Shifted proper orthogonal decomposition and artificial neural networks for time-continuous reduced order models of transport-dominated systems

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61388998%3A_____%2F22%3A00560836" target="_blank" >RIV/61388998:_____/22:00560836 - isvavai.cz</a>

Výsledek na webu

<a href="http://www2.it.cas.cz/fm2015/im/admin/showfile/data/my/Papers/2022/16-TPFM2022.pdf" target="_blank" >http://www2.it.cas.cz/fm2015/im/admin/showfile/data/my/Papers/2022/16-TPFM2022.pdf</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.14311/TPFM.2022.016" target="_blank" >10.14311/TPFM.2022.016</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Shifted proper orthogonal decomposition and artificial neural networks for time-continuous reduced order models of transport-dominated systems

Popis výsledku v původním jazyce

Transport-dominated systems are pervasive in both industrial and scientific applications. However, they provide a challenge for common mode-based model order reduction (MOR) approaches, as they often require a large number of linear modes to obtain a sufficiently accurate reduced order model (ROM). In this work, we utilize the shifted proper orthogonal decomposition (sPOD), a methodology tailored for MOR of transport-dominated systems, and combine it with an interpolation based on artificial neural networks (ANN) to obtain a time-continuous ROM usable in engineering practice. The resulting MOR framework is purely data-driven, i.e., it does not require any information on the full order model (FOM) structure, which extends its applicability. On the other hand, compared to the standard projection-based approaches to MOR, the dimensionality reduction utilizing sPOD and ANN is significantly more computationally expensive since it requires a solution of high-dimensional optimization problems.

Název v anglickém jazyce

Shifted proper orthogonal decomposition and artificial neural networks for time-continuous reduced order models of transport-dominated systems

Popis výsledku anglicky

Transport-dominated systems are pervasive in both industrial and scientific applications. However, they provide a challenge for common mode-based model order reduction (MOR) approaches, as they often require a large number of linear modes to obtain a sufficiently accurate reduced order model (ROM). In this work, we utilize the shifted proper orthogonal decomposition (sPOD), a methodology tailored for MOR of transport-dominated systems, and combine it with an interpolation based on artificial neural networks (ANN) to obtain a time-continuous ROM usable in engineering practice. The resulting MOR framework is purely data-driven, i.e., it does not require any information on the full order model (FOM) structure, which extends its applicability. On the other hand, compared to the standard projection-based approaches to MOR, the dimensionality reduction utilizing sPOD and ANN is significantly more computationally expensive since it requires a solution of high-dimensional optimization problems.

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

20302 - Applied mechanics

Projekt

<a href="/cs/project/EF15_003%2F0000493" target="_blank" >EF15_003/0000493: Centrum pro výzkum nelineárního dynamického chování pokročilých materiálů ve strojírenství (CeNDYNMAT)</a><br>

Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2022

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

Topical Problems of Fluid Mechanics 2022

ISBN

978-80-87012-77-2

ISSN

2336-5781

e-ISSN

—

Počet stran výsledku

8

Strana od-do

111-118

Název nakladatele

Ústav termomechaniky AV ČR, v. v. i.

Místo vydání

Praha

Místo konání akce

Praha

Datum konání akce

16. 2. 2022

Typ akce podle státní příslušnosti

EUR - Evropská akce

Kód UT WoS článku

001235659500016