Convolution neural network for fluid flow simulations in cascade with oscillating blades

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F49777513%3A23520%2F25%3A43975744" target="_blank" >RIV/49777513:23520/25:43975744 - isvavai.cz</a>

Výsledek na webu

<a href="https://doi.org/10.1016/j.cam.2024.116478" target="_blank" >https://doi.org/10.1016/j.cam.2024.116478</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1016/j.cam.2024.116478" target="_blank" >10.1016/j.cam.2024.116478</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Convolution neural network for fluid flow simulations in cascade with oscillating blades

Popis výsledku v původním jazyce

This paper aims to design a computational model for simulating the unsteady flow field in a cascade of oscillating blades. The core of the new model is a convolutional neural network, which is trained on a simplified cascade consisting of three blades. The primary advantage lies in significantly reducing the computational cost, as the new model is several orders of magnitude faster than traditional CFD methods for evaluations, though training the model remains computationally intensive. The convolutional neural network can accurately predict the unsteady flow field, as demonstrated in validation examples. In the next step, a composition algorithm is proposed to combine several simplified cases, enabling the solution of a cascade with any number of blades.

Název v anglickém jazyce

Convolution neural network for fluid flow simulations in cascade with oscillating blades

Popis výsledku anglicky

This paper aims to design a computational model for simulating the unsteady flow field in a cascade of oscillating blades. The core of the new model is a convolutional neural network, which is trained on a simplified cascade consisting of three blades. The primary advantage lies in significantly reducing the computational cost, as the new model is several orders of magnitude faster than traditional CFD methods for evaluations, though training the model remains computationally intensive. The convolutional neural network can accurately predict the unsteady flow field, as demonstrated in validation examples. In the next step, a composition algorithm is proposed to combine several simplified cases, enabling the solution of a cascade with any number of blades.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

20302 - Applied mechanics

Projekt

<a href="/cs/project/GA24-12144S" target="_blank" >GA24-12144S: Výzkum 3D struktur proudění a jejich vlivu na aeroelastickou stabilitu kaskády turbínových lopatek pomocí experimentů a metody hlubokého učení</a><br>

Návaznosti

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

Rok uplatnění

2025

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

462

Číslo periodika v rámci svazku

JUL 2025

Stát vydavatele periodika

NL - Nizozemsko

Počet stran výsledku

11

Strana od-do

116478

Kód UT WoS článku

001399265700001

EID výsledku v databázi Scopus

2-s2.0-85214314064