Engineering Mechanics

International Conference

Proceedings Vol. 27/28 (2022)


May 9 – 12, 2022, Milovy, Czech Republic
Editors: Cyril Fischer and Jiří Náprstek

Copyright © 2022 Institute of Theoretical and Applied Mechanics of the Czech Academy of Sciences, Prague

ISBN 978-80-86246-48-2 (printed)
ISBN 978-80-86246-51-2 (electronic)
ISSN 1805-8248 (printed)
ISSN 1805-8256 (electronic)

list of papers scientific commitee

Flow field prediction in a blade cascade using a convolution neural network
Bublík O., Heidler V., Pecka A., Vimmr J.
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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Ownership of copyright in original research articles remains with the Authors, and provided that, when reproducing parts of the contribution, the Authors acknowledge and/or reference the Proceedings, the Authors do not need to seek permission for re-use of their material.

All papers were reviewed by members of the scientific committee.

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