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A reduced order model for turbulent flows in the urban environment using machine learning
Building and Environment, Volume: 148, Pages: 323 - 337
Swansea University Author:
Dunhui Xiao
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DOI (Published version): 10.1016/j.buildenv.2018.10.035
Abstract
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...
Published in: | Building and Environment |
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ISSN: | 0360-1323 |
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2019
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URI: | https://cronfa.swan.ac.uk/Record/cronfa46445 |
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2021-01-15T10:25:10.4900135 v2 46445 2018-12-06 A reduced order model for turbulent flows in the urban environment using machine learning 62c69b98cbcdc9142622d4f398fdab97 0000-0003-2461-523X Dunhui Xiao Dunhui Xiao true false 2018-12-06 AERO 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. Journal Article Building and Environment 148 323 337 0360-1323 Non-intrusive reduced order modelling, Urban flows, Proper orthogonal decomposition, Machine learning, Gaussian process regression, Operational modelling 15 1 2019 2019-01-15 10.1016/j.buildenv.2018.10.035 COLLEGE NANME Aerospace Engineering COLLEGE CODE AERO Swansea University 2021-01-15T10:25:10.4900135 2018-12-06T14:51:12.8651530 Faculty of Science and Engineering School of Aerospace, Civil, Electrical, General and Mechanical Engineering - Aerospace Engineering Dunhui Xiao 0000-0003-2461-523X 1 C.E. Heaney 2 L. Mottet 3 F. Fang 4 W. Lin 5 I.M. Navon 6 Y. Guo 7 O.K. Matar 8 A.G. Robins 9 C.C. Pain 10 0046445-13122018164928.pdf urban.pdf 2018-12-13T16:49:28.0700000 Output 2199396 application/pdf Accepted Manuscript true 2019-11-15T00:00:00.0000000 Released under the terms of a Creative Commons Attribution Non-Commercial No Derivatives License (CC-BY-NC-ND). true eng |
title |
A reduced order model for turbulent flows in the urban environment using machine learning |
spellingShingle |
A reduced order model for turbulent flows in the urban environment using machine learning Dunhui Xiao |
title_short |
A reduced order model for turbulent flows in the urban environment using machine learning |
title_full |
A reduced order model for turbulent flows in the urban environment using machine learning |
title_fullStr |
A reduced order model for turbulent flows in the urban environment using machine learning |
title_full_unstemmed |
A reduced order model for turbulent flows in the urban environment using machine learning |
title_sort |
A reduced order model for turbulent flows in the urban environment using machine learning |
author_id_str_mv |
62c69b98cbcdc9142622d4f398fdab97 |
author_id_fullname_str_mv |
62c69b98cbcdc9142622d4f398fdab97_***_Dunhui Xiao |
author |
Dunhui Xiao |
author2 |
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 |
format |
Journal article |
container_title |
Building and Environment |
container_volume |
148 |
container_start_page |
323 |
publishDate |
2019 |
institution |
Swansea University |
issn |
0360-1323 |
doi_str_mv |
10.1016/j.buildenv.2018.10.035 |
college_str |
Faculty of Science and Engineering |
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facultyofscienceandengineering |
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Faculty of Science and Engineering |
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facultyofscienceandengineering |
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Faculty of Science and Engineering |
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School of Aerospace, Civil, Electrical, General and Mechanical Engineering - Aerospace Engineering{{{_:::_}}}Faculty of Science and Engineering{{{_:::_}}}School of Aerospace, Civil, Electrical, General and Mechanical Engineering - Aerospace Engineering |
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description |
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. |
published_date |
2019-01-15T03:58:01Z |
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1763752942130692096 |
score |
11.02167 |