NEURAL NETWORK PREDICTION OF THE FLOW FIELD IN A PERIODIC DOMAIN WITH HYPERNETWORK PARAMETRIZATION

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F49777513%3A23520%2F22%3A43968209" target="_blank" >RIV/49777513:23520/22:43968209 - isvavai.cz</a>

Výsledek na webu

<a href="https://www.scipedia.com/sj/eccomas2022" target="_blank" >https://www.scipedia.com/sj/eccomas2022</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.23967/eccomas.2022.192" target="_blank" >10.23967/eccomas.2022.192</a>

Jazyk výsledku

angličtina

Název v původním jazyce

NEURAL NETWORK PREDICTION OF THE FLOW FIELD IN A PERIODIC DOMAIN WITH HYPERNETWORK PARAMETRIZATION

Popis výsledku v původním jazyce

This paper is concerned with fast flow field prediction in a blade cascade for variable blade shapes as well as variable Reynolds number using the machine-learning architecture called convolutional neural network. To generate flow field for a specific Reynolds number, an encoder-decoder convolutional neural network, also called U-Net, is used. The values 500, 1000 and 1500 of the Reynolds number are chosen as the training set. Three U-Nets were trained on CFD results for 100 blade profiles, each U-Net for a different Reynolds number. In order to get a prediction for variable Reynolds number, a so-called hypernetwork in employed. The hypernetwork essentially interpolates between the two trained U-Nets. The architecture of the hypernetwork is fully-connected feedforward neural network with one input neuron correspond-ing to the Reynolds number, one hidden layer and the output layer corresponds to the weights for the interpolated U-Net. The concept of the hypernetwork-based parametrization is tested on a problem of compressible fluid flow through a blade cascade with three unseen blade profiles and unseen Reynolds number.

Název v anglickém jazyce

NEURAL NETWORK PREDICTION OF THE FLOW FIELD IN A PERIODIC DOMAIN WITH HYPERNETWORK PARAMETRIZATION

Popis výsledku anglicky

This paper is concerned with fast flow field prediction in a blade cascade for variable blade shapes as well as variable Reynolds number using the machine-learning architecture called convolutional neural network. To generate flow field for a specific Reynolds number, an encoder-decoder convolutional neural network, also called U-Net, is used. The values 500, 1000 and 1500 of the Reynolds number are chosen as the training set. Three U-Nets were trained on CFD results for 100 blade profiles, each U-Net for a different Reynolds number. In order to get a prediction for variable Reynolds number, a so-called hypernetwork in employed. The hypernetwork essentially interpolates between the two trained U-Nets. The architecture of the hypernetwork is fully-connected feedforward neural network with one input neuron correspond-ing to the Reynolds number, one hidden layer and the output layer corresponds to the weights for the interpolated U-Net. The concept of the hypernetwork-based parametrization is tested on a problem of compressible fluid flow through a blade cascade with three unseen blade profiles and unseen Reynolds number.

Druh

D - Stať ve sborníku

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

2022

Kód důvěrnosti údajů

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

Název statě ve sborníku

ECCOMAS conference proceeding

ISBN

—

ISSN

2696-6999

e-ISSN

—

Počet stran výsledku

10

Strana od-do

1-10

Název nakladatele

Scipedia S.L.

Místo vydání

Barcelona

Místo konání akce

Oslo

Datum konání akce

5. 6. 2022

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

—