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Aero-Propulsive Modeling of Transport Aircraft for Air Traffic Management Applications
Abstract
Aerodynamic and propulsive modeling of current aircraft fleet is vital for Air Traffic Management applications, especially for use in decision support tools to predict aircraft trajectories and simulation tools. There are various models already in use or proposed. A different aerodynamic and propulsive modeling is presented in this paper. The new model considers the compressibility effects and non-linear variations of the drag polar. In addition, the thrust and specific fuel consumption models proposed consider variations of these quantities both with altitude and the Mach number. Copyright © 2004 by the American Institute of Aeronautics and Astronautics, Inc. All rights reserved.
... Adopting INFLT in its development, this model was applied to estimate the climbing phase of Boeing 737-400 and Learjet 60. Using AFM data, Cavcar and Cavcar [9] derived an APM to find the time-to-climb values for Boeing 737-400. Although both of these models include the compressibility and wing camber effects, they are not incorporating the compressible drag rise effect above the critical Mach number. ...
... Also, Gong and Chan used an engine scaling factor in their propulsive model rather than deriving an optimisation model to estimate the coefficients of an empirical formula for the engine thrust, taking into account flight altitude and Mach number effects [8]. Cavcar and Cavcar presented an empirical model for their propulsive model at various constant flight altitudes [9]. Baklacioglu and Cavcar [9] developed a genetic algorithm-(GA) based APM derived from Boeing 737-400 AFM data and capable of making climb and descent trajectory estimations. ...
... Cavcar and Cavcar presented an empirical model for their propulsive model at various constant flight altitudes [9]. Baklacioglu and Cavcar [9] developed a genetic algorithm-(GA) based APM derived from Boeing 737-400 AFM data and capable of making climb and descent trajectory estimations. Their propulsive model took into account both flight altitude and Mach number as input parameters simultaneously [6]. ...
This paper describes a reverse engineering methodology so as to accomplish an aero-propulsive modelling (APM) through implementing a drag polar estimation for a case study jet aircraft in case of the absence of the thrust data of the aircraft’s engine. Since the available thrust force can be replaced by the required thrust force for the sustained turn, this approach allows the elimination for the need of the thrust parameter in deriving an aero-propulsive model utilising equations of motion. Two different modelling approaches have been adopted: (i) implementing the 6-DOF model data for sustained turn and climb flight to achieve induced drag model; and then incorporating the glide data to obtain the total drag polar model; (ii) using the 6-DOF model data together with introducing the effect of C L - α dependency. The error assessments showed that the derived CSA models were able to predict the drag polar values accurately, providing linear correlation coefficient ( R ) values equal to 0.9982 and 0.9998 for the small α assumption and C L - α dependency, respectively. A direct comparison between the trimmed C D values of 6-DOF model and the values predicted by the CSA model was accomplished, which yielded highly satisfactory results within high subsonic and transonic C L values.
... For the stated reasons, there are different forecasting approaches in the literature to predict the fuel efficiency (Dinc et al., 2020), exhaust emission index (Van Hung et al., 2021) and fuel flow (FF) rate (Baklacioglu, 2016). With such approaches, for example, more accurate aircraft trajectory predictions can be calculated and more efficient fuel consumption can be achieved (Cavcar and Cavcar, 2004). To provide a more effective fuel consumption, some parameters are controlled during combustion in aircraft engines. ...
Purpose-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 takeoffs were used. As a result, a new hybrid convolutional neural network (CNN)-bi-directional long short term memory (BiLSTM) model was developed as intended. Design/methodology/approach-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. Findings-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 R 2 , 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 R 2 , RMSE and MAPE, respectively. In the proposed hybrid model when tested, values of 0.9743, 2.539 and 1.62 were obtained for R 2 , 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. Originality/value-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.
... For the stated reasons, there are different forecasting approaches in the literature to predict the fuel efficiency (Dinc et al., 2020), exhaust emission index (Van Hung et al., 2021) and fuel flow (FF) rate (Baklacioglu, 2016). With such approaches, for example, more accurate aircraft trajectory predictions can be calculated and more efficient fuel consumption can be achieved (Cavcar and Cavcar, 2004). To provide a more effective fuel consumption, some parameters are controlled during combustion in aircraft engines. ...
Purpose
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.
Design/methodology/approach
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.
Findings
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 R ² , 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 R ² , RMSE and MAPE, respectively. In the proposed hybrid model when tested, values of 0.9743, 2.539 and 1.62 were obtained for R ² , 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.
Originality/value
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.
The energy method, which deals with the total energy of the aircraft (sum of potential energy and kinetic energy), is frequently used in the climb analysis of high performance aircraft. Within the scope of this study, by using this energy approach; a new method is presented to obtain specific excess power contours (Ps), which show the performance limits of the aircraft, present the altitude and speed combinations at which they can fly at different specific excess powers, and help determine the trajectory corresponding to the minimum time to climb without the need for any mathematical operation. In the method presented for the B737-800 aircraft; aircraft performance model consisting of aerodynamic model, thrust, and fuel flow rate models was created and Ps contours implementing the energy maneuverability method were obtained by using this model. The data used in the study are real thrust and flight data record (FDR) data. For the models, the cuckoo search algorithm (CSA) method, which is relatively new but has proven itself in many challenging optimization problems, is used. Particle swarm optimization (PSO), a different metaheuristic method, was used to validate CSA models. In all of the optimization processes made using the Matlab program, very accurate results were accomplished with both metaheuristic methods.
To compute the most efficient route that the aircraft has to fly, the flight management system (FMS) needs a mathematical representation of the aircraft performance. However, after several years of operation, various factors can degrade the overall performance of the aircraft. Such degradation can affect the reliability of the aircraft model, and the crew would lose confidence in the fuel planning estimated by the FMS. This paper presents the results of a study in which a new adaptive algorithm is proposed for continuously updating the FMS performance model using cruise flight data. The proposed algorithm combines aircraft performance monitoring techniques with adaptive lookup tables to model the aerodynamic characteristics of the aircraft. The methodology was applied to the well-known Cessna Citation X business aircraft, for which a research aircraft flight simulator was available. The development of this methodology was accomplished by creating an initial performance model, adapting it using flight data in cruise, and finally comparing its prediction with a series of flight data collected with the flight simulator. Results have shown that the proposed methodology was able to reduce fuel flow prediction mean errors by about 5%, whereas the standard deviation was reduced by a factor of 3.4.
This Paper presents the validation studies results of an engine mathematical performance model identification for flight management system trajectory prediction and optimization applications. The methodology was applied to the Cessna Citation X business aircraft, for which the aircraft flight manual and the flight crew operating manual are available. In addition, another data source based on computerized trajectory was also used to generate several climb and descent flight profiles required in the engine model identification process. To demonstrate and further validate the accuracy of the proposed engine performance model, a level-D research aircraft flight simulator of the Cessna Citation X was used as a reference. According to the Federal Aviation Administration (FAA, AC 120-40B), level D corresponds to the highest qualification level for the flight dynamics and engine modeling. Validation of the methodology was accomplished by comparing the prediction model with a series of flight data collected with the flight simulator for different flight conditions and different flight phases including takeoff, climb, cruise, and idle descent. Comparison results were validated with a tolerance of ±5% for each engine performance predicted by the model in terms of fan speed, core speed, thrust, and fuel flow.
The requirement for an accurate engine thrust model has a major antecedence in airline fuel saving programs, assessment of environmental effects of fuel consumption, emissions reduction studies, and air traffic management applications. In this study, utilizing engine manufacturers’ real data, a metaheuristic model based on genetic algorithms (GAs) and a machine learning model based on neural networks (NNs) trained with Levenberg-Marquardt (LM), delta-bar-delta (DBD), and conjugate gradient (CG) algorithms were accomplished to incorporate the effect of both flight altitude and Mach number in the estimation of thrust. For the GA model, the analysis of population size impact on the model’s accuracy and effect of number of data on model coefficients were also performed. For the NN model, design of optimum topology was searched for one- and two-hidden-layer networks. Predicted thrust values presented a close agreement with real thrust data for both models, among which LM trained NNs gave the best accuracies.
In this study, a new aero-propulsive model (APM) was derived from the flight manual data of a transport aircraft using Genetic Algorithms (GAs) to perform accurate trajectory predictions. This new GA-based APM provided several improvements to the existing models. The use of GAs enhanced the accuracy of both propulsive and aerodynamic modelling. The effect of compressible drag rise above the critical Mach number, which was not included in previous models, was considered along with the effects of compressibility and profile camber in the aerodynamic model. Consideration of the thrust dependency with respect to Mach number and the altitude in the propulsive model expression was observed to be a more practical approach. The proposed GA model successfully predicted the trajectory for the descent phase, as well, which was not possible in previous models. Close agreement was observed when comparing the time to climb and time to descent values obtained from the model with the flight manual data.
The aviation environmental impact, including emissions and noise, has increasing importance in the design and operation of commercial aircrafts. The evolution of aircraft design has the potential to significantly improve flight efficiency, but it represents only a long-term solution, whereas improving flight procedures can contribute to the reduction of environmental impact for current and future aircrafts in a shorter time scale. Trajectory optimization methods have been successfully used to minimize pollutant emissions during departure and arrivals, and noise over communities near the airports. In this context, a trajectory optimization framework has been developed, capable of finding flight paths that minimize emissions, flight costs and noise impact while taking into account operational restrictions. This can become a very powerful tool both for policy makers when studying departure and arrival routes, and for pilots, when the next generation of air traffic management systems, studied by SESAR and NextGen, will allow the aircraft to have greater freedom in defining its own flight path. This work introduces this framework and further focus on a numerical benchmark against recorded ADS-B flights departing from the Frankfurt airport. Based on estimated performance data, new optimized trajectories are calculated for each original flight. Analysis of the suggested departure procedures showed improvements in the order of 11% for sleep disturbance by only optimizing the vertical flight profile, and up to 21% in aircraft gaseous emissions. © 2012 by the American Institute of Aeronautics and Astronautics, Inc. All rights reserved.
Approximate cruise range solutions are introduced for the constant altitude – constant high subsonic speed flight of turbojet/fan transport aircraft with cambered wing design. The variation of the specific fuel consumption with Mach number is also considered in derivation of the approximate solutions. The method aims at estimation of the cruise range of aircraft during conceptual or preliminary design phase. An application of the solutions is also presented.
The Center/TRACON Automaton System (CTAS) is a set of air traffic management tools developed by NASA in conjunction with the FAA. As part of its functionality, CTAS predicts aircraft flight trajectories using aero- propulsive models and the kinetic equations of motion for various flight conditions including climbs. Precise aero- propulsive models for all aircraft types are not yet available to NASA researchers. In an effort to improve climb trajectory prediction of jet aircraft for which CTAS does not have a precise aero-propulsive model, a technique was developed to derive an aero-propulsive model from readily available time-to-climb data found in flight manuals. A case study was performed on a Boeing 737-400, for which time-to-climb data and aero-propulsive model data were known. A new aero-propulsive model, identified by three aerodynamic and one propulsive parameter, was derived from the time-to-climb data. The results showed it was possible to derive an aero-propulsive model for an aircraft type that will allow CTAS to compute time-to-climb for a range of climb speeds that agree closely with known data. This technique was then applied to a Learjet 60, an aircraft type for which a precise aero-propulsive model is not available. A comparison of top-of-climb predictions made with a derived aero-propulsive model and actual top-of- climb from Learjet 60 radar track data reveal close agreement.
Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Aeronautics and Astronautics, 2001. Includes bibliographical references (p. 95-96). by Raffi Babikian. S.M.
MITRE HAS DEVELOPED AN ALGORITHM FOR ESTIMATING THE FUEL CONSUMPTION OF COMMERCIAL JET AIRCRAFT FROM PATH PROFILE DATA.THIS PERMITS EASY EVALUATION OF NEW ATC PROCEDURES WITHOUT LABORIOUS AND EXPENSIVE COMPUTER PROGRAMS OR REAL-TIME TESTING. THE APPROACH TO THIS ALGORITHMS IS DETAILED IN THIS PAPER. IT DERIVES FROM BASIC CONCEPTS OF ENERGY BALANCE. IT IS BASED UPON GENERALLY KNOWN ASSUMPTIONS AND APPROXIMATIONS FORCOMMERCIAL JET OPERATIONS. THE ALGORITHM HAS BEEN USED SUCCESSFULLY IN SEVERAL STUDIES OF ATC PROCEDURES. DETAILED VERIFICATION BY COMPARISON TO ACTUAL AIRCRAFT RADAR TRACKING ANDCOCKPIT INSTRUMENT DATA IS UNDERWAY.
Aircraft Engine Performance Parameters
- J Mattingly
Mattingly, J.D., " Aircraft Engine Performance Parameters ", URL: http://www.aircraftenginedesign.com/abe_right5.html [cited 11 Aug. 2004] 18 " JT15D-5 Fact Sheet, " Pratt & Whitney Canada Inc., Longueuil, Quebec, 1988. 19 Eurocontrol, Base of Aircraft Data (BADA), Revision 3.5, Bretigny, July 2003. 20 Jane's All the World's Aircraft 1990-1991, Ed. Lambert, M., Jane's Information Group Ltd., London, 1990. 21 " CFM56-3 Technology, " CFM International, URL: http://www.cfm56.com/engines/cfm56-3/tech.html [cited 11 Aug. 2004]
AirplaneDesignPartVI:PreliminaryCalculationofAerodynamic,ThrustandPowerCharacteristics,Ottawa, Roskam Aviation andEngineering Corporation
- Roskamj