Modelling of fuel flow-rate of commercial aircraft for the climbing flight using cuckoo search algorithm

Abstract

Purpose The purpose of this study is to create a new fuel flow rate model adopting cuckoo search algorithm (CSA) for the climbing phase of the flight. Design/methodology/approach Using the real flight data records (FDRs) of B737-800 passenger aircraft, a new fuel flow rate model for the climbing phase of the flight was developed by incorporating CSA. In the model, fuel flow rate is given as a function of altitude and true airspeed. The aim is to create a model that yields results that are closest to the real fuel flow rate values obtained from flight data records. Various error analysis methods were used to test the accuracy of the obtained values. Finally, the effect of change of some CSA parameters on the model was investigated. Findings It was observed that the derived model is able to predict real fuel flow rate values with high accuracy. It has been deduced that increasing the number of nest (n) and discovery rate of alien nests (pa) values of CSA parameters to a certain value gradually decreases the model’s accuracy. Practical implications This model is considered to be useful in air traffic management decision support systems, simulation applications, aircraft trajectory prediction models and aircraft performance modelling studies because of the high accuracy accomplished by the CSA model. Originality/value The originality of this study is the development of a new fuel flow rate model using CSA as a first attempt in the literature. The use of real flight data is important for the originality and reliability of the model.

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

“… Each cuckoo lays one egg at a time in a randomly selected nest. The best nests with high-quality eggs from these eggs will be carried over to the next generations. The number of available host nests is fixed; the egg laid will be found by the host bird with a probability between 0 and 1. If the host bird finds the cuckoo egg, it will either get rid of the egg or leave its current nest to build a new nest (Oruc and Baklacioglu, 2020a; Yang and Deb, 2014; Joshi et al , 2017; Walton et al , 2011; Yang and Deb, 2009). …”

“…The number of available host nests is fixed; the egg laid will be found by the host bird with a probability between 0 and 1. If the host bird finds the cuckoo egg, it will either get rid of the egg or leave its current nest to build a new nest (Oruc and Baklacioglu, 2020a;Yang and Deb, 2014;Joshi et al, 2017;Walton et al, 2011;Yang and Deb, 2009).…”

“…There are many studies on this subject in the literature due to the importance of fuel consumption. In these studies, there are models that estimate fuel consumption for all flight phases (climb, descent and cruise), as well as models made for only one flight phase (Baklacioglu, 2015a; Baklacioglu, 2016; Baklacioglu, 2021; Collins, 1982; Oruc and Baklacioglu, 2020a; Sahin and Agayeva, 2018; Trani et al , 2004; Turgut and Rosen, 2012) along with related thrust modeling studies (Baklacioglu, 2017; Baklacioglu, 2015b). In addition, there are many studies conducted to optimize fuel consumption and emissions for different aircraft engine types.…”

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.…”

Modeling of fuel flow-rate of commercial aircraft for the descent flight using particle swarm optimization

“…Trani et al (2004) developed an artificial neural network model that predicts aircraft fuel consumption by using Fokker F-100 aircraft performance manual data. For the climbing phase of the flight, Baklacioglu (2015) developed the genetic algorithm model, while Oruc and Baklacioglu (2020) developed the cuckoo search algorithm model. Different empirical formulas were found in the studies conducted.…”