{"title":"Compressor cascade correlations modelling at design points using artificial neural networks","authors":"Patrik Kovář, Jiří Fürst","doi":"10.24132/acm.2023.828","DOIUrl":null,"url":null,"abstract":"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.","PeriodicalId":37801,"journal":{"name":"Applied and Computational Mechanics","volume":"242 ","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2023-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Applied and Computational Mechanics","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.24132/acm.2023.828","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"Chemical Engineering","Score":null,"Total":0}
引用次数: 0
Abstract
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.
期刊介绍:
The ACM journal covers a broad spectrum of topics in all fields of applied and computational mechanics with special emphasis on mathematical modelling and numerical simulations with experimental support, if relevant. Our audience is the international scientific community, academics as well as engineers interested in such disciplines. Original research papers falling into the following areas are considered for possible publication: solid mechanics, mechanics of materials, thermodynamics, biomechanics and mechanobiology, fluid-structure interaction, dynamics of multibody systems, mechatronics, vibrations and waves, reliability and durability of structures, structural damage and fracture mechanics, heterogenous media and multiscale problems, structural mechanics, experimental methods in mechanics. This list is neither exhaustive nor fixed.