Hostname: page-component-cd9895bd7-gxg78 Total loading time: 0 Render date: 2024-12-22T20:24:15.112Z Has data issue: false hasContentIssue false
  • English
  • Français

Predicting the fuel flow rate of commercial aircraft via multilayer perceptron, radial basis function and ANFIS artificial neural networks

Published online by Cambridge University Press:  19 October 2020

T. Baklacioglu Open the ORCID record for T. Baklacioglu [Opens in a new window]
T. Baklacioglu*
Affiliation:
Eskisehir Technical University, Eskisehir 26555, Turkey
*
[email protected]
Get access Rights & Permissions [Opens in a new window]

Abstract

A first attempt is made to use recently developed, non-conventional Artificial Neural Network (ANN) models with Multilayer Perceptron (MLP), Radial Basis Function (RBF) and Adaptive Neuro-Fuzzy Interference System (ANFIS) architectures to predict the fuel flow rate of a commercial aircraft using real data obtained from Flight Data Records (FDRs) of the cruise, climb and descent phases. The training of the architectures with a single hidden layer is performed by utilising the Delta-Bar-Delta (DBD), Conjugate Gradient (CG) and Quickprop (QP) algorithms. The optimum network topologies are sought by varying the number of processing elements in the hidden layer of the networks using a trial-and-error method. An evaluation of the approximate fuel intake values against the ideal fuel intake data from the FDRs indicates a good fit for all three ANN models. Thus, more accurate fuel intake estimations can be obtained by applying the RBF-ANN model during the climb and descent flight stages, whereas the MLP-ANN model is more effective for the cruise phase. The best accuracy obtained in terms of the linear correlation coefficient is 0.99988, 0.91946 and 0.95252 for the climb, cruise and descent phase, respectively.

Keywords

aircraftfuel flow rateartificial neural networksradial basis functionadaptive neuro-fuzzy interference systemflight data records
Type
Research Article
Information
The Aeronautical Journal , Volume 125 , Issue 1285 , March 2021 , pp. 453 - 471
Copyright
© The Author(s), 2020. Published by Cambridge University Press on behalf of Royal Aeronautical Society

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

REFERENCES

Turgut, E.T., Cavcar, M., Usanmaz, O., Canarslanlar, A.O., Dogeroglu, T., Armutlu, K. and Yay, O.D. Fuel flow analysis for the cruise phase of commercial aircraft on domestic routes, Aerosp Sci Technol, 2014, 37, pp 1–9.CrossRefGoogle Scholar
Mitchell, D., Ekstrand, H., Prats, X. and GrÖnstedt, T. An environmental assessment of air traffic speed constraints in the departure phase of flight: A case study at Gothenburg Landvetter Airport, Sweden, Transp Res Part D, Transp Environ, 2012, 17, (8), pp 610–618.CrossRefGoogle Scholar
Collins, B. Estimation of aircraft fuel consumption, J Aircr, 1982, 19, (11), pp 969–975. http://dx.doi.org/10.2514/3.44799, also AIAA Paper 81-0789R.CrossRefGoogle Scholar
Trani, A., Wing-Ho, F., Schilling, G., Baik, H. and Seshadri, A. A neural network model to estimate aircraft fuel consumption, 4th AIAA Aviation Technology, Integration and Operations (ATIO) Forum, Chicago, 20–22 September 2004, AIAA Paper 2004-6401, 2004.CrossRefGoogle Scholar
Senzig, D.A., Fleming, G.G. and Iovinelli, R.J. Modeling of terminal-area airplane fuel consumption, J Aircr, 2009, 46, (4), pp 1089–1093.CrossRefGoogle Scholar
Bartel, M. and Young, T.M. Simplified thrust and fuel consumption models for modern two-shaft turbofan engines, J Aircr, 2008, 45, (4), pp 1450–1456.CrossRefGoogle Scholar
Turgut, E.T. and Rosen, M.A. Relationship between fuel consumption and altitude for commercial aircraft during descent: Preliminary assessment with a genetic algorithm, Aerosp Sci Technol, 2012, 17, pp 65–73.CrossRefGoogle Scholar
Nuic, A. User Manual for the Base of Aircraft Data (BADA), Revision 3.13, EUROCONTROL Experimental Centre. EEC Technical/Scientific Report No. 15/04/02-43, 2015.Google Scholar
Baklacioglu, T. Fuel flow-rate modelling of transport aircraft for the climb flight using genetic algorithms, Aeronaut J, 2015, 119, (1212), pp 173–183.CrossRefGoogle Scholar
Baklacioglu, T. Modeling the fuel flow-rate of transport aircraft during flight phases using genetic algorithm-optimized neural networks, Aerosp Sci Technol, 2016, 49, pp 52–62.CrossRefGoogle Scholar
Huang, C., Xu, Y. and Johnson, M.E. Statistical modeling of the fuel flow rate of GA piston engine aircraft using flight operational data. Transp Res Part D Transp Environ, 2017, 53, pp 50–62.CrossRefGoogle Scholar
Jovanovic, R.Z., Sretenovic, A.A. and Zivkovic, B.D. Ensemble of various neural networks for prediction of heating energy consumption, Energ Buildings, 2015, 94, pp 189–199.CrossRefGoogle Scholar
Chandok, J.S., Kar, I.N. and Tuli, S. Estimation of furnace exit gas temperature (FEGT) using optimized radial basis and back-propagation neural networks, Energ Convers Manage, 2008, 49, pp 1989–1998.CrossRefGoogle Scholar
Ekonomou, L. Greek long-term energy consumption prediction using artificial neural networks, Energy, 2010, 35, pp 512–517.CrossRefGoogle Scholar
Sun, W. and Xu, Y. Financial security evaluation of the electric power industry in China based on a back propagation neural network optimized by genetic algorithm, Energy, 2016, 101, pp 366–379.CrossRefGoogle Scholar
Svorcan, J., Stupar, S., Trivkovic, S., PetraŠinovic, N. and Ivanov, T. Active boundary layer control in linear cascades using CFD and artificial neural networks, Aerosp Sci Technol, 2014, 39, pp 243–249.CrossRefGoogle Scholar
Sekhar, P. and Mohanty, S. An online power system static security assessment module using multi-layer perceptron and radial basis function network, Int J Elec Power, 2016, 76, pp 165–173.CrossRefGoogle Scholar
Takagi, T. and Sugeno, M. Fuzzy identification of systems and its applications to modeling and control, IEEE Trans Syst Man Cybern, 1985, 1, pp 116–132.CrossRefGoogle Scholar
Khatiba, T., Mohameda, A. and Sopian, K. A review of solar energy modeling techniques, Renew Sustain Energy Rev, 2012, 16, pp 2864–2869.CrossRefGoogle Scholar
Voyant, C., Muselli, M., Paoli, C. and Nivet, M.L. Hybrid methodology for hourly global radiation forecasting in Mediterranean area, Renew Energy, 2013, 53, pp 1–11.CrossRefGoogle Scholar
Adewole, B.Z., Abidakun, O.A. and Asere, A.A. Artificial neural network prediction of exhaust emissions and flame temperature in LPG (liquefied petroleum gas) fueled low swirl burner, Energy, (2013) 61, pp 606–611.CrossRefGoogle Scholar
Taghavifar, H. and Mardani, A. A comparative trend in forecasting ability of artificial neural networks and regressive support vector machine methodologies for energy dissipation modeling of off-road vehicles, Energy, 2014, 66, pp 569–576.CrossRefGoogle Scholar
Asadi, E., Gameiro da Silva, M., Antunes, C.H., Dias, L. and Glicksman, L. Multi-objective optimization for building retrofit: A model using genetic algorithm and artificial neural network and an application, Energ Buildings, 2014, 81, pp 444–456.CrossRefGoogle Scholar
Chiu, S.L. Fuzzy model identification based on cluster estimation, J Intell Fuzzy Syst, 1994, 2, (3), pp 267–278.CrossRefGoogle Scholar