早死亡的原因，因此有必要开发更可靠和更准确的数字工具。本研究实施了三维变分（3DVar）数据同化（DA）的方法，以减少预测的污染浓度之间的差异计算流体动力学（CFD）的基础上，在风洞实验中测量的。该方法是实施风洞测试的情况下，代表了一个本地化的邻里环境。从污染物浓度的绝对误差、均方误差和散点图等方面讨论了DA对CFD模拟精度的提高。结果表明，计算流体动力学结果和风洞数据之间的差异，计算的均方误差，可以减少到三个数量级时，使用DA。这种减少的错误是保留在计算流体动力学结果和它的好处可以看到，通过几个时间步骤后，重新运行的计算流体动力学模拟。随后提出了一种传感器的最优定位方法。在精度和传感器数量之间存在折衷。发现当将传感器放置/考虑在污染源附近或污染浓度高的区域时，精度得到提高。这表明只需要14%的风洞数据，将均方误差降低了一个数量级。
It is recognised worldwide that air pollution is the cause of premature deaths daily, thus necessitating the development of more reliable and accurate numerical tools. The present study implements a three dimensional Variational (3DVar) data assimilation (DA) approach to reduce the discrepancy between predicted pollution concentrations based on Computational Fluid Dynamics (CFD) with the ones measured in a wind tunnel experiment. The methodology is implemented on a wind tunnel test case which represents a localised neighbourhood environment. The improved accuracy of the CFD simulation using DA is discussed in terms of absolute error, mean squared error and scatter plots for the pollution concentration. It is shown that the difference between CFD results and wind tunnel data, computed by the mean squared error, can be reduced by up to three order of magnitudes when using DA. This reduction in error is preserved in the CFD results and its benefit can be seen through several time steps after re-running the CFD simulation. Subsequently an optimal sensors positioning is proposed. There is a trade-off between the accuracy and the number of sensors. It was found that the accuracy was improved when placing/considering the sensors which were near the pollution source or in regions where pollution concentrations were high. This demonstrated that only 14% of the wind tunnel data was needed, reducing the mean squared error by one order of magnitude.