NEURAL NETWORK PREDICTION OF THE FLOW FIELD IN A PERIODIC DOMAIN WITH HYPERNETWORK PARAMETRIZATION
The result's identifiers
Result code in 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>
Result on the web
<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>
Alternative languages
Result language
angličtina
Original language name
NEURAL NETWORK PREDICTION OF THE FLOW FIELD IN A PERIODIC DOMAIN WITH HYPERNETWORK PARAMETRIZATION
Original language description
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.
Czech name
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Czech description
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Classification
Type
D - Article in proceedings
CEP classification
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OECD FORD branch
20302 - Applied mechanics
Result continuities
Project
<a href="/en/project/GA21-31457S" target="_blank" >GA21-31457S: Fast flow-field prediction using deep neural networks for solving fluid-structure interaction problems</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)
Others
Publication year
2022
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
ECCOMAS conference proceeding
ISBN
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ISSN
2696-6999
e-ISSN
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Number of pages
10
Pages from-to
1-10
Publisher name
Scipedia S.L.
Place of publication
Barcelona
Event location
Oslo
Event date
Jun 5, 2022
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
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