Compressor cascade positive and negative stall incidence angle correlation modelling using artificial neural networks

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21220%2F23%3A00366274" target="_blank" >RIV/68407700:21220/23:00366274 - isvavai.cz</a>

Výsledek na webu

<a href="https://www.engmech.cz/" target="_blank" >https://www.engmech.cz/</a>

DOI - Digital Object Identifier

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Jazyk výsledku

angličtina

Název v původním jazyce

Compressor cascade positive and negative stall incidence angle correlation modelling using artificial neural networks

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

Compressor cascade positive and negative stall incidence angle correlation modelling using artificial neural networks

Popis výsledku anglicky

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.

Druh

D - Stať ve sborníku

CEP obor

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OECD FORD obor

20301 - Mechanical engineering

Projekt

Výsledek vznikl pri realizaci vícero projektů. Více informací v záložce Projekty.

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2023

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

Engineering Mechanics 2023: Book of full texts

ISBN

978-80-87012-84-0

ISSN

1805-8248

e-ISSN

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Počet stran výsledku

4

Strana od-do

127-130

Název nakladatele

Institute of Thermomechanics, AS CR, v.v.i.

Místo vydání

Prague

Místo konání akce

Milovy

Datum konání akce

9. 5. 2023

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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