Multi-objective optimization of a small turbojet engine energetic performance

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dc.authoridAYGUN, HAKAN/0000-0001-9064-9644
dc.authoridKIRMIZI, Mehmet/0000-0003-0510-2981
dc.authoridTuran, Onder/0000-0003-0303-4313
dc.authorscopusid57205697037
dc.authorscopusid57506776400
dc.authorscopusid37461539100
dc.authorscopusid54394470800
dc.authorwosidAYGUN, HAKAN/W-9502-2018
dc.contributor.authorAygun, Hakan
dc.contributor.authorKirmizi, Mehmet
dc.contributor.authorKilic, Ulas
dc.contributor.authorTuran, Onder
dc.date.accessioned2024-08-25T18:36:10Z
dc.date.available2024-08-25T18:36:10Z
dc.date.issued2023
dc.departmentEge Üniversitesien_US
dc.description.abstractApplication fields of small turbojet engines (STJE) have been increasing day by day due to their superior features such as high power to weight ratio and reliability. In this study, parametric cycle analysis peculiar to STJE is implemented for different design variables such as compressor pressure ratio (CPR), turbine inlet temperature (TIT) as well as ambient temperature (T0). Based on these evaluations, several performance metrics of STJE are dealt with together by applying three different methods such as multi-objective genetic algorithm (MOGA), particle swarm optimization (MOPSO) and grey wolf optimization (MOGWO) under five analyses. According to performance analyses, net thrust of the STJE has improvement from 3.2 kN to 5.41 kN due to the increased TIT whereas it deteriorates from 4.87 kN to 4.67 kN due to the elevated CPR. However, with effect of the higher TIT, specific fuel consumption (SFC) of the STJE ascends from 42.96 g/kNs to 49.04 g/kNs while it diminishes from 39.57 g/kNs to 31.5 g/kNs owing to the higher CPR. The higher T0 leads net thrust to lower but the higher SFC. According to optimization findings at fourth analysis, the lower SFC is obtained with 31.51 g/kNs by MOGA than the other methods where SFC is 33.11 g/kNs whereas the higher net thrust is obtained with 6.209 kN by both MOPSO and MOGWO than the findings of MOGA where net thrust is 4.68 kN. When considering five optimization analyses, the findings of MOGA, MOGWO and MOPSO could be utilized depending on aircraft mission that turbojet engine requires to perform. It is thought that performing of multi-objective optimization could help in designing turbojet engines to the engineers.en_US
dc.identifier.doi10.1016/j.energy.2023.126983
dc.identifier.issn0360-5442
dc.identifier.issn1873-6785
dc.identifier.scopus2-s2.0-85148677055en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.urihttps://doi.org/10.1016/j.energy.2023.126983
dc.identifier.urihttps://hdl.handle.net/11454/100537
dc.identifier.volume271en_US
dc.identifier.wosWOS:000948024700001en_US
dc.identifier.wosqualityQ1en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherPergamon-Elsevier Science Ltden_US
dc.relation.ispartofEnergyen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.snmz20240825_Gen_US
dc.subjectGas turbine engineen_US
dc.subjectMulti -objective optimizationen_US
dc.subjectGenetic algorithmen_US
dc.subjectParticle swarm optimizationen_US
dc.subjectGrey wolf optimizationen_US
dc.subjectExergy Analysisen_US
dc.subjectAlgorithmsen_US
dc.subjectAircraften_US
dc.titleMulti-objective optimization of a small turbojet engine energetic performanceen_US
dc.typeArticleen_US

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