Flow-Field Prediction in Periodic Domains Using a Convolution Neural Network with Hypernetwork Parametrization

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F49777513%3A23520%2F23%3A43968210" target="_blank" >RIV/49777513:23520/23:43968210 - isvavai.cz</a>

Výsledek na webu

<a href="https://www.worldscientific.com/doi/abs/10.1142/S1758825123500187" target="_blank" >https://www.worldscientific.com/doi/abs/10.1142/S1758825123500187</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1142/S1758825123500187" target="_blank" >10.1142/S1758825123500187</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Flow-Field Prediction in Periodic Domains Using a Convolution Neural Network with Hypernetwork Parametrization

Popis výsledku v původním jazyce

This paper deals with flow field prediction in a blade cascade using the convolution neural network. The convolutional neural network (CNN) predicts density, pressure and velocity fields based on the given geometry. The blade cascade is modeled as a single interblade channel with periodic boundary conditions. In this paper, an algorithm that enforces periodic boundary conditions onto the CNN is presented. The main target of this study is to parametrize the CNN model depending on the Reynolds number. A new parametrization approach based on a so-called hypernetwork is employed for this purpose. The idea of this approach is that when the Reynolds number is modified, the hypernetwork modifies the weights of the CNN in such a way that it produces flow fields corresponding to that particular Reynolds number. The concept of the hypernetwork-based parametrization is tested on the problem of a compressible fluid flow through a blade cascade with variable blade profiles and Reynolds numbers.

Název v anglickém jazyce

Flow-Field Prediction in Periodic Domains Using a Convolution Neural Network with Hypernetwork Parametrization

Popis výsledku anglicky

This paper deals with flow field prediction in a blade cascade using the convolution neural network. The convolutional neural network (CNN) predicts density, pressure and velocity fields based on the given geometry. The blade cascade is modeled as a single interblade channel with periodic boundary conditions. In this paper, an algorithm that enforces periodic boundary conditions onto the CNN is presented. The main target of this study is to parametrize the CNN model depending on the Reynolds number. A new parametrization approach based on a so-called hypernetwork is employed for this purpose. The idea of this approach is that when the Reynolds number is modified, the hypernetwork modifies the weights of the CNN in such a way that it produces flow fields corresponding to that particular Reynolds number. The concept of the hypernetwork-based parametrization is tested on the problem of a compressible fluid flow through a blade cascade with variable blade profiles and Reynolds numbers.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

20302 - Applied mechanics

Projekt

<a href="/cs/project/GA21-31457S" target="_blank" >GA21-31457S: Použití neuronových sítí pro rychlou predikci proudového pole v úlohách interakce tekutiny s tělesem</a><br>

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

International Journal of Applied Mechanics

ISSN

1758-8251

e-ISSN

1758-826X

Svazek periodika

15

Číslo periodika v rámci svazku

2

Stát vydavatele periodika

SG - Singapurská republika

Počet stran výsledku

20

Strana od-do

1-20

Kód UT WoS článku

000923377600001

EID výsledku v databázi Scopus

2-s2.0-85147495139