Modelling and Comparison of Compressor Performance Parameters by Using ANFIS

Article Preview

Abstract:

Developing a robust control algorithm for an aircraft engine requires an accurate nonlinear mathematical model. In formation of a nonlinear mathematical model, some components like compressor and turbine are modeled by using component maps. These maps show the connection between the compressor performance parameters. To show this connection, map data is digitized by using some techniques. In this study, we digitized a compressor map data by using ANFIS (Adaptive Neuro Fuzzy Inference System). RMSE (Root Mean Square Error) were calculated for different types of FIS (Fuzzy Inference System) structures constructed with different number of membership functions. The model was formed by using all valid data which is collected from a small turboprop engine compressor. Results demonstrate that the designed ANFIS structure can serve as an alternative model to estimate both online and offline compressor performance parameters.

You might also be interested in these eBooks

Info:

Periodical:

Pages:

710-715

Citation:

Online since:

August 2014

Export:

Price:

Сopyright:

© 2014 Trans Tech Publications Ltd. All Rights Reserved

Share:

Citation:

* - Corresponding Author

[1] Information on http: /etd. lib. metu. edu. tr/upload/12611442/index. pdf.

Google Scholar

[2] J. Kurzke: How to Get Component Maps for an Aircraft Gas Turbine's Performance Calculations, ASME Paper No: 96-GT-164 (1996).

Google Scholar

[3] Q. Z. Al-Hamdan and M.S. Ebaid: Modeling and Simulation of a Gas Turbine Engine for Power Generation, Journal of Engineering for Gas Turbine and Power Vol. 128 (2006), 302-311.

DOI: 10.1115/1.2061287

Google Scholar

[4] S. M. Camporeale, B. Fortunato and M. Mastrovito: A Modular Code for Real Time Dynamic Simulation of Gas Turbines in Simulink, Journal of Engineering for Gas Turbines and Power Vol. 128 (2006), 506-517.

DOI: 10.1115/1.2132383

Google Scholar

[5] G. Kocer and O. Uzol: Real-Time Simulation of a Small Turbojet Engine, Proceeding of 4th Ankara International Aerospace Conference, Ankara (2007).

Google Scholar

[6] G. Sieros, A. Stamatis and K. Mathioudakis: Jet Engine Component Maps for Performance Modeling and Diagnosis, Journal of Propulsion and Power Vol. 13, (1997), 665–674.

DOI: 10.2514/2.5218

Google Scholar

[7] P. Moraal and I. Kolmanovsky: Turbocharger Modeling for Automotive Control Application, SAE Technical Paper Series Vol. 108 (1999), 1324–1338.

DOI: 10.4271/1999-01-0908

Google Scholar

[8] M. Orkisz and S. Stawarz: Modeling of Turbine Engine Axial-Flow Compressor and Turbine Characteristics", Journal of Propulsion and Power Vol. 16 (2000), 336–339.

DOI: 10.2514/2.5574

Google Scholar

[9] P. Ailer, I. Santa, G. Szederkenyi and K.M. Hangos: Non-Linear Model-Building of a Low-Power Gas Turbine, PeriodicaPolytechnicaSer. Transportation Engineering Vol. 29 (2001), 117-135.

Google Scholar

[10] C. D. Kong, S. Kho and J.Y. Ki: Component Map Generation of a Gas Turbine Using Genetic Algorithms, Journal of Engineering for Gas Turbines and Power Vol. 128 (2006), 92–96.

DOI: 10.1115/1.2032431

Google Scholar

[11] C. D. Kong and J.Y. Ki: Components Map Generation of Gas Turbine Engine Using Genetic Algorithms and Engine Performance Deck Data, Journal of Engineering Gas Turbines and Power, Vol. 129 (2007), 312–317.

DOI: 10.1115/1.2436561

Google Scholar

[12] C. Bao, M. Ouyang, and B. Yi: Modeling and Optimization of the Air System in polymer Exchange Membrane Fuel Cell System, Journal of Power Sources, Vol. 156 (2006), 232–243.

DOI: 10.1016/j.jpowsour.2005.06.008

Google Scholar

[13] Y. Yu, L. Chen, F. Sun and C. Wu: Neural Network Based Analysis and Prediction of a Compressor's Characteristic Performance Map, Journal of Applied Energy, Vol. 84(2007), 48–55.

DOI: 10.1016/j.apenergy.2006.04.005

Google Scholar

[14] K. Ghorbanian and M. Gholamrezaei: Neural Network Modeling of Axial Flow Compressor Off-Design Performance, Proceeding of 10th Fluid Dynamic Conference Yazd, Iran(2006).

Google Scholar

[15] K. Ghorbanian and M. Gholamrezaei: Neural Network Modeling of Axial Flow Compressor Performance Map, Proceeding of 45th AIAA Aerospace Science Meeting and Exhibit Reno, USA(2006).

DOI: 10.2514/6.2007-1165

Google Scholar

[16] K. Ghorbanian and M. Gholamrezaei: Axial Compressor Performance Map Prediction Using Artificial Neural Network, ASME Paper No: GT2007-27165, Montreal, Canada(2007).

DOI: 10.1115/gt2007-27165

Google Scholar

[17] K. Ghorbanian and M. Gholamrezaei: An Artificial Neural Network Approach to Compressor Performance Prediction, Journal of Applied Energy, Vol. 86(2009), 1210-1221.

DOI: 10.1016/j.apenergy.2008.06.006

Google Scholar

[18] K. Ghorbanian and M. Gholamrezaei: Compressor Map Generation Using Feed Forward Neural Network, Journal of Power and Energy Vol. 223(2009).

Google Scholar

[19] A. Lazzaretto and A. Toffolo: Analytical and Neural-Network Models for Gas Turbine Design and Off-Design Simulation, International Journal of Thermodynamics Vol. 4(2010), 173-182.

Google Scholar

[20] S. Sanaye, M. Dehghandokht, H. Mohammadbeigi and S. Bahrami: Modeling of Rotary Vane Compressor Applying Artificial Neural Network, International Journal of Refrigeration, Vol. 34 (2011), 764-772.

DOI: 10.1016/j.ijrefrig.2010.12.007

Google Scholar

[21] K. Ghorbanian and M. Gholamrezaei: Application of Fuzzy Logic to Axial Compressor Performance Map Prediction, Proceeding of ASME Power 2007, Texas, USA(2007).

DOI: 10.1115/power2007-22174

Google Scholar

[22] F. Chu, F. Wang, X. Wang and S. Zhang: Performance Modeling of Centrifugal Compressor Using Kernel Partial Least Squares, Journal ofApplied Thermal Engineering Vol. 44(2012), 90-99.

DOI: 10.1016/j.applthermaleng.2012.03.043

Google Scholar

[23] I. Yazar, E. Kiyak and F. Caliskan: A New Approach for TurbomachineryModelling by Using ANFIS Structure, Proceeding of 6th Exergy-Energy and Environment Symposium- IEEES6, Rize, Turkey(2013).

Google Scholar

[24] Information on http: /digitalcommons. calpoly. edu/cgi/viewcontent. cgi?article=1028&context=aerosp.

Google Scholar

[25] D. W. Kang, T. S. Kim H.B. Hurand J.K. Park: The Effect of Firing Biogas on the Performance and Operating Characteristics of Simple and Recuperative Cycle Gas Turbine Combined Heat and Power Systems, Journal of Applied Energy Vol. 93(2012).

DOI: 10.1016/j.apenergy.2011.12.038

Google Scholar

[26] Information on https: /etd. ohiolink. edu/ap: 0: 0: APPLICATION_PROCESS=DOWNLOAD_ETD_SUB_DOC_ACCNUM: F1501_ID: toledo1271367584, attachment.

Google Scholar

[27] J. R. Jang: ANFIS: Adaptive-Network-Based Fuzzy Inference System, Journal of IEEE Transactions on Systems, Man, and Cybernetics, Vol. 23(1993), 665-685.

DOI: 10.1109/21.256541

Google Scholar

[28] Information on http: /www. mathworks. com.

Google Scholar

[29] T. Takagi and M. Sugeno: Derivation of Fuzzy Control Rules From Human Operator's Control Actions, Proceeding of IFAC Symposium on Fuzzy Information, Knowledge Representation and Decision Analysis(1983), 55-60.

DOI: 10.1016/s1474-6670(17)62005-6

Google Scholar