In this section
Several of our current PhD candidates and fellow researchers at the Data Science Institute have published, or in the proccess of publishing, papers to present their research.
@article{Aristodemou:2019:10.1016/j.buildenv.2019.106383,
author = {Aristodemou, E and Arcucci, R and Mottet, L and Robins, A and Pain, C and Guo, Y-K},
doi = {10.1016/j.buildenv.2019.106383},
journal = {Building and Environment},
title = {Enhancing CFD-LES air pollution prediction accuracy using data assimilation},
url = {http://dx.doi.org/10.1016/j.buildenv.2019.106383},
volume = {165},
year = {2019}
}
TY - JOUR
AB - 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.
AU - Aristodemou,E
AU - Arcucci,R
AU - Mottet,L
AU - Robins,A
AU - Pain,C
AU - Guo,Y-K
DO - 10.1016/j.buildenv.2019.106383
PY - 2019///
SN - 0007-3628
TI - Enhancing CFD-LES air pollution prediction accuracy using data assimilation
T2 - Building and Environment
UR - http://dx.doi.org/10.1016/j.buildenv.2019.106383
UR - http://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000494993200015&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=1ba7043ffcc86c417c072aa74d649202
UR - http://hdl.handle.net/10044/1/86221
VL - 165
ER -
Data Science Institute
William Penney Laboratory
Imperial College London
South Kensington Campus
London SW7 2AZ
United Kingdom
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