Aeromechanical optimization of first row compressor test stand blades using a hybrid machine learning model of genetic algorithm, artificial neural networks and design of experiments

Aeromechanical optimization of first row compressor test stand blades using a hybrid machine learning model of genetic algorithm, artificial neural networks and design of experiments

In this paper, optimization of the first blade of a new test rig is pursued using a hybrid model comprising the genetic algorithm, artificial neural networks and design of experiments. Blade tuning is conducted using three-dimensional geometric parameters. Taper and sweep angle play important roles...

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Bibliographic Details
Main Authors: Mohammad Ghalandari, Alireza Ziamolki, Amir Mosavi, Shahaboddin Shamshirband, Kwok-Wing Chau, Saeed Bornassi
Format: Article
Language:English
Published: Taylor & Francis Group 2019-01-01
Series:Engineering Applications of Computational Fluid Mechanics
Subjects:
axial compressor blade
aeroelasticity
multidisciplinary design optimization
computational fluid dynamics (cfd)
machine learning
artificial neural network (ann)
design of experiments (doe)
Online Access:http://dx.doi.org/10.1080/19942060.2019.1649196

Internet

http://dx.doi.org/10.1080/19942060.2019.1649196

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