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Optimized modeling of energy and environmental metrics of mixed flow turbofan engine used regional aircraft

As a kind of gas turbine engines, turbofan engines have powered a number of aero-vehicles in aviation sector. The necessity of turbofan with higher energy efficiency has been greatly drawn attention since these are operating dependent to fossil fuels. In this study, energy and emission metrics of fi...

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Autores principales: UÇAR, Ukbe Usame, Aygun, Hakan, Tanyeri, Burak
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer International Publishing 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9986672/
https://www.ncbi.nlm.nih.gov/pubmed/37122584
http://dx.doi.org/10.1007/s10973-023-11996-7
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author UÇAR, Ukbe Usame
Aygun, Hakan
Tanyeri, Burak
author_facet UÇAR, Ukbe Usame
Aygun, Hakan
Tanyeri, Burak
author_sort UÇAR, Ukbe Usame
collection PubMed
description As a kind of gas turbine engines, turbofan engines have powered a number of aero-vehicles in aviation sector. The necessity of turbofan with higher energy efficiency has been greatly drawn attention since these are operating dependent to fossil fuels. In this study, energy and emission metrics of fifty-one mixed flow turbofan engines (MFTE) with different bypass ratio, overall pressure ratio and fuel flow are modeled with multi-regression (MR) method. The obtained models are subjected to metaheuristic approaches involving genetic algorithm (GA) and simulated annealing (SA) so as to decrease error of the models. According to MR findings, rated thrust of MFTEs is estimated with 1.4877 of minimum square error (MSE) whereas GA and SA make it lower as 1.3404 and 1.2524, respectively. On the other hand, NO(x) emission index of MFTEs is predicted with relatively low coefficient of determination (R(2)) as 0.8620. However, its accuracy is enhanced to 0.8633 (with GA) and 0.8655 (with SA). Finally, exergy efficiency of MFTEs is estimated the highest model correctness with GA. Namely, R(2) of the model is computed as 0.9280 with GA and 0.9277 with SA. Without applying these methods, its R(2) is obtained as 0.9263 with MR. When considering these outcomes, thanks to modeling and optimization methods, prediction of performance and emission indexes of mixed flow turbofan engines could be performed with lower error values. It is thought that the study helps in prediction of environmental effect regarding turbofan engines that are utilized at busy airports.
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spelling pubmed-99866722023-03-06 Optimized modeling of energy and environmental metrics of mixed flow turbofan engine used regional aircraft UÇAR, Ukbe Usame Aygun, Hakan Tanyeri, Burak J Therm Anal Calorim Article As a kind of gas turbine engines, turbofan engines have powered a number of aero-vehicles in aviation sector. The necessity of turbofan with higher energy efficiency has been greatly drawn attention since these are operating dependent to fossil fuels. In this study, energy and emission metrics of fifty-one mixed flow turbofan engines (MFTE) with different bypass ratio, overall pressure ratio and fuel flow are modeled with multi-regression (MR) method. The obtained models are subjected to metaheuristic approaches involving genetic algorithm (GA) and simulated annealing (SA) so as to decrease error of the models. According to MR findings, rated thrust of MFTEs is estimated with 1.4877 of minimum square error (MSE) whereas GA and SA make it lower as 1.3404 and 1.2524, respectively. On the other hand, NO(x) emission index of MFTEs is predicted with relatively low coefficient of determination (R(2)) as 0.8620. However, its accuracy is enhanced to 0.8633 (with GA) and 0.8655 (with SA). Finally, exergy efficiency of MFTEs is estimated the highest model correctness with GA. Namely, R(2) of the model is computed as 0.9280 with GA and 0.9277 with SA. Without applying these methods, its R(2) is obtained as 0.9263 with MR. When considering these outcomes, thanks to modeling and optimization methods, prediction of performance and emission indexes of mixed flow turbofan engines could be performed with lower error values. It is thought that the study helps in prediction of environmental effect regarding turbofan engines that are utilized at busy airports. Springer International Publishing 2023-03-06 2023 /pmc/articles/PMC9986672/ /pubmed/37122584 http://dx.doi.org/10.1007/s10973-023-11996-7 Text en © Akadémiai Kiadó, Budapest, Hungary 2023, Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic.
spellingShingle Article
UÇAR, Ukbe Usame
Aygun, Hakan
Tanyeri, Burak
Optimized modeling of energy and environmental metrics of mixed flow turbofan engine used regional aircraft
title Optimized modeling of energy and environmental metrics of mixed flow turbofan engine used regional aircraft
title_full Optimized modeling of energy and environmental metrics of mixed flow turbofan engine used regional aircraft
title_fullStr Optimized modeling of energy and environmental metrics of mixed flow turbofan engine used regional aircraft
title_full_unstemmed Optimized modeling of energy and environmental metrics of mixed flow turbofan engine used regional aircraft
title_short Optimized modeling of energy and environmental metrics of mixed flow turbofan engine used regional aircraft
title_sort optimized modeling of energy and environmental metrics of mixed flow turbofan engine used regional aircraft
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9986672/
https://www.ncbi.nlm.nih.gov/pubmed/37122584
http://dx.doi.org/10.1007/s10973-023-11996-7
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