PREDICTION OF NOx AND FUEL FLOW OF COMMERCIAL HIGH BYPASS AIRCRAFT ENGINES BASED ON CSA-SVR MODEL

Year 2024, Volume: 25 Issue: 4, 542 - 556, 27.12.2024

Rıdvan Oruç

https://doi.org/10.18038/estubtda.1471531

Abstract

Due to the negative effects of emissions caused by fossil fuels used by aircraft engines on the environment and human health, and the fact that fuel consumption is a high cost input for airlines, the aviation community has many studies on both issues. In order to overcome these problems, much space has been devoted to modeling, prediction and optimization studies on emissions and fuel consumption in the literature. Within the scope of this study, a model was created to predict the NOx emission values and fuel flow of high by-pass turbofan engines, which are also used in today's commercial air transportation. 165 different turbofan data taken from the International Civil Aviation Organization (ICAO) emission databank were used for modeling, and the specified parameters were modeled according to the by-pass ratio (BPR), overall pressure ratio (OPR) and rated thrust input parameters. In this context, the Cuckoo search algorithm-support vector regression (CSA-SVR) method for the Landing and Take-off (LTO) cycle, which includes the idle, take-off (T/O), climb out (C/O) and approach (App) phases, was used for the first time in the literature for the above-mentioned purpose. As a result of the error analysis methods, the minimum R2 value for 4 phases in FF estimation was found to be 0.972763. This value for NOX was 0.6745 in the idle phase. However, the fact that this value was found to be 0.861497, 0.884984 and 0.792779 for T/O, C/O and App, respectively, shows the success of the model in estimating actual data.

Keywords

Cuckoo search algorithm, Support vector regression, Aircraft emission modelling, Aircraft fuel modelling

References

• [1] Kesgin U. Aircraft emissions at Turkish airports. Energy, 2006;31(2–3).
• [2] Fan Y Van, Perry S, Klemeš JJ, Lee CT. A review on air emissions assessment: Transportation. J Clean Prod, 2018;194.
• [3] Aygun H. Exergo-sustainability behavior of high by-pass turbofan engine of a passenger aircraft during main flight phases. Energy Sources, Part A Recover. Util. Environ. Eff, 2021; doi: 10.1080/15567036.2021.1947421.
• [4] Airbus. Global Market Forecast 2022 [Internet]. 2022 [cited 2023 Apr 13]. Available from: https://www.airbus.com/en/products-services/commercial-aircraft/market/global-market-forecast
• [5] Boeing. Commercial Market Outlook 2022–2041 [Internet]. 2022 [cited 2023 Apr 13]. Available from: https://www.boeing.com/commercial/market/commercial-market-outlook/index.page
• [6] Baklacioglu T. Modeling the fuel flow-rate of transport aircraft during flight phases using genetic algorithm-optimized neural networks. Aerosp Sci Technol, 2016;49:52–62.
• [7] Baklacioglu T. Predicting the fuel flow rate of commercial aircraft via multilayer perceptron, radial basis function and ANFIS artificial neural networks. Aeronaut J, 2021;125(1285):453–71.
• [8] Khandelwal B, Karakurt A, Sekaran PR. Hydrogen powered aircraft : The future of air transport. Prog Aerosp Sci, 2013;60:45–59.
• [9] Oruc R, Baklacioglu T, Turan O, Aydin H. Modeling of environmental effect factor and exergetic sustainability index with cuckoo search algorithm for a business jet. Aircr Eng Aerosp Technol, 2022
• [10] Oruc R, Baklacioglu T. Propulsive modelling for JT9D-3, JT15D-4C and TF-30 turbofan engines using particle swarm optimization. Aircr Eng Aerosp Technol. 2020;92(6):939–46.
• [11] Atilgan R, Turan O, Altuntas O, Aydın H, Synylo K. Environmental impact assessment of a turboprop engine with the aid of exergy. Energy, 2013;58:664–71.
• [12] Aydin H, Turan O, Midilli A, Karakoc TH. Exergetic and exergo–economic analysis of a turboprop engine: a case study for CT7–9C. Int J Exergy, 2012;11(1):69–88.
• [13] IATA. Fuel Fact Sheet [Internet]. 2024 [cited 2024 Mar 26]. Available from: https://www.iata.org/en/iata-repository/pressroom/fact-sheets/fact-sheet---fuel/
• [14] Aygun H, Dursun OO, Toraman S. Machine learning based approach for forecasting emission parameters of mixed flow turbofan engine at high power modes., Energy, 2023; 271. doi: 10.1016/j.energy.2023.127026.
• [15] ICAO. Impacts of Aviation NOx Emissions on Air Quality, Health, and Climate [Internet]. 2022 [cited 2024 Mar 26]. Available from: https://www.icao.int/environmental-protection/Documents/EnvironmentalReports/2022/ENVReport2022_Art18.pdf
• [16] Ucar UU, Aygun H, Tanyeri B. Optimized modeling of energy and environmental metrics of mixed flow turbofan engine used regional aircraft. J Therm Anal Calorim, 2023
• [17] Dursun OO, Toraman S, Aygun H. Deep learning approach for prediction of exergy and emission parameters of commercial high by‑pass turbofan engines. Environ Sci Pollut Res, 2023;30:27539–27559.
• [18] Trani AA, Wing-Ho FC, Schilling G, Baik H, Seshadri A. A neural network model to estimate aircraft fuel consumption. In: Collection of Technical Papers - AIAA 4th Aviation Technology, Integration, and Operations Forum, ATIO. Chicago, Illinois: AIAA; 2004. p. 6401.
• [19] Baklacioglu T. Fuel flow-rate modelling of transport aircraft for the climb flight using genetic algorithms. Aeronaut J, 2015;119(1212):173–83.
• [20] Oruc R, Baklacioglu T. Modelling of fuel flow-rate of commercial aircraft for the climbing flight using cuckoo search algorithm. Aircr Eng Aerosp Technol, 2020;92(3):495–501.
• [21] Oruc R, Baklacioglu T. Modeling of fuel flow-rate of commercial aircraft for the descent flight using particle swarm optimization. Aircr Eng Aerosp Technol, 2021;93(2):319–26.
• [22] EASA. ICAO Aircraft Engine Emissions Databank [Internet]. [cited 2022 Nov 23]. Available from: https://www.easa.europa.eu/en/domains/environment/icao-aircraft-engine-emissions-databank
• [23] Yang XS. Nature-Inspired Optimization Algorithms. Nature-Inspired Optimization Algorithms, 2014.
• [24] Yang XS, Deb S. Engineering optimisation by cuckoo search. Int J Math Model Numer Optim, 2010
• [25] Joshi AS, Kulkarni O, Kakandikar GM, Nandedkar VM. Cuckoo Search Optimization- A Review. Mater Today Proc, 2017;4(8):7262–9.
• [26] Gandomi AH, Yang XS, Alavi AH. Cuckoo search algorithm: A metaheuristic approach to solve structural optimization problems. Eng Comput, 2013;29(1):17–35.
• [27] Yang XS, Deb S. Cuckoo search via Lévy flights. In: 2009 World Congress on Nature and Biologically Inspired Computing, NABIC 2009 - Proceedings. IEEE; 2009. p. 210–4.
• [28] Huang S, Tian L, Zhang J, Chai X, Wang H, Zhang H. Support Vector Regression Based on the Particle Swarm Optimization Algorithm for Tight Oil Recovery Prediction. ACS Omega. 2021;6(47):32142–32150.
• [29] Wu J, Zhou J, Gao Y. Support vector regression based on particle swarm optimization and projection pursuit technology for rainfall forecasting. In: 2009 International Conference on Computational Intelligence and Security. Beijing, China: IEEE; 2009. p. 227–330.
• [30] Chang CC, Lin CJ. LIBSVM: A Library for support vector machines. ACM Trans Intell Syst Technol, 2011;2(3).
• [31] Zhao S, Wang L. No Title. In: 2010 Third International Joint Conference on Computational Science and Optimization, 2010. p. 484–7.