No Cover Image

Journal article 760 views 138 downloads

A domain decomposition non-intrusive reduced order model for turbulent flows

D. Xiao, C.E. Heaney, F. Fang, L. Mottet, R. Hu, D.A. Bistrian, E. Aristodemou, I.M. Navon, C.C. Pain, Dunhui Xiao Orcid Logo

Computers & Fluids, Volume: 182, Pages: 15 - 27

Swansea University Author: Dunhui Xiao Orcid Logo

Check full text

DOI (Published version): 10.1016/j.compfluid.2019.02.012

Abstract

In this paper, a new Domain Decomposition Non-Intrusive Reduced Order Model (DDNIROM) is developed for turbulent flows. The method works by partitioning the computational domain into a number of subdomains in such a way that the summation of weights associated with the finite element nodes within ea...

Full description

Published in: Computers & Fluids
ISSN: 00457930
Published: 2019
Online Access: Check full text

URI: https://cronfa.swan.ac.uk/Record/cronfa48945
Tags: Add Tag
No Tags, Be the first to tag this record!
first_indexed 2019-02-21T20:05:59Z
last_indexed 2019-04-02T10:17:42Z
id cronfa48945
recordtype SURis
fullrecord <?xml version="1.0"?><rfc1807><datestamp>2019-04-01T14:12:21.0982279</datestamp><bib-version>v2</bib-version><id>48945</id><entry>2019-02-21</entry><title>A domain decomposition non-intrusive reduced order model for turbulent flows</title><swanseaauthors><author><sid>62c69b98cbcdc9142622d4f398fdab97</sid><ORCID>0000-0003-2461-523X</ORCID><firstname>Dunhui</firstname><surname>Xiao</surname><name>Dunhui Xiao</name><active>true</active><ethesisStudent>false</ethesisStudent></author></swanseaauthors><date>2019-02-21</date><deptcode>AERO</deptcode><abstract>In this paper, a new Domain Decomposition Non-Intrusive Reduced Order Model (DDNIROM) is developed for turbulent flows. The method works by partitioning the computational domain into a number of subdomains in such a way that the summation of weights associated with the finite element nodes within each subdomain is approximately equal, and the communication between subdomains is minimised. With suitably chosen weights, it is expected that there will be approximately equal accuracy associated with each subdomain. This accuracy is maximised by allowing the partitioning to occur through areas of the domain that have relatively little flow activity, which, in this case, is characterised by the pointwise maximum Reynolds stresses.A Gaussian Process Regression (GPR) machine learning method is used to construct a set of local approximation functions (hypersurfaces) for each subdomain. Each local hypersurface represents not only the fluid dynamics over the subdomain it belongs to, but also the interactions of the flow dynamics with the surrounding subdomains. Thus, in this way, the surrounding subdomains may be viewed as providing boundary conditions for the current subdomain.We consider a specific example of turbulent air flow within an urban neighbourhood at a test site in London and demonstrate the effectiveness of the proposed DDNIROM.</abstract><type>Journal Article</type><journal>Computers &amp; Fluids</journal><volume>182</volume><paginationStart>15</paginationStart><paginationEnd>27</paginationEnd><publisher/><issnPrint>00457930</issnPrint><keywords>Non-Intrusive Reduced Order Modelling, Domain Decomposition, Machine Learning, Gaussian Process Regression, Urban flows, Turbulent flows, Finite Element Method</keywords><publishedDay>31</publishedDay><publishedMonth>12</publishedMonth><publishedYear>2019</publishedYear><publishedDate>2019-12-31</publishedDate><doi>10.1016/j.compfluid.2019.02.012</doi><url/><notes/><college>COLLEGE NANME</college><department>Aerospace Engineering</department><CollegeCode>COLLEGE CODE</CollegeCode><DepartmentCode>AERO</DepartmentCode><institution>Swansea University</institution><apcterm/><lastEdited>2019-04-01T14:12:21.0982279</lastEdited><Created>2019-02-21T13:18:54.9929637</Created><path><level id="1">Faculty of Science and Engineering</level><level id="2">School of Aerospace, Civil, Electrical, General and Mechanical Engineering - Aerospace Engineering</level></path><authors><author><firstname>D.</firstname><surname>Xiao</surname><order>1</order></author><author><firstname>C.E.</firstname><surname>Heaney</surname><order>2</order></author><author><firstname>F.</firstname><surname>Fang</surname><order>3</order></author><author><firstname>L.</firstname><surname>Mottet</surname><order>4</order></author><author><firstname>R.</firstname><surname>Hu</surname><order>5</order></author><author><firstname>D.A.</firstname><surname>Bistrian</surname><order>6</order></author><author><firstname>E.</firstname><surname>Aristodemou</surname><order>7</order></author><author><firstname>I.M.</firstname><surname>Navon</surname><order>8</order></author><author><firstname>C.C.</firstname><surname>Pain</surname><order>9</order></author><author><firstname>Dunhui</firstname><surname>Xiao</surname><orcid>0000-0003-2461-523X</orcid><order>10</order></author></authors><documents><document><filename>0048945-21022019132251.pdf</filename><originalFilename>xiao2019v2.pdf</originalFilename><uploaded>2019-02-21T13:22:51.8370000</uploaded><type>Output</type><contentLength>7405145</contentLength><contentType>application/pdf</contentType><version>Accepted Manuscript</version><cronfaStatus>true</cronfaStatus><embargoDate>2020-02-15T00:00:00.0000000</embargoDate><copyrightCorrect>true</copyrightCorrect><language>eng</language></document></documents><OutputDurs/></rfc1807>
spelling 2019-04-01T14:12:21.0982279 v2 48945 2019-02-21 A domain decomposition non-intrusive reduced order model for turbulent flows 62c69b98cbcdc9142622d4f398fdab97 0000-0003-2461-523X Dunhui Xiao Dunhui Xiao true false 2019-02-21 AERO In this paper, a new Domain Decomposition Non-Intrusive Reduced Order Model (DDNIROM) is developed for turbulent flows. The method works by partitioning the computational domain into a number of subdomains in such a way that the summation of weights associated with the finite element nodes within each subdomain is approximately equal, and the communication between subdomains is minimised. With suitably chosen weights, it is expected that there will be approximately equal accuracy associated with each subdomain. This accuracy is maximised by allowing the partitioning to occur through areas of the domain that have relatively little flow activity, which, in this case, is characterised by the pointwise maximum Reynolds stresses.A Gaussian Process Regression (GPR) machine learning method is used to construct a set of local approximation functions (hypersurfaces) for each subdomain. Each local hypersurface represents not only the fluid dynamics over the subdomain it belongs to, but also the interactions of the flow dynamics with the surrounding subdomains. Thus, in this way, the surrounding subdomains may be viewed as providing boundary conditions for the current subdomain.We consider a specific example of turbulent air flow within an urban neighbourhood at a test site in London and demonstrate the effectiveness of the proposed DDNIROM. Journal Article Computers & Fluids 182 15 27 00457930 Non-Intrusive Reduced Order Modelling, Domain Decomposition, Machine Learning, Gaussian Process Regression, Urban flows, Turbulent flows, Finite Element Method 31 12 2019 2019-12-31 10.1016/j.compfluid.2019.02.012 COLLEGE NANME Aerospace Engineering COLLEGE CODE AERO Swansea University 2019-04-01T14:12:21.0982279 2019-02-21T13:18:54.9929637 Faculty of Science and Engineering School of Aerospace, Civil, Electrical, General and Mechanical Engineering - Aerospace Engineering D. Xiao 1 C.E. Heaney 2 F. Fang 3 L. Mottet 4 R. Hu 5 D.A. Bistrian 6 E. Aristodemou 7 I.M. Navon 8 C.C. Pain 9 Dunhui Xiao 0000-0003-2461-523X 10 0048945-21022019132251.pdf xiao2019v2.pdf 2019-02-21T13:22:51.8370000 Output 7405145 application/pdf Accepted Manuscript true 2020-02-15T00:00:00.0000000 true eng
title A domain decomposition non-intrusive reduced order model for turbulent flows
spellingShingle A domain decomposition non-intrusive reduced order model for turbulent flows
Dunhui Xiao
title_short A domain decomposition non-intrusive reduced order model for turbulent flows
title_full A domain decomposition non-intrusive reduced order model for turbulent flows
title_fullStr A domain decomposition non-intrusive reduced order model for turbulent flows
title_full_unstemmed A domain decomposition non-intrusive reduced order model for turbulent flows
title_sort A domain decomposition non-intrusive reduced order model for turbulent flows
author_id_str_mv 62c69b98cbcdc9142622d4f398fdab97
author_id_fullname_str_mv 62c69b98cbcdc9142622d4f398fdab97_***_Dunhui Xiao
author Dunhui Xiao
author2 D. Xiao
C.E. Heaney
F. Fang
L. Mottet
R. Hu
D.A. Bistrian
E. Aristodemou
I.M. Navon
C.C. Pain
Dunhui Xiao
format Journal article
container_title Computers & Fluids
container_volume 182
container_start_page 15
publishDate 2019
institution Swansea University
issn 00457930
doi_str_mv 10.1016/j.compfluid.2019.02.012
college_str Faculty of Science and Engineering
hierarchytype
hierarchy_top_id facultyofscienceandengineering
hierarchy_top_title Faculty of Science and Engineering
hierarchy_parent_id facultyofscienceandengineering
hierarchy_parent_title Faculty of Science and Engineering
department_str 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
document_store_str 1
active_str 0
description In this paper, a new Domain Decomposition Non-Intrusive Reduced Order Model (DDNIROM) is developed for turbulent flows. The method works by partitioning the computational domain into a number of subdomains in such a way that the summation of weights associated with the finite element nodes within each subdomain is approximately equal, and the communication between subdomains is minimised. With suitably chosen weights, it is expected that there will be approximately equal accuracy associated with each subdomain. This accuracy is maximised by allowing the partitioning to occur through areas of the domain that have relatively little flow activity, which, in this case, is characterised by the pointwise maximum Reynolds stresses.A Gaussian Process Regression (GPR) machine learning method is used to construct a set of local approximation functions (hypersurfaces) for each subdomain. Each local hypersurface represents not only the fluid dynamics over the subdomain it belongs to, but also the interactions of the flow dynamics with the surrounding subdomains. Thus, in this way, the surrounding subdomains may be viewed as providing boundary conditions for the current subdomain.We consider a specific example of turbulent air flow within an urban neighbourhood at a test site in London and demonstrate the effectiveness of the proposed DDNIROM.
published_date 2019-12-31T03:59:38Z
_version_ 1763753044329103360
score 11.036706

Similar Items