Prediction of emission and exergy parameters of commercial high by-pass turbofan engines based on CSA-SVR model

Abstract This paper presents a new method for estimating emissions and exergy from aircraft engines. In this context, a combination of the support vector regression (SVR) method, which is quite successful in solving complex problems, and the cuckoo search algorithm (CSA) method, which is relatively new but frequently used in many areas, was used. In the study, exergy analyses of the engines of the aircraft most commonly used in today's air transportation were also performed. International Civil Aviation Organization (ICAO) emission databank data were used in the analyses made for the approach phase of the flight. Exergetic parameters (environmental effect factor, exergy efficiency and waste exergy ratio) obtained for 165 different turbofan engines and emission indices taken from ICAO databank for these engines were modeled according to 4 different engine performance parameters. These parameters are bypass ratio, overall pressure ratio, combustion type and rated thrust. As a result of the analyses, it was seen that the CSA-SVR model predicted the real data quite accurately. Namely, the minimum coefficient of determination ( R 2 ) value for emission parameters was 0.8407, this value for fuel flow rate was 0.9792, and the minimum R 2 value for exergy parameters was 0.8939. From this perspective, it is thought that the CSA-SVR model will help studies on estimating environmental impacts in areas close to the airport.