Enhancing CFD-LES air pollution prediction accuracy using data assimilation

Journal article


Aristodemou, Elsa, Arcucci, R, Mottet, L, Robins, A, Pain, C and Guo, Y (2019). Enhancing CFD-LES air pollution prediction accuracy using data assimilation. Building and Environment. 165.
AuthorsAristodemou, Elsa, Arcucci, R, Mottet, L, Robins, A, Pain, C and Guo, Y
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.

Year2019
JournalBuilding and Environment
Journal citation165
PublisherElsevier
ISSN0007-3628
Digital Object Identifier (DOI)doi:https://www.doi.org/10.1016/j.buildenv.2019.106383
Publication dates
Print01 Nov 2019
Online03 Sep 2019
Publication process dates
Accepted29 Aug 2019
Deposited23 Dec 2019
Accepted author manuscript
License
CC BY-NC-ND 4.0
File Access Level
Open
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https://openresearch.lsbu.ac.uk/item/88v6y

Accepted author manuscript

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