Imperial College London

Professor Omar K. Matar, FREng

Faculty of EngineeringDepartment of Chemical Engineering

Vice-Dean (Education), Faculty of Engineering



+44 (0)20 7594 9618o.matar Website




Miss Nazma Mojid +44 (0)20 7594 3918




506ACE ExtensionSouth Kensington Campus






BibTex format

author = {Xiao, D and Heaney, CE and Mottet, L and Fang, F and Lin, W and Navon, IM and Guo, Y and Matar, OK and Robins, AG and Pain, CC},
doi = {10.1016/j.buildenv.2018.10.035},
journal = {Building and Environment},
pages = {323--337},
title = {A reduced order model for turbulent flows in the urban environment using machine learning},
url = {},
volume = {148},
year = {2019}

RIS format (EndNote, RefMan)

AB - To help create a comfortable and healthy indoor and outdoor environment in which to live, there is a need to understand turbulent air flows within the urban environment. To this end, building on a previously reported method [1], we develop a fast-running Non-Intrusive Reduced Order Model (NIROM) for predicting the turbulent air flows found within an urban environment. To resolve larger scale turbulent fluctuations, we employ a Large Eddy Simulation (LES) model and solve the resulting computational model on unstructured meshes. The objective is to construct a rapid-running NIROM from these results that will have ‘similar’ dynamics to the original LES model. Based on Proper Orthogonal Decomposition (POD) and machine learning techniques, this Reduced Order Model (ROM) is six orders of magnitude faster than the high-fidelity LES model and we demonstrate how ‘similar’ it can be to the high-fidelity model by comparing statistical quantities such as the mean flows, Reynolds stresses and probability densities of the velocities. We also include validation of the high-fidelity model against data from wind tunnel experiments.This paper represents a key step towards the use of reduced order modelling for operational purposes with the tantalising possibility of it being used in place of Gaussian plume models, and the potential for greatly improved model fidelity and confidence.
AU - Xiao,D
AU - Heaney,CE
AU - Mottet,L
AU - Fang,F
AU - Lin,W
AU - Navon,IM
AU - Guo,Y
AU - Matar,OK
AU - Robins,AG
AU - Pain,CC
DO - 10.1016/j.buildenv.2018.10.035
EP - 337
PY - 2019///
SN - 0360-1323
SP - 323
TI - A reduced order model for turbulent flows in the urban environment using machine learning
T2 - Building and Environment
UR -
UR -
VL - 148
ER -