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The purpose of this paper is to create a new fuel flow rate model using cuckoo search algorithm (CSA) for the descending stage of the flight.

Using the actual flight data record data of the B737-800 aircraft, a new fuel flow rate model has been developed for this aircraft type. The created model is to predict the fuel flow rate with high accuracy depending on the altitude and true airspeed. In addition, the CSA fuel flow rate model was used to calculate the fuel consumption for the point merge system, which is used for combining the initial approach to the final approach at Istanbul Airport, the largest airport of Turkey.

As a result of the analysis, the correlation coefficient value is found as 0.996858 for Flight 1, 0.998548 for Flight 2, 0.995363 and 0.997351 for Flight 3 and Flight 4, respectively. The values that are so close to 1 indicate that the model predicts the real fuel flow rate data with high accuracy.

This model is considered to be useful in air traffic management decision support systems, aircraft performance models, models used for trajectory prediction and strategies used by the aviation community to reduce fuel consumption and related emissions.

The importance of this study lies in the fact that to the best of the authors’ knowledge, it is the first fuel flow rate model developed using CSA for the descent stage in the existing literature; the data set used is real values.

The purpose of this paper is to create models that predict exergetic sustainability index (ESI) and environmental effect factor (EEF) values with high accuracy according to various engine parameters.

In this study, models were created to estimate ESI and EEF sustainability parameters in various flight phases for a business jet with a turboprop engine using the cuckoo search algorithm (CSA) method. The database used for modeling includes the various engine parameters (torque, engine airflow, gas generator speed, fuel mass flow, power and air-fuel ratio) obtained by running a business aircraft engine more than once at different settings and the actual ESI and EEF values obtained depending on these parameters. In addition, sensitivity analysis was performed to measure the effect of engine parameters on the models. Finally, the effect of the CSA number of nest (n) parameter on the model accuracy was investigated.

It has been observed that the models predict ESI and EEF values with high accuracy. As a result of the sensitivity analysis, it was seen that the air-fuel ratio had a greater effect on the output parameters.

These models are thought to assist in the exergetic environment analysis used to find the greatest losses for turboprop business jets and identify their causes and further improve system performance. Thus, they will be a useful tool to minimize the negative impact of business jet on environmental sustainability.

To the best of the authors’ knowledge, this study stands out in the literature because it is the first exergo-metaheuristic approach developed with CSA for business aircraft engine; moreover, the data set used consists of real values.

The purpose of this paper is to create a new fuel flow rate model for the descent phase of the flight using particle swarm optimization (PSO).

A new fuel flow rate model was developed for the descent phase of the B737-800 aircraft, which is frequently used in commercial air transport using PSO method. For the analysis, the actual flight data records (FDRs) data containing the fuel flow rate, speed, altitude, engine speed, time and many more data were used. In this regard, an empirical formula has been created that gives real fuel flow rate values as a function of altitude and true airspeed. In addition, in the fuel flow rate predictions made for the descent phase of the specified aircraft, a different model has been created that can be used without any optimization process when FDR data are not available for a specific aircraft take-off weight condition.

The error analysis applied to the models showed that both models predict real fuel flow rate values with high precision.

Because of the high accuracy of the PSO model, it is thought to be useful in air traffic management, decision support systems, models used for trajectory prediction, aircraft performance models, strategies used to reduce fuel consumption and emissions because of fuel consumption.

This study is the first fuel flow rate model for descent flight using PSO algorithm. The use of real FDR data in the analysis shows the originality of this study.

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.

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.

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.

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.

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.

The purpose of this paper is to create high-accuracy thrust modelling for cruise flight using particle swarm optimization (PSO) algorithm.

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.

It was observed that the model created predicted real thrust values with high precision.

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.

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.