Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/81645
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dc.contributorDepartment of Civil and Environmental Engineering-
dc.creatorGhalandari, M-
dc.creatorZiamolki, A-
dc.creatorMosavi, A-
dc.creatorShamshirband, S-
dc.creatorChau, KW-
dc.creatorBornassi, S-
dc.date.accessioned2020-02-10T12:28:23Z-
dc.date.available2020-02-10T12:28:23Z-
dc.identifier.issn1994-2060-
dc.identifier.urihttp://hdl.handle.net/10397/81645-
dc.language.isoenen_US
dc.publisherHong Kong Polytechnic University, Department of Civil and Structural Engineeringen_US
dc.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 (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use,distribution, and reproduction in any medium, provided the original work is properly cited.en_US
dc.rightsThe 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 https://dx.doi.org/10.1080/19942060.2019.1649196en_US
dc.subjectAxial compressor bladeen_US
dc.subjectAeroelasticityen_US
dc.subjectMultidisciplinary design optimizationen_US
dc.subjectComputational fluid dynamics (CFD)en_US
dc.subjectMachine learningen_US
dc.subjectArtificial neural network (ANN)en_US
dc.subjectDesign of experiments (DOE)en_US
dc.titleAeromechanical optimization of first row compressor test stand blades using a hybrid machine learning model of genetic algorithm, artificial neural networks and design of experimentsen_US
dc.typeJournal/Magazine Articleen_US
dc.identifier.spage892-
dc.identifier.epage904-
dc.identifier.volume13-
dc.identifier.issue1-
dc.identifier.doi10.1080/19942060.2019.1649196-
dcterms.abstractIn 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.-
dcterms.accessRightsopen accessen_US
dcterms.bibliographicCitationEngineering applications of computational fluid mechanics, 1 Jan. 2019, v. 13, no. 1, p. 892-904-
dcterms.isPartOfEngineering applications of computational fluid mechanics-
dcterms.issued2019-
dc.identifier.isiWOS:000481449000001-
dc.identifier.eissn1997-003X-
dc.description.validate202002 bcrc-
dc.description.oaVersion of Recorden_US
dc.identifier.FolderNumberOA_Scopus/WOSen_US
dc.description.pubStatusPublisheden_US
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