Model order reduction for particle-laden flows: systems with rotations and discrete transport operators

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61388998%3A_____%2F23%3A00573854" target="_blank" >RIV/61388998:_____/23:00573854 - isvavai.cz</a>

Výsledek na webu

<a href="http://www2.it.cas.cz/fm2015/im/admin/showfile/data/my/Papers/2023/14-TPFM2023.pdf" target="_blank" >http://www2.it.cas.cz/fm2015/im/admin/showfile/data/my/Papers/2023/14-TPFM2023.pdf</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.14311/TPFM.2023.014" target="_blank" >10.14311/TPFM.2023.014</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Model order reduction for particle-laden flows: systems with rotations and discrete transport operators

Popis výsledku v původním jazyce

In the present work, we concentrate on particle-laden flows as an example of industry-relevant transport-dominated systems. Our previously-developed framework for data-driven model order reduction (MOR) of such systems, the shifted proper orthogonal decomposition with interpolation via artificial neural networks, is further extended by improving the handling of general transport operators. First, even with intrusive MOR approaches, the underlying numerical solvers can provide only discrete realizations of transports linked to the movement of individual particles in the system. On the other hand, our MOR methodology requires continuous transport operators. Thus, the original framework was extended by the possibility to reconstruct continuous approximations of known discrete transports via another artificial neural network. Second, the treatment of rotation-comprising transports was significantly improved.

Název v anglickém jazyce

Model order reduction for particle-laden flows: systems with rotations and discrete transport operators

Popis výsledku anglicky

In the present work, we concentrate on particle-laden flows as an example of industry-relevant transport-dominated systems. Our previously-developed framework for data-driven model order reduction (MOR) of such systems, the shifted proper orthogonal decomposition with interpolation via artificial neural networks, is further extended by improving the handling of general transport operators. First, even with intrusive MOR approaches, the underlying numerical solvers can provide only discrete realizations of transports linked to the movement of individual particles in the system. On the other hand, our MOR methodology requires continuous transport operators. Thus, the original framework was extended by the possibility to reconstruct continuous approximations of known discrete transports via another artificial neural network. Second, the treatment of rotation-comprising transports was significantly improved.

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

20302 - Applied mechanics

Projekt

<a href="/cs/project/TM04000048" target="_blank" >TM04000048: Laser Improved Novel Surface Engineering and Repair of Tools</a><br>

Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2023

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 2023

ISBN

978-80-87012-83-3

ISSN

2336-5781

e-ISSN

—

Počet stran výsledku

8

Strana od-do

96-103

Název nakladatele

Ústav termomechaniky AV ČR, v. v. i.

Místo vydání

Praha

Místo konání akce

Prague

Datum konání akce

22. 2. 2023

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

001235670200014