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BSO algorithm and artificial neural network aided emission optimisation for gas turbine engine

Published online by Cambridge University Press:  11 November 2024

M. Konar Open the ORCID record for M. Konar [Opens in a new window] ,
O. Cam Open the ORCID record for O. Cam [Opens in a new window]  and
M. O. Aktaş
M. Konar*
Affiliation:
Faculty of Aeronautics and Astronautics, Erciyes University, Kayseri, Türkiye
O. Cam
Affiliation:
Ali Cavit Celebioglu Civil Aviation College, Erzincan Binali Yildirim University, Erzincan, Türkiye
M. O. Aktaş
Affiliation:
Air NCO Vocational Higher School, National Defense University, Izmir, Türkiye
*
Corresponding author: M. Konar; Email: [email protected]
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Abstract

Aircraft play a major role in meeting the fast and efficient transportation needs of modern society, thanks to their advanced features. However, gas turbine engines used in aircraft have many negative effects on human health. One of the negative effects is the exhaust gases released by these engines to nature. In this study, it is discussed to present alternative models based on heuristic methods to reduce the emission values of the synthetic fuel mixture used in the combustion chamber of gas turbine engines. For this purpose, a model based on artificial neural networks (ANN) based on the back-tracking search optimisation (BSO) algorithm is proposed by using experimentally obtained emission values found in the literature. In the proposed model, the parameters of the optimum ANN structure are first determined by the BSO algorithm. Then, by using the optimum ANN structure, the most appropriate input values were found with the BSO algorithm, and the emission values were reduced. The simulation results have shown that the proposed method will be a fast and safe alternative method for reducing emission values.

Keywords

gas turbine enginesback-tracking search optimisation algorithmoptimisationartificial neural networks
Type
Research Article
Information
The Aeronautical Journal , Volume 129 , Issue 1331 , January 2025 , pp. 107 - 122
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Copyright
© The Author(s), 2024. Published by Cambridge University Press on behalf of Royal Aeronautical Society

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