Neural-network-based fluid–structure interaction applied to vortex-induced vibration

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F49777513%3A23520%2F23%3A43968682" target="_blank" >RIV/49777513:23520/23:43968682 - isvavai.cz</a>

Výsledek na webu

<a href="https://www.sciencedirect.com/science/article/pii/S0377042723001140?pes=vor" target="_blank" >https://www.sciencedirect.com/science/article/pii/S0377042723001140?pes=vor</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1016/j.cam.2023.115170" target="_blank" >10.1016/j.cam.2023.115170</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Neural-network-based fluid–structure interaction applied to vortex-induced vibration

Popis výsledku v původním jazyce

In this paper, a fluid–structure interaction (FSI) solver with neural-network-based fluid-flow prediction is proposed. This concept is applied to the problem of vortex-induced vibration of a cylinder. The majority of studies that are concerned with fluid-flow prediction using neural networks solve problems with fixed boundary. In this paper, a convolutional neural network (CNN) is used to predict unsteady incompressible laminar flow with moving boundary. A deformable non-Cartesian grid, which traces the boundary of the fluid domain, is used in this paper. The CNN is trained for oscillating cylinder with various frequencies and amplitudes. The dynamics of the elastically-mounted cylinder is modelled using a linear spring–mass–damper model and solved by an implicit differential scheme. The results show that the CNN-based FSI solver is capable of capturing the so-called lock-in phenomenon for the problem of vortex-induced vibration of a cylinder and the quantitative behaviour is similar to the results of the CFD-based FSI solver. Moreover, the CNN-based FSI solver is two orders of magnitude faster than the CFD-based FSI solver and the speedup is expected to be even greater on larger problems.

Název v anglickém jazyce

Neural-network-based fluid–structure interaction applied to vortex-induced vibration

Popis výsledku anglicky

In this paper, a fluid–structure interaction (FSI) solver with neural-network-based fluid-flow prediction is proposed. This concept is applied to the problem of vortex-induced vibration of a cylinder. The majority of studies that are concerned with fluid-flow prediction using neural networks solve problems with fixed boundary. In this paper, a convolutional neural network (CNN) is used to predict unsteady incompressible laminar flow with moving boundary. A deformable non-Cartesian grid, which traces the boundary of the fluid domain, is used in this paper. The CNN is trained for oscillating cylinder with various frequencies and amplitudes. The dynamics of the elastically-mounted cylinder is modelled using a linear spring–mass–damper model and solved by an implicit differential scheme. The results show that the CNN-based FSI solver is capable of capturing the so-called lock-in phenomenon for the problem of vortex-induced vibration of a cylinder and the quantitative behaviour is similar to the results of the CFD-based FSI solver. Moreover, the CNN-based FSI solver is two orders of magnitude faster than the CFD-based FSI solver and the speedup is expected to be even greater on larger problems.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

20302 - Applied mechanics

Projekt

Výsledek vznikl pri realizaci vícero projektů. Více informací v záložce Projekty.

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2023

Kód důvěrnosti údajů

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

Název periodika

JOURNAL OF COMPUTATIONAL AND APPLIED MATHEMATICS

ISSN

0377-0427

e-ISSN

1879-1778

Svazek periodika

428

Číslo periodika v rámci svazku

AUG 2023

Stát vydavatele periodika

NL - Nizozemsko

Počet stran výsledku

9

Strana od-do

—

Kód UT WoS článku

000962241600001

EID výsledku v databázi Scopus

2-s2.0-85149732684