IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v16y2023i6p2634-d1094057.html
   My bibliography  Save this article

Linear Model of a Turboshaft Aero-Engine Including Components Degradation for Control-Oriented Applications

Author

Listed:
  • Teresa Castiglione

    (Department of Mechanical Energy and Management Engineering, University of Calabria, 87036 Rende, Italy)

  • Diego Perrone

    (Department of Mechanical Energy and Management Engineering, University of Calabria, 87036 Rende, Italy)

  • Luciano Strafella

    (Department of Engineering for Innovation, University of Salento, 73100 Lecce, Italy)

  • Antonio Ficarella

    (Department of Engineering for Innovation, University of Salento, 73100 Lecce, Italy)

  • Sergio Bova

    (Department of Mechanical Energy and Management Engineering, University of Calabria, 87036 Rende, Italy)

Abstract

The engine fuel control system plays a crucial role in engine performance and fuel economy. Fuel control, in traditional engine control systems, is carried out by means of sensor-based control methods, which correct the fuel flow rate through correlations or scheduled parameters in order to reduce the error between a measured parameter and its desired value. In the presence of component degradation, however, the relationship between the engine measurable parameters and performance may lead to an increase in the control error. In this research, linear models for advanced control systems and for direct fuel control in the presence of components degradation are proposed, with the main objective being to directly predict and correct fuel consumption in the presence of degradation instead of adopting measurable parameters. Two techniques were adopted for model linearization: Small Perturbation and System Identification. Results showed that both models are characterized by high accuracy in predicting the output engine variables, with the mean errors between model prediction and data below 1%. The maximum errors, recorded for shaft power, were about 6% for Small Perturbation and lower than 3% for System Identification. A simple correlation between engine performance and components degradation was also demonstrated; in particular, the achieved results allow one to conclude that the Small Perturbation approach is the best candidate for controller development when a prediction of components degradation is included.

Suggested Citation

  • Teresa Castiglione & Diego Perrone & Luciano Strafella & Antonio Ficarella & Sergio Bova, 2023. "Linear Model of a Turboshaft Aero-Engine Including Components Degradation for Control-Oriented Applications," Energies, MDPI, vol. 16(6), pages 1-18, March.
  • Handle: RePEc:gam:jeners:v:16:y:2023:i:6:p:2634-:d:1094057
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/16/6/2634/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/1996-1073/16/6/2634/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Ning-bo Zhao & Jia-long Yang & Shu-ying Li & Yue-wu Sun, 2014. "A GM (1, 1) Markov Chain-Based Aeroengine Performance Degradation Forecast Approach Using Exhaust Gas Temperature," Mathematical Problems in Engineering, Hindawi, vol. 2014, pages 1-11, April.
    2. Eduardo Cabrera & João M. Melo de Sousa, 2022. "Use of Sustainable Fuels in Aviation—A Review," Energies, MDPI, vol. 15(7), pages 1-23, March.
    3. Teresa Castiglione & Pietropaolo Morrone & Luigi Falbo & Diego Perrone & Sergio Bova, 2020. "Application of a Model-Based Controller for Improving Internal Combustion Engines Fuel Economy," Energies, MDPI, vol. 13(5), pages 1-22, March.
    4. Sogut, M. Ziya & Yalcin, Enver & Karakoc, T. Hikmet, 2017. "Assessment of degradation effects for an aircraft engine considering exergy analysis," Energy, Elsevier, vol. 140(P2), pages 1417-1426.
    5. Xiaohuan Sun & Soheil Jafari & Seyed Alireza Miran Fashandi & Theoklis Nikolaidis, 2021. "Compressor Degradation Management Strategies for Gas Turbine Aero-Engine Controller Design," Energies, MDPI, vol. 14(18), pages 1-21, September.
    Full references (including those not matched with items on IDEAS)

    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. Atilgan, Ramazan & Onder Turan,, 2020. "Economy and exergy of aircraft turboprop engine at dynamic loads," Energy, Elsevier, vol. 213(C).
    2. Tobias Mueller & Steven Gronau, 2023. "Fostering Macroeconomic Research on Hydrogen-Powered Aviation: A Systematic Literature Review on General Equilibrium Models," Energies, MDPI, vol. 16(3), pages 1-33, February.
    3. Balli, Ozgur, 2023. "Exergetic, sustainability and environmental assessments of a turboshaft engine used on helicopter," Energy, Elsevier, vol. 276(C).
    4. Changjun Huang & Lv Zhou & Fenliang Liu & Yuanzhi Cao & Zhong Liu & Yun Xue, 2023. "Deformation Prediction of Dam Based on Optimized Grey Verhulst Model," Mathematics, MDPI, vol. 11(7), pages 1-15, April.
    5. Diego Luna & Rafael Estevez, 2022. "Optimization of Biodiesel and Biofuel Process," Energies, MDPI, vol. 15(16), pages 1-4, August.
    6. Lucas Reijnders, 2022. "Defining and Operationalizing Sustainability in the Context of Energy," Energies, MDPI, vol. 15(14), pages 1-9, July.
    7. Vishal Ram & Surender Reddy Salkuti, 2023. "An Overview of Major Synthetic Fuels," Energies, MDPI, vol. 16(6), pages 1-35, March.
    8. Jonas Müller & Nico Besser & Philipp Hermsen & Stefan Pischinger & Jürgen Knauf & Pooya Bagherzade & Johannes Fryjan & Andreas Balazs & Simon Gottorf, 2023. "Virtual Development of Advanced Thermal Management Functions Using Model-in-the-Loop Applications," Energies, MDPI, vol. 16(7), pages 1-26, April.
    9. Nicola Menga & Akhila Mothakani & Maria Grazia De Giorgi & Radoslaw Przysowa & Antonio Ficarella, 2022. "Extreme Learning Machine-Based Diagnostics for Component Degradation in a Microturbine," Energies, MDPI, vol. 15(19), pages 1-22, October.
    10. Jakovljević, Ivan & Mijailović, Radomir & Mirosavljević, Petar, 2018. "Carbon dioxide emission during the life cycle of turbofan aircraft," Energy, Elsevier, vol. 148(C), pages 866-875.
    11. Moaaz Shehab & Kai Moshammer & Meik Franke & Edwin Zondervan, 2023. "Analysis of the Potential of Meeting the EU’s Sustainable Aviation Fuel Targets in 2030 and 2050," Sustainability, MDPI, vol. 15(12), pages 1-20, June.
    12. Fatigati, Fabio & Di Battista, Davide & Cipollone, Roberto, 2021. "Design improvement of volumetric pump for engine cooling in the transportation sector," Energy, Elsevier, vol. 231(C).
    13. Balli, Ozgur & Caliskan, Hakan, 2021. "Turbofan engine performances from aviation, thermodynamic and environmental perspectives," Energy, Elsevier, vol. 232(C).
    14. Savvas Savvakis & Dimitrios Mertzis & Elias Nassiopoulos & Zissis Samaras, 2020. "A Design of the Compression Chamber and Optimization of the Sealing of a Novel Rotary Internal Combustion Engine Using CFD," Energies, MDPI, vol. 13(9), pages 1-21, May.
    15. Zhao, Hang & Liao, Zengbu & Liu, Jinxin & Li, Ming & Liu, Wei & Wang, Lei & Song, Zhiping, 2022. "A highly robust thrust estimation method with dissimilar redundancy framework for gas turbine engine," Energy, Elsevier, vol. 245(C).
    16. Ranasinghe, Kavindu & Guan, Kai & Gardi, Alessandro & Sabatini, Roberto, 2019. "Review of advanced low-emission technologies for sustainable aviation," Energy, Elsevier, vol. 188(C).
    17. Rafael Estevez & Laura Aguado-Deblas & Francisco J. López-Tenllado & Carlos Luna & Juan Calero & Antonio A. Romero & Felipa M. Bautista & Diego Luna, 2022. "Biodiesel Is Dead: Long Life to Advanced Biofuels—A Comprehensive Critical Review," Energies, MDPI, vol. 15(9), pages 1-39, April.
    18. Chen, Yu-Zhi & Zhao, Xu-Dong & Xiang, Heng-Chao & Tsoutsanis, Elias, 2021. "A sequential model-based approach for gas turbine performance diagnostics," Energy, Elsevier, vol. 220(C).

    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:gam:jeners:v:16:y:2023:i:6:p:2634-:d:1094057. 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: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    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.