Propulsive modelling for JT9D-3, JT15D-4C and TF-30 turbofan engines using particle swarm optimization

Abstract

<jats:sec> <jats:title content-type="abstract-subheading">Purpose</jats:title> <jats:p>The purpose of this paper is to create high-accuracy thrust modelling for cruise flight using particle swarm optimization (PSO) algorithm.</jats:p> </jats:sec> <jats:sec> <jats:title content-type="abstract-subheading">Design/methodology/approach</jats:title> <jats:p>In this study, using PSO, new thrust models with high accuracy for the cruise flight stages of Pratt &amp; 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.</jats:p> </jats:sec> <jats:sec> <jats:title content-type="abstract-subheading">Findings</jats:title> <jats:p>It was observed that the model created predicted real thrust values with high precision.</jats:p> </jats:sec> <jats:sec> <jats:title content-type="abstract-subheading">Practical implications</jats:title> <jats:p>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.</jats:p> </jats:sec> <jats:sec> <jats:title content-type="abstract-subheading">Originality/value</jats:title> <jats:p>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.</jats:p> </jats:sec>

Fuel flow rate modeling for descent using cuckoo search algorithm: a case study for point merge system procedure at Istanbul airport

“…Namely, the next position of each bird/particle in the flock is found by adding the velocity vector to its current position. In this way, the algorithm tries to reach the optimum by constantly updating the position of the particles in the flock (Engelbrecht, 2007; Shi, 2001; Oruc and Baklacioglu, 2020b; Oruc et al , 2020). One of the biggest disadvantages of PSO is that it is very much affected by parameter change.…”

JT9D Engine Thrust Estimation and Model Sensitivity Analysis Using Gradient Boosting Regression Method

“…Zhou and Zhang presented a study report on engine thrust estimation using an ensemble of improved wavelet extreme learning machines to satisfy the conditions of direct thrust control [10]. In addition, Oruc and Bakircioglu used a particle swarm optimization algorithm to model JT9D engine thrust; in this study, the investigators found high-accuracy thrust values during cruise flights [11]. A study was proposed to investigate the application of artificial neural networks in accurately predicting aircraft thrust by leveraging the capability of ANNs to learn from large datasets [12].…”

Optimization and off-design calculations of a turbojet engine using the hybrid ant colony – particle swarm optimization method

“…Performance calculations of turbojet engines are well-known, but accurate calculations require detailed information about the components (Mattingly, 2002; Cohen et al , 1995; Walsh and Fletcher, 1998; Reffold, 1995; Kurzke, 1995). Some researchers used regression methods for modeling thrust and fuel flow consumption (Baklacioglu, 2015a, 2015b, 2016, 2017, 2021; Piskin et al , 2019; Oruc and Baklacioglu, 2020a, 2020b, 2021; Oruc et al , 2020) without going internal component details. There are also many optimization studies performed at on-design conditions.…”