Engineering Mechanics

Proceedings Vol. 27/28 (2022)

ENGINEERING MECHANICS 2022

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

 

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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