IDEAS home Printed from https://ideas.repec.org/a/eee/energy/v140y2017ip2p1398-1406.html
   My bibliography  Save this article

Comparison of various regression models for predicting compressor and turbine performance parameters

Author

Listed:
  • Yazar, Isil
  • Yavuz, Hasan Serhan
  • Yavuz, Arzu Altin

Abstract

This paper investigates various regression models to predict the compressor and turbine map parameters. To this end, we used the data measured from two different types of compressors and turbines. The compressor is basically a single stage radial machine, whereas, the turbine is formed of high pressure and low pressure parts. The emphasis of this study is to construct various models for prediction of corrected mass flow rate and isentropic efficiency. Except for prediction capabilities, the study also compares the regression model’s performances. Results show that the designed models can be used for the development of dynamic mathematical model of a gas turbine engine.

Suggested Citation

  • Yazar, Isil & Yavuz, Hasan Serhan & Yavuz, Arzu Altin, 2017. "Comparison of various regression models for predicting compressor and turbine performance parameters," Energy, Elsevier, vol. 140(P2), pages 1398-1406.
    • Handle: RePEc:eee:energy:v:140:y:2017:i:p2:p:1398-1406
    DOI: 10.1016/j.energy.2017.05.061
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0360544217308174
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.energy.2017.05.061?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Acquah, H. de-Graft, 2013. "On the Comparison of Akaike Information Criterion and Consistent Akaike Information Criterion in Selection of an Asymmetric Price Relationship: Bootstrap Simulation Results," AGRIS on-line Papers in Economics and Informatics, Czech University of Life Sciences Prague, Faculty of Economics and Management, vol. 5(1), pages 1-7, March.
    2. Nikolić, Vlastimir & Petković, Dalibor & Shamshirband, Shahaboddin & Ćojbašić, Žarko, 2015. "Adaptive neuro-fuzzy estimation of diffuser effects on wind turbine performance," Energy, Elsevier, vol. 89(C), pages 324-333.
    3. Panapakidis, Ioannis P. & Dagoumas, Athanasios S., 2017. "Day-ahead natural gas demand forecasting based on the combination of wavelet transform and ANFIS/genetic algorithm/neural network model," Energy, Elsevier, vol. 118(C), pages 231-245.
    4. Yu, Youhong & Chen, Lingen & Sun, Fengrui & Wu, Chih, 2007. "Neural-network based analysis and prediction of a compressor's characteristic performance map," Applied Energy, Elsevier, vol. 84(1), pages 48-55, January.
    5. Ghorbanian, K. & Gholamrezaei, M., 2009. "An artificial neural network approach to compressor performance prediction," Applied Energy, Elsevier, vol. 86(7-8), pages 1210-1221, July.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Duan, Jiandong & Fan, Shaogui & An, Quntao & Sun, Li & Wang, Guanglin, 2017. "A comparison of micro gas turbine operation modes for optimal efficiency based on a nonlinear model," Energy, Elsevier, vol. 134(C), pages 400-411.
    2. Kirmizi, Mehmet & Aygun, Hakan & Turan, Onder, 2023. "Performance and energy analysis of turboprop engine for air freighter aircraft with the aid of multiple regression," Energy, Elsevier, vol. 283(C).
    3. Witanowski, Łukasz & Klonowicz, Piotr & Lampart, Piotr & Klimaszewski, Piotr & Suchocki, Tomasz & Jędrzejewski, Łukasz & Zaniewski, Dawid & Ziółkowski, Paweł, 2023. "Impact of rotor geometry optimization on the off-design ORC turbine performance," Energy, Elsevier, vol. 265(C).
    4. Ranasinghe, Kavindu & Guan, Kai & Gardi, Alessandro & Sabatini, Roberto, 2019. "Review of advanced low-emission technologies for sustainable aviation," Energy, Elsevier, vol. 188(C).
    5. Kilic, Ugur & Yalin, Gorkem & Cam, Omer, 2023. "Digital twin for Electronic Centralized Aircraft Monitoring by machine learning algorithms," Energy, Elsevier, vol. 283(C).
    6. Karakurt, Izzet, 2021. "Modelling and forecasting the oil consumptions of the BRICS-T countries," Energy, Elsevier, vol. 220(C).
    7. Wang, Qiang & Song, Xiaoxin, 2019. "Forecasting China's oil consumption: A comparison of novel nonlinear-dynamic grey model (GM), linear GM, nonlinear GM and metabolism GM," Energy, Elsevier, vol. 183(C), pages 160-171.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Balerna, Camillo & Lanzetti, Nicolas & Salazar, Mauro & Cerofolini, Alberto & Onder, Christopher, 2020. "Optimal low-level control strategies for a high-performance hybrid electric power unit," Applied Energy, Elsevier, vol. 276(C).
    2. Cortés, O. & Urquiza, G. & Hernández, J.A., 2009. "Optimization of operating conditions for compressor performance by means of neural network inverse," Applied Energy, Elsevier, vol. 86(11), pages 2487-2493, November.
    3. Li, Zhihui & Liu, Yanming, 2017. "Blade-end treatment for axial compressors based on optimization method," Energy, Elsevier, vol. 126(C), pages 217-230.
    4. Safiyullah, F. & Sulaiman, S.A. & Naz, M.Y. & Jasmani, M.S. & Ghazali, S.M.A., 2018. "Prediction on performance degradation and maintenance of centrifugal gas compressors using genetic programming," Energy, Elsevier, vol. 158(C), pages 485-494.
    5. Guan, Cong & Theotokatos, Gerasimos & Zhou, Peilin & Chen, Hui, 2014. "Computational investigation of a large containership propulsion engine operation at slow steaming conditions," Applied Energy, Elsevier, vol. 130(C), pages 370-383.
    6. Likun Ren & Haiqin Qin & Zhenbo Xie & Jing Xie & Bianjiang Li, 2022. "A Thermodynamics-Oriented and Neural Network-Based Hybrid Model for Military Turbofan Engines," Sustainability, MDPI, vol. 14(10), pages 1-15, May.
    7. Tsoutsanis, Elias & Meskin, Nader & Benammar, Mohieddine & Khorasani, Khashayar, 2014. "A component map tuning method for performance prediction and diagnostics of gas turbine compressors," Applied Energy, Elsevier, vol. 135(C), pages 572-585.
    8. Tahan, Mohammadreza & Tsoutsanis, Elias & Muhammad, Masdi & Abdul Karim, Z.A., 2017. "Performance-based health monitoring, diagnostics and prognostics for condition-based maintenance of gas turbines: A review," Applied Energy, Elsevier, vol. 198(C), pages 122-144.
    9. Li, Shunxi & Su, Bowen & St-Pierre, David L. & Sui, Pang-Chieh & Zhang, Guofang & Xiao, Jinsheng, 2017. "Decision-making of compressed natural gas station siting for public transportation: Integration of multi-objective optimization, fuzzy evaluating, and radar charting," Energy, Elsevier, vol. 140(P1), pages 11-17.
    10. Rao, Congjun & Zhang, Yue & Wen, Jianghui & Xiao, Xinping & Goh, Mark, 2023. "Energy demand forecasting in China: A support vector regression-compositional data second exponential smoothing model," Energy, Elsevier, vol. 263(PC).
    11. Zhang, Meng & Guo, Huan & Sun, Ming & Liu, Sifeng & Forrest, Jeffrey, 2022. "A novel flexible grey multivariable model and its application in forecasting energy consumption in China," Energy, Elsevier, vol. 239(PE).
    12. Meira, Erick & Cyrino Oliveira, Fernando Luiz & de Menezes, Lilian M., 2022. "Forecasting natural gas consumption using Bagging and modified regularization techniques," Energy Economics, Elsevier, vol. 106(C).
    13. Wei, Nan & Li, Changjun & Peng, Xiaolong & Li, Yang & Zeng, Fanhua, 2019. "Daily natural gas consumption forecasting via the application of a novel hybrid model," Applied Energy, Elsevier, vol. 250(C), pages 358-368.
    14. Chong, W.T. & Gwani, M. & Shamshirband, S. & Muzammil, W.K. & Tan, C.J. & Fazlizan, A. & Poh, S.C. & Petković, Dalibor & Wong, K.H., 2016. "Application of adaptive neuro-fuzzy methodology for performance investigation of a power-augmented vertical axis wind turbine," Energy, Elsevier, vol. 102(C), pages 630-636.
    15. Artur M. Schweidtmann & Alexander Mitsos, 2019. "Deterministic Global Optimization with Artificial Neural Networks Embedded," Journal of Optimization Theory and Applications, Springer, vol. 180(3), pages 925-948, March.
    16. Beyca, Omer Faruk & Ervural, Beyzanur Cayir & Tatoglu, Ekrem & Ozuyar, Pinar Gokcin & Zaim, Selim, 2019. "Using machine learning tools for forecasting natural gas consumption in the province of Istanbul," Energy Economics, Elsevier, vol. 80(C), pages 937-949.
    17. Ravnik, J. & Hriberšek, M., 2019. "A method for natural gas forecasting and preliminary allocation based on unique standard natural gas consumption profiles," Energy, Elsevier, vol. 180(C), pages 149-162.
    18. Zahid, Taimoor & Xu, Kun & Li, Weimin & Li, Chenming & Li, Hongzhe, 2018. "State of charge estimation for electric vehicle power battery using advanced machine learning algorithm under diversified drive cycles," Energy, Elsevier, vol. 162(C), pages 871-882.
    19. Mehrbakhsh Nilashi & Fausto Cavallaro & Abbas Mardani & Edmundas Kazimieras Zavadskas & Sarminah Samad & Othman Ibrahim, 2018. "Measuring Country Sustainability Performance Using Ensembles of Neuro-Fuzzy Technique," Sustainability, MDPI, vol. 10(8), pages 1-20, August.
    20. Zhang, Yi & Xu, Yujie & Zhou, Xuezhi & Guo, Huan & Zhang, Xinjing & Chen, Haisheng, 2019. "Compressed air energy storage system with variable configuration for accommodating large-amplitude wind power fluctuation," Applied Energy, Elsevier, vol. 239(C), pages 957-968.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:energy:v:140:y:2017:i:p2:p:1398-1406. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.journals.elsevier.com/energy .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.