Oruc R., Baklacioglu T.Oruc, R, Baklacioglu, T2023-05-092023-05-092020-06-082020.01.011748-8842https://hdl.handle.net/20.500.12597/13334Purpose: The purpose of this paper is to create high-accuracy thrust modelling for cruise flight using particle swarm optimization (PSO) algorithm. Design/methodology/approach: In this study, using PSO, new thrust models with high accuracy for the cruise flight stages of Pratt & Whitney JT9D-3, JT15D-4C and TF-30 engines were created. For this aim, real Mach number, flight altitude and thrust values taken from the engine manufacturers were used. In the model, thrust is given as a function of altitude and Mach number. The sensitivity of the results given by the PSO thrust model has been examined using several different error types. Finally, the effect of some PSO parameters on the created models is examined. Findings: It was observed that the model created predicted real thrust values with high precision. Practical implications: The PSO thrust model can be used in the trajectory estimates of today’s aircraft with the use of accurate scaling factors. In addition, using the developed PSO thrust model together with a correct aerodynamic model provides more effective management of air traffic flow in air traffic management applications. Combining the PSO model with fuel flow-rate models will significantly increase engine efficiency and performance; thus, making a major contribution to reducing engine emissions. Originality/value: The originality of this study is that it is the first thrust modelling made with PSO in the literature for turbofan engines. The use of real data in the study and the creation of models for several different turbofan engines are important for the reliability of thrust models.falseAircraft | Cruise flight | Particle swarm optimization | Thrust prediction | Turbofan enginePropulsive modelling for JT9D-3, JT15D-4C and TF-30 turbofan engines using particle swarm optimizationPropulsive modelling for JT9D-3, JT15D-4C and TF-30 turbofan engines using particle swarm optimizationArticle10.1108/AEAT-02-2020-003110.1108/AEAT-02-2020-00312-s2.0-85085023669WOS:000533901600001939946921758-4213