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Continuous descent approach (CDA) is a method, which allows the aircraft flying its individual optimal vertical profile down to runway threshold with engines operating at low‐thrust power. The main objective of this paper is to provide less‐fuel consumption, less noise and less emission with using CDA procedures instead of conventional procedures.

Conventional and CDA procedures were modelled in the Istanbul terminal area (TMA), which has five entry points. The real speed and the real altitude limitations were maintained on these entry points. System for Assessing Aviation's Global Emissions research results were also used to determine the emission savings.

With CDA procedures, more than 40 kg fuel and 2 min time savings per flight are obtained; furthermore, regarding CO₂ and H₂O, significant emission savings are also noted.

Some of the benefits of CDA procedures are reported for Istanbul TMA by using true flight data.

Metroplex is a system of two or more airports, in physical proximity, with highly interdependent arrival and departure operations. The purpose of this study is the construction of an efficient and effective air route model based on the point merge system (PMS) to reduce aircraft fuel consumption and CO₂ emissions for three metroplex airports in Istanbul terminal control area (TMA).

A PMS arrival route model is constructed for metroplex airports. In the proposed model, two situations are taken into consideration: for delay which can be defined as flying on sequencing legs (PMS_del) and for no delay (PMS_{no del}). An empirical model is developed using a data set including the flight data records of ten actual B737-800 domestic flights. With this empirical model, both the baseline and the PMS models (PMS_del and PMS_{no del}) are compared in terms of fuel consumption, CO₂ emissions and flight distance and time as a theoretical computation.

In the proposed PMS_{no del} arrival route model, according to different entry points for Istanbul Ataturk International Airport (LTBA), the analyses show an average reduction of 26 per cent in flight time, 24.5 per cent in flight distance, 17 per cent in fuel burned and CO₂ emissions; in addition, for Sabiha Gökcen International Airport (LTFJ) there are 34, 23 and 32 per cent average savings for flight time, flight distance and fuel burned together with CO₂ emissions obtained, respectively. Even if the PMS_del model, for LTFJ except only one entry point, for LTBA except two entry points, better results are obtained than baseline.

The point merge model for metroplex airports in this paper can be applied by airspace designers and Air Navigation Service Providers to perform efficient and effective arrival routes.

In this study, a point merge model is constructed for metroplex airports. Quantitative results, using an empirical model, are achieved in terms of fuel consumption, CO₂ emissions and flight distance and time at metroplex airports.

This paper aims to investigate the effects of descent time spent with flaps extended on fuel burn (FB) and specific range for five different flight path angles (FPAs) ranging between 2.0° and 4.0° for a commercial aircraft.

A large data set of actual flight data (n = 475) of the same type of a frequently used commercial aircraft were investigated by using statistical methods.

The result of the comparison of the highest and the lowest FBs of flight profiles for each FPAs present that the fuel saving was achieved by keeping at as a high airspeed as possible and deploying flaps as late as possible, which is in line with the objective of delayed deceleration approaches. From analyzing the flight profiles, it was proven that delaying deceleration and also descending without flaps or with flap over a shorter time resulted in less FB of 101.1, 70.9 and 94.9 kg for FPA 2.5°, FPA 3.0° and FPA 3.5°, respectively.

This study differs from prior studies because it focused on the effects of the different vertical profiles on FB. Also, the use of real flight data recorder data in the analysis presents the originality of this study.

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 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 design a new standard instrument arrival called the point merge system (PMS) for converging runways. The PMS enables controllers to handle traffic with no heading instruction, as well as aiming to reduce a controller's frequency occupancy time.

The point merge model was designed for converging runways. Istanbul International Ataturk Airport, which has converging runways, was chosen as an application area for this model. The same 50 traffic arrivals per hour were used both for point merge and vectoring. Implementation was compared using a real time simulation.

The simulation results show that the total average number of instructions is about 33 per cent less and the frequency occupancy is about 37 per cent less for point merge than for vectoring. In addition, in terms of trajectory dispersion, in point merge, traffic is within a narrower triangular area, while in vectoring large traffic dispersion occurs.

The point merge model for converging runways proposed in this paper can be applied by airspace designers and air navigation service providers to perform efficient standard instrument arrival routes.

The PMS has been developed for single and parallel runways; however, in this study, the point merge model is designed for converging runways at Istanbul International Ataturk Airport.

The purpose of this study is to develop and test a new deep learning model to predict aircraft fuel consumption. For this purpose, real data obtained from different landings and take-offs were used. As a result, a new hybrid convolutional neural network (CNN)-bi-directional long short term memory (BiLSTM) model was developed as intended.

The data used are divided into training and testing according to the k-fold 5 value. In this study, 13 different parameters were used together as input parameters. Fuel consumption was used as the output parameter. Thus, the effect of many input parameters on fuel flow was modeled simultaneously using the deep learning method in this study. In addition, the developed hybrid model was compared with the existing deep learning models long short term memory (LSTM) and BiLSTM.

In this study, when tested with LSTM, one of the existing deep learning models, values of 0.9162, 6.476, and 5.76 were obtained for R², root mean square error (RMSE), and mean absolute percentage error (MAPE), respectively. For the BiLSTM model when tested, values of 0.9471, 5.847 and 4.62 were obtained for R², RMSE and MAPE, respectively. In the proposed hybrid model when tested, values of 0.9743, 2.539 and 1.62 were obtained for R², RMSE and MAPE, respectively. The results obtained according to the LSTM and BiLSTM models are much closer to the actual fuel consumption values. The error of the models used was verified against the actual fuel flow reports, and an average absolute percent error value of less than 2% was obtained.

In this study, a new hybrid CNN-BiLSTM model is proposed. The proposed model is trained and tested with real flight data for fuel consumption estimation. As a result of the test, it is seen that it gives much better results than the LSTM and BiLSTM methods found in the literature. For this reason, it can be used in many different engine types and applications in different fields, especially the turboprop engine used in the study. Because it can be applied to different engines than the engine type used in the study, it can be easily integrated into many simulation models.