Compressor cascade correlations modelling at design points using artificial neural networks

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21220%2F23%3A00370309" target="_blank" >RIV/68407700:21220/23:00370309 - isvavai.cz</a>

Výsledek na webu

<a href="https://doi.org/10.24132/acm.2023.828" target="_blank" >https://doi.org/10.24132/acm.2023.828</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.24132/acm.2023.828" target="_blank" >10.24132/acm.2023.828</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Compressor cascade correlations modelling at design points using artificial neural networks

Popis výsledku v původním jazyce

In recent years, the flow analysis by means of computational fluid dynamics (CFD) has become a useful design and optimization tool. Unfortunately, despite advances in the computational power, numerical simulations are still very time consuming. Thus, empirical correlation models keep their importance as a tool for early stages of axial compressor design and for prediction of basic performance parameters. These correlations were developed based on experimental data obtained from 2D measurements performed on cases of classical airfoils such as the NACA 65-series or C.4 profiles. There is insufficient amount of experimental data for other families of airfoils, but CFD simulations can be used instead and their results correlated using artificial neural networks (ANN), as described in this work. Unlike the classical deep learning approach using perceptrons, this work presents neural networks employing higher order neural units.

Název v anglickém jazyce

Compressor cascade correlations modelling at design points using artificial neural networks

Popis výsledku anglicky

In recent years, the flow analysis by means of computational fluid dynamics (CFD) has become a useful design and optimization tool. Unfortunately, despite advances in the computational power, numerical simulations are still very time consuming. Thus, empirical correlation models keep their importance as a tool for early stages of axial compressor design and for prediction of basic performance parameters. These correlations were developed based on experimental data obtained from 2D measurements performed on cases of classical airfoils such as the NACA 65-series or C.4 profiles. There is insufficient amount of experimental data for other families of airfoils, but CFD simulations can be used instead and their results correlated using artificial neural networks (ANN), as described in this work. Unlike the classical deep learning approach using perceptrons, this work presents neural networks employing higher order neural units.

Druh

J_SC - Článek v periodiku v databázi SCOPUS

CEP obor

—

OECD FORD obor

20304 - Aerospace 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 periodika

Applied and Computational Mechanics

ISSN

1802-680X

e-ISSN

—

Svazek periodika

17

Číslo periodika v rámci svazku

2

Stát vydavatele periodika

CZ - Česká republika

Počet stran výsledku

16

Strana od-do

1-16

Kód UT WoS článku

—

EID výsledku v databázi Scopus

2-s2.0-85190865578