Journal article 267 views 84 downloads
A reduced order model for turbulent flows in the urban environment using machine learning / Dunhui Xiao, C.E. Heaney, L. Mottet, F. Fang, W. Lin, I.M. Navon, Y. Guo, O.K. Matar, A.G. Robins, C.C. Pain
Building and Environment, Volume: 148, Pages: 323 - 337
Swansea University Author: Dunhui Xiao
PDF | Accepted Manuscript
Released under the terms of a Creative Commons Attribution Non-Commercial No Derivatives License (CC-BY-NC-ND).Download (2.09MB)
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 , we develop a fast-running Non-Intrusive Reduced Order Model (NIROM) for...
|Published in:||Building and Environment|
Check full text
No Tags, Be the first to tag this record!
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 , 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.
Non-intrusive reduced order modelling, Urban flows, Proper orthogonal decomposition, Machine learning, Gaussian process regression, Operational modelling
College of Engineering