Proceedings Vol. 27/28 (2022)
ENGINEERING MECHANICS 2022
May 9 – 12, 2022, Milovy, Czech Republic
Copyright © 2022 Institute of Theoretical and Applied Mechanics of the Czech Academy of Sciences, Prague
ISBN 978-80-86246-51-2 (electronic)
ISSN 1805-8248 (printed)
ISSN 1805-8256 (electronic)
list of papers scientific commitee
pages 41 - 44, full text
This paper deals with fast flow field prediction in a blade cascade using a machine-learning architecture called a convolutional neural network. Specifically, our work focuses on the U-Net architecture. With the following study, our goal is to parameterize the neural network architecture, in dependence on Reynolds and Mach numbers. The hyper network in our model is a simple fully-connected feedforward neural network generating weights, which fit the U-net architecture with the specific Mach and Reynolds number. The concept of the hyper network-based parametrization is tested on the problem of a compressible fluid flow through the blade cascade.
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All papers were reviewed by members of the scientific committee.