Enhancing CFD-LES air pollution prediction accuracy using data assimilation
File(s)
Accepted version
Author(s)
Type
Journal Article
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.
Date Issued
2019-11-01
Date Acceptance
2019-08-29
Citation
Building and Environment, 2019, 165
ISSN
0007-3628
Publisher
Elsevier
Journal / Book Title
Building and Environment
Volume
165
Copyright Statement
© 2019 Elsevier Ltd. All rights reserved. This manuscript is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International Licence http://creativecommons.org/licenses/by-nc-nd/4.0/
Sponsor
Engineering & Physical Science Research Council (E
Identifier
http://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000494993200015&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=1ba7043ffcc86c417c072aa74d649202
Grant Number
RG80519
Subjects
Science & Technology
Technology
Construction & Building Technology
Engineering, Environmental
Engineering, Civil
Engineering
CFD
Data assimilation
Wind tunnel
Fluidity
Urban environment
Pollutant concentration
Sensor positioning
WIND-TUNNEL
DISPERSION
IMPLEMENTATION
RANS
VENTILATION
SIMULATION
SCALE
FLOWS
Publication Status
Published
Article Number
ARTN 106383
Date Publish Online
2019-09-03