IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v15y2022i19p6874-d919782.html
   My bibliography  Save this article

Life Test Optimization for Gas Turbine Engine Based on Life Cycle Information Support and Modeling

Author

Listed:
  • Sagit Valeev

    (Department of Computer Science and Robotics, Ufa State Aviation Technical University, Ufa 450000, Russia
    Department of Information Technologies and Mathematics, Sochi State University, Sochi 354008, Russia)

  • Natalya Kondratyeva

    (Department of Computer Science and Robotics, Ufa State Aviation Technical University, Ufa 450000, Russia
    Department of Information Technologies and Mathematics, Sochi State University, Sochi 354008, Russia)

Abstract

The task of choosing the modes and duration of life tests of complex technical objects, such as aircraft engines, is a complex and difficult-to-formalize task. Experimental optimization of the parameters of life tests of complex technical objects is costly in terms of material and time resources, which makes such an approach to the choice of test parameters practically difficult. The problem of life test optimization for gas turbine engines on the basis of the engine life cycle information support and statistical modeling is discussed. Within the framework of the research, the features of the optimization of life tests based on simulation modeling of the life cycle of gas turbine engines were studied. The criterion of the efficiency of the life tests was introduced, and this characterized the predicted effect (technical and economic) of the operation of a batch of engines, the reliability of which was confirmed by life tests; a method of complex optimization of resource tests in the life cycle system was developed. An objective function was formed for the complex optimization of life tests based on life cycle simulation. The principles of formation and refinement of the simulation model of the life cycle for the optimization of life tests were determined. A simulation model of the main stages of the life cycle of an auxiliary gas turbine engine was developed. A study was performed on the influence of the quality of the production of “critical” engine elements, the system of engine acceptance and shipment, as well as the effect of a range of parameters of the engine loading mode on the efficiency of the life tests of an auxiliary gas turbine engine. The optimal parameters of periodic life tests of an auxiliary gas turbine engine were determined by simulation modeling in the life cycle system, which made it possible to increase the equivalence of tests by several times and reduce their duration in comparison with the program of serial tests.

Suggested Citation

  • Sagit Valeev & Natalya Kondratyeva, 2022. "Life Test Optimization for Gas Turbine Engine Based on Life Cycle Information Support and Modeling," Energies, MDPI, vol. 15(19), pages 1-15, September.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:19:p:6874-:d:919782
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/15/19/6874/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/15/19/6874/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Li, Junqiu & Wang, Yihe & Chen, Jianwen & Zhang, Xiaopeng, 2017. "Study on energy management strategy and dynamic modeling for auxiliary power units in range-extended electric vehicles," Applied Energy, Elsevier, vol. 194(C), pages 363-375.
    2. Alexander Inozemtsev & Anton Petrochenkov & Vladimir Kazantsev & Igor Shmidt & Alexey Sazhenkov & Dmitry Dadenkov & Igor Gribkov & Pavel Ivanov, 2022. "The Fuzzy Logic in the Problems of Test Control of a Bypass Turbojet Engine Gas Generator," Mathematics, MDPI, vol. 10(3), pages 1-17, February.
    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. Anton Petrochenkov & Nikolai Pavlov & Nikolai Bachev & Alexander Romodin & Iurii Butorin & Nikolai Kolesnikov, 2023. "Ensuring Power Balance in the Electrical Grid of an Oil-and-Gas-Producing Enterprise with Distributed Generation Using Associated Petroleum Gas," Sustainability, MDPI, vol. 15(19), pages 1-15, September.
    2. Hou, Daizheng & Sun, Qun & Bao, Chunjiang & Cheng, Xingqun & Guo, Hongqiang & Zhao, Ying, 2019. "An all-in-one design method for plug-in hybrid electric buses considering uncertain factor of driving cycles," Applied Energy, Elsevier, vol. 253(C), pages 1-1.
    3. Li, Guidan & Yang, Zhe & Li, Bin & Bi, Huakun, 2019. "Power allocation smoothing strategy for hybrid energy storage system based on Markov decision process," Applied Energy, Elsevier, vol. 241(C), pages 152-163.
    4. Gye-Seong Lee & Dong-Hyun Kim & Jong-Ho Han & Myeong-Hwan Hwang & Hyun-Rok Cha, 2019. "Optimal Operating Point Determination Method Design for Range-Extended Electric Vehicles Based on Real Driving Tests," Energies, MDPI, vol. 12(5), pages 1-17, March.
    5. Omkar Parkar & Benjamin Snyder & Adibuzzaman Rahi & Sohel Anwar, 2023. "Modified Particle Swarm Optimization Based Powertrain Energy Management for Range Extended Electric Vehicle," Energies, MDPI, vol. 16(13), pages 1-21, June.
    6. Xiao, B. & Ruan, J. & Yang, W. & Walker, P.D. & Zhang, N., 2021. "A review of pivotal energy management strategies for extended range electric vehicles," Renewable and Sustainable Energy Reviews, Elsevier, vol. 149(C).
    7. Ye Yang & Youtong Zhang & Jingyi Tian & Si Zhang, 2018. "Research on a Plug-In Hybrid Electric Bus Energy Management Strategy Considering Drivability," Energies, MDPI, vol. 11(8), pages 1-22, August.
    8. Xiao Hu & Shikun Liu & Ke Song & Yuan Gao & Tong Zhang, 2021. "Novel Fuzzy Control Energy Management Strategy for Fuel Cell Hybrid Electric Vehicles Considering State of Health," Energies, MDPI, vol. 14(20), pages 1-20, October.
    9. Majid Aghasharifian Esfahani & Mohammadmehdi Namazi & Theoklis Nikolaidis & Soheil Jafari, 2022. "Advanced Control Algorithm for FADEC Systems in the Next Generation of Turbofan Engines to Minimize Emission Levels," Mathematics, MDPI, vol. 10(10), pages 1-15, May.
    10. Wang, Yaxin & Lou, Diming & Xu, Ning & Fang, Liang & Tan, Piqiang, 2021. "Energy management and emission control for range extended electric vehicles," Energy, Elsevier, vol. 236(C).
    11. Paweł Krawczyk & Artur Kopczyński & Jakub Lasocki, 2022. "Modeling and Simulation of Extended-Range Electric Vehicle with Control Strategy to Assess Fuel Consumption and CO 2 Emission for the Expected Driving Range," Energies, MDPI, vol. 15(12), pages 1-41, June.
    12. Yu, Jin & Song, Yurun & Zhang, Huasen & Dong, Xiaohan, 2022. "Novel design of compound coupled hydro-mechanical transmission on heavy-duty vehicle for energy recycling," Energy, Elsevier, vol. 239(PD).
    13. Wang, Yingli & Duan, Jialong & Zhao, Yuanyuan & Yuan, Haiwen & He, Benlin & Tang, Qunwei, 2018. "Film-type rain energy converters from conductive polymer/PtCo hybrids," Applied Energy, Elsevier, vol. 218(C), pages 317-324.
    14. Burgos Payán, Manuel & Roldan Fernandez, Juan Manuel & Maza Ortega, Jose Maria & Riquelme Santos, Jesus Manuel, 2019. "Techno-economic optimal power rating of induction motors," Applied Energy, Elsevier, vol. 240(C), pages 1031-1048.
    15. Borghi, Massimo & Mattarelli, Enrico & Muscoloni, Jarin & Rinaldini, Carlo Alberto & Savioli, Tommaso & Zardin, Barbara, 2017. "Design and experimental development of a compact and efficient range extender engine," Applied Energy, Elsevier, vol. 202(C), pages 507-526.
    16. Song, Ke & Wang, Xiaodi & Li, Feiqiang & Sorrentino, Marco & Zheng, Bailin, 2020. "Pontryagin’s minimum principle-based real-time energy management strategy for fuel cell hybrid electric vehicle considering both fuel economy and power source durability," Energy, Elsevier, vol. 205(C).
    17. Iqbal, Mehroze & Becherif, Mohamed & Ramadan, Haitham S. & Badji, Abderrezak, 2021. "Dual-layer approach for systematic sizing and online energy management of fuel cell hybrid vehicles," Applied Energy, Elsevier, vol. 300(C).
    18. Liu, Hanwu & Lei, Yulong & Fu, Yao & Li, Xingzhong, 2022. "A novel hybrid-point-line energy management strategy based on multi-objective optimization for range-extended electric vehicle," Energy, Elsevier, vol. 247(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:15:y:2022:i:19:p:6874-:d:919782. 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.