VTechWorks staff will be away for the Thanksgiving holiday beginning at noon on Wednesday, November 27, through Friday, November 29. We will resume normal operations on Monday, December 2. Thank you for your patience.
 

Frame-independent vector-cloud neural network for nonlocal constitutive modeling on arbitrary grids

Simple item page
dc.contributor.authorZhou, Xu-Huien
dc.contributor.authorHan, Jiequnen
dc.contributor.authorXiao, Hengen
dc.date.accessioned2022-02-15T14:10:57Zen
dc.date.available2022-02-15T14:10:57Zen
dc.date.issued2022-01-01en
dc.date.updated2022-02-15T14:10:54Zen
dc.description.abstractConstitutive models are widely used for modeling complex systems in science and engineering, where first-principle-based, well-resolved simulations are often prohibitively expensive. For example, in fluid dynamics, constitutive models are required to describe nonlocal, unresolved physics such as turbulence and laminar–turbulent transition. However, traditional constitutive models based on partial differential equations (PDEs) often lack robustness and are too rigid to accommodate diverse calibration datasets. We propose a frame-independent, nonlocal constitutive model based on a vector-cloud neural network that can be learned with data. The model predicts the closure variable at a point based on the flow information in its neighborhood. Such nonlocal information is represented by a group of points, each having a feature vector attached to it, and thus the input is referred to as vector cloud. The cloud is mapped to the closure variable through a frame-independent neural network, invariant both to coordinate translation and rotation and to the ordering of points in the cloud. As such, the network can deal with any number of arbitrarily arranged grid points and thus is suitable for unstructured meshes in fluid simulations. The merits of the proposed network are demonstrated for scalar transport PDEs on a family of parameterized periodic hill geometries. The vector-cloud neural network is a promising tool not only as nonlocal constitutive models and but also as general surrogate models for PDEs on irregular domains.en
dc.description.versionAccepted versionen
dc.format.extent23 page(s)en
dc.format.mimetypeapplication/pdfen
dc.identifierARTN 114211 (Article number)en
dc.identifier.doihttps://doi.org/10.1016/j.cma.2021.114211en
dc.identifier.eissn1879-2138en
dc.identifier.issn0045-7825en
dc.identifier.orcidXiao, Heng [0000-0002-3323-4028]en
dc.identifier.urihttp://hdl.handle.net/10919/108363en
dc.identifier.volume388en
dc.language.isoenen
dc.publisherElsevieren
dc.relation.urihttp://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000720462200008&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=930d57c9ac61a043676db62af60056c1en
dc.rightsIn Copyrighten
dc.rights.urihttp://rightsstatements.org/vocab/InC/1.0/en
dc.subjectEngineering, Multidisciplinaryen
dc.subjectMathematics, Interdisciplinary Applicationsen
dc.subjectMechanicsen
dc.subjectEngineeringen
dc.subjectMathematicsen
dc.subjectConstitutive modelingen
dc.subjectNonlocal closure modelen
dc.subjectSymmetry and invarianceen
dc.subjectInverse modelingen
dc.subjectDeep learningen
dc.subjectSTRESS MODELSen
dc.subjectTURBULENCEen
dc.subjectREPRESENTATIONen
dc.subjectEQUATIONen
dc.subjectSYSTEMSen
dc.subjectFLOWen
dc.subjectApplied Mathematicsen
dc.subject01 Mathematical Sciencesen
dc.subject09 Engineeringen
dc.titleFrame-independent vector-cloud neural network for nonlocal constitutive modeling on arbitrary gridsen
dc.title.serialComputer Methods in Applied Mechanics and Engineeringen
dc.typeArticle - Refereeden
dc.type.dcmitypeTexten
dc.type.otherArticleen
dc.type.otherJournalen
pubs.organisational-group/Virginia Techen
pubs.organisational-group/Virginia Tech/Engineeringen
pubs.organisational-group/Virginia Tech/Engineering/Aerospace and Ocean Engineeringen
pubs.organisational-group/Virginia Tech/University Research Institutesen
pubs.organisational-group/Virginia Tech/University Research Institutes/Fralin Life Sciencesen
pubs.organisational-group/Virginia Tech/All T&R Facultyen
pubs.organisational-group/Virginia Tech/Engineering/COE T&R Facultyen
pubs.organisational-group/Virginia Tech/University Research Institutes/Fralin Life Sciences/Durelle Scotten

Files

Original bundle
Now showing 1 - 1 of 1
Thumbnail Image
Name:
2103.06685.pdf
Size:
3.98 MB
Format:
Adobe Portable Document Format
Description:
Accepted version
Download

Collections

All Faculty Deposits
Scholarly Works, Aerospace and Ocean Engineering