Enhancing CFD-LES air pollution prediction accuracy using data assimilation. (November 2019)

• Record Type: Journal Article
• Authors: Aristodemou, Elsa; Arcucci, Rossella; Mottet, Laetitia; Robins, Alan; Pain, Christopher; Guo, Yi-Ke
• Is Part Of: Building and environment. Volume 165(2019)
• Publication Date: 2019-11
• Subjects: CFD -- Data assimilation -- Wind tunnel -- Fluidity -- Urban environment -- Pollutant concentration -- Sensor positioning; Buildings -- Environmental engineering -- Periodicals; Building -- Research -- Periodicals; Constructions -- Technique de l'environnement -- Périodiques; Electronic journals; 696
• DOI: 10.1016/j.buildenv.2019.106383
• Languages: English
• ISSNs: 0360-1323

Abstract

Abstract: 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. Highlights: We improve the prediction of pollution dispersion based on Computational Fluid Dynamics. We use data assimilation to improve accuracy of predicted pollutant concentration. Our model reduces the discrepancy between Fluidity and wind tunnel data. We improve the prediction of pollution dispersion in an urban environment. We determine the optimal position of sensors in an urban environment.