Engineering Mechanics

Proceedings Vol. 29 (2023)

ENGINEERING MECHANICS 2023

Copyright © 2023 Institute of Thermomechanics of the Czech Academy of Sciences, Prague

 

Kovář P., Fürst J.
pages 127 - 130, full text

The analysis of the flows by computational fluid dynamics becomes useful design and optimization method during recent years. Despite the advances in the computational power but it could be still very demanding. Therefore empirical models are commonly used as a main tool for design and prediction of basic performance of axial compressor cascades. The empirical correlations are derived from experimental data obtained from two-dimensional measurements. Unfortunately, sufficient amount of data is available only in cases of well-known airfoils as e.g. NACA 65-series or C.4 profiles. Thus, there is en effort to find a similar relation which will serve in the same manner for another family of the airfoils. The construction of such correlations using artificial neural networks is proposed in this work. In contrast to standard deep neural network, the proposed neural network is built using higher order neural units.

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