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Enhancing CFD-LES air pollution prediction accuracy using data assimilation

机译：使用数据同化提高CFD-LES空气污染预测准确性

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摘要

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.
机译：全世界公认，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准确的数值工具。本研究实现了三维变分（3DVar）数据同化（DA）方法，以减少基于计算流体动力学（CFD）的预测污染浓度与在风洞实验中测量的浓度之间的差异。该方法是在风洞测试用例上实现的，该用例代表了本地化的邻域环境。从绝对误差，均方误差和污染浓度散点图的角度讨论了使用DA进行的CFD模拟提高的精度。结果表明，使用DA时，通过均方误差计算出的CFD结果与风洞数据之间的差异最多可以减少三个数量级。 CFD结果中保留了这种减少的误差，并且在重新运行CFD仿真后的几个时间步骤中可以看到其好处。随后提出了最佳的传感器定位。在精度和传感器数量之间需要权衡。发现在靠近污染源或污染浓度高的区域放置/考虑传感器时，可以提高精度。这表明仅需要14％的风洞数据，从而将均方误差降低了一个数量级。

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