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Title: 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
Authors: Ghalandari, M
Ziamolki, A
Mosavi, A
Shamshirband, S
Chau, KW 
Bornassi, S
Keywords: Axial compressor blade
Multidisciplinary design optimization
Computational fluid dynamics (CFD)
Machine learning
Artificial neural network (ANN)
Design of experiments (DOE)
Issue Date: 2019
Publisher: Hong Kong Polytechnic University, Department of Civil and Structural Engineering
Source: Engineering applications of computational fluid mechanics, 1 Jan. 2019, v. 13, no. 1, p. 892-904 How to cite?
Journal: Engineering applications of computational fluid mechanics 
Abstract: 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 in this optimization process. Compressor characteristics involving mass flow and efficiency, and stress and eigenfrequencies of the blades are the main objectives of the evaluation. Owing to the design of blade attachments and their dynamic isolation from the disk, the vibrational behavior of the one blade is tuned based on the self-excited and forced vibration phenomenon. Using a semi-analytical MATLAB code instability, the conditions are satisfied. The code uses Whitehead's theory and force response theory to predict classical and stall flutter speeds. Forced vibrational instability is controlled using Campbell's theory. The aerodynamics of the new blade geometry is determined using multistage computational fluid dynamics simulation. The numerical results show increasing performance near the surge line and improvement in the working interval along with a 4% increase in mass flow. From the vibrational point of view, the reduced frequency increases by at least 5% in both stall and classical regions, and force response constraints are satisfied.
ISSN: 1994-2060
EISSN: 1997-003X
DOI: 10.1080/19942060.2019.1649196
Rights: © 2019 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group.This is an Open Access article distributed under the terms of the Creative Commons Attribution License (, which permits unrestricted use,distribution, and reproduction in any medium, provided the original work is properly cited.
The following publication Mohammad Ghalandari, Alireza Ziamolki, Amir Mosavi, Shahaboddin Shamshirband, Kwok-Wing Chau & Saeed Bornassi (2019) 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, Engineering Applications of Computational Fluid Mechanics, 13:1, 892-904 is available at
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