IDEAS home Printed from https://ideas.repec.org/a/eee/energy/v198y2020ics0360544220304874.html
   My bibliography  Save this article

A numerical and analytical coupling method for predicting the performance of intermediate-pressure steam turbines in operation

Author

Listed:
  • Yamamoto, Satoru
  • Uemura, Akihiro
  • Miyazawa, Hironori
  • Furusawa, Takashi
  • Yonezawa, Koichi
  • Umezawa, Shuichi
  • Ohmori, Shuichi
  • Suzuki, Takeshi

Abstract

This study proposes a numerical and analytical coupling method for simulating unsteady steam flow through three-stage stator and rotor blade rows of an intermediate-pressure steam turbine operating in a power plant by considering both the secular-changed blades and the leakage flows in labyrinth seals. The flow simulation was based on the in-house CFD code developed by Tohoku University, whereas the leakage flows in labyrinth seals were predicted by an analytical method. The effects of secular-changed blades and leakage flows on unsteady steam flows are discussed on the basis of time-dependent solutions. Next, the total performances obtained under conditions using manufactured and secular-changed blades both with and without leakage flows were compared with the actual data measured in operation. The obtained results indicate that the increase in static temperature at the outlet of actual intermediate-pressure steam turbines during a long-time operation is obviously caused by the secular change of blades. This paper presents the first results based on a numerical approach to clarify the temperature increase in a working intermediate-pressure steam turbine.

Suggested Citation

  • Yamamoto, Satoru & Uemura, Akihiro & Miyazawa, Hironori & Furusawa, Takashi & Yonezawa, Koichi & Umezawa, Shuichi & Ohmori, Shuichi & Suzuki, Takeshi, 2020. "A numerical and analytical coupling method for predicting the performance of intermediate-pressure steam turbines in operation," Energy, Elsevier, vol. 198(C).
    • Handle: RePEc:eee:energy:v:198:y:2020:i:c:s0360544220304874
    DOI: 10.1016/j.energy.2020.117380
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0360544220304874
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.energy.2020.117380?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. Marinai, Luca & Probert, Douglas & Singh, Riti, 2004. "Prospects for aero gas-turbine diagnostics: a review," Applied Energy, Elsevier, vol. 79(1), pages 109-126, September.
    2. Cai, Liu-xi & Wang, Shun-sen & Mao, Jing-ru & Di, Juan & Feng, Zhen-ping, 2015. "The influence of nozzle chamber structure and partial-arc admission on the erosion characteristics of solid particles in the control stage of a supercritical steam turbine," Energy, Elsevier, vol. 82(C), pages 341-352.
    3. 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.
    4. Yamamoto, Satoru, 2005. "Computation of practical flow problems with release of latent heat," Energy, Elsevier, vol. 30(2), pages 197-208.
    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. Zhang, Mingjie & Yang, Jiangang & Zhang, Wanfu & Gu, Qianlei, 2024. "Turbomachines seal flow resistance enhancement and leakage reduction based on flow control method with bow-shaped auxiliary teeth," Energy, Elsevier, vol. 300(C).
    2. Moriguchi, Shota & Miyazawa, Hironori & Furusawa, Takashi & Yamamoto, Satoru, 2021. "Large eddy simulation of a linear turbine cascade with a trailing edge cutback," Energy, Elsevier, vol. 220(C).

    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. Kang, Do Won & Kim, Tong Seop, 2018. "Model-based performance diagnostics of heavy-duty gas turbines using compressor map adaptation," Applied Energy, Elsevier, vol. 212(C), pages 1345-1359.
    2. Muhammad Baqir Hashmi & Mohammad Mansouri & Amare Desalegn Fentaye & Shazaib Ahsan & Konstantinos Kyprianidis, 2024. "An Artificial Neural Network-Based Fault Diagnostics Approach for Hydrogen-Fueled Micro Gas Turbines," Energies, MDPI, vol. 17(3), pages 1-23, February.
    3. Maria Grazia De Giorgi & Nicola Menga & Antonio Ficarella, 2023. "Exploring Prognostic and Diagnostic Techniques for Jet Engine Health Monitoring: A Review of Degradation Mechanisms and Advanced Prediction Strategies," Energies, MDPI, vol. 16(6), pages 1-37, March.
    4. 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).
    5. Mingliang Bai & Jinfu Liu & Yujia Ma & Xinyu Zhao & Zhenhua Long & Daren Yu, 2020. "Long Short-Term Memory Network-Based Normal Pattern Group for Fault Detection of Three-Shaft Marine Gas Turbine," Energies, MDPI, vol. 14(1), pages 1-22, December.
    6. Feng Lu & Jipeng Jiang & Jinquan Huang & Xiaojie Qiu, 2018. "An Iterative Reduced KPCA Hidden Markov Model for Gas Turbine Performance Fault Diagnosis," Energies, MDPI, vol. 11(7), pages 1-21, July.
    7. Jiao Liu & Jinfu Liu & Daren Yu & Myeongsu Kang & Weizhong Yan & Zhongqi Wang & Michael G. Pecht, 2018. "Fault Detection for Gas Turbine Hot Components Based on a Convolutional Neural Network," Energies, MDPI, vol. 11(8), pages 1-18, August.
    8. Chen, Yu-Zhi & Tsoutsanis, Elias & Xiang, Heng-Chao & Li, Yi-Guang & Zhao, Jun-Jie, 2022. "A dynamic performance diagnostic method applied to hydrogen powered aero engines operating under transient conditions," Applied Energy, Elsevier, vol. 317(C).
    9. Zhou, Dengji & Yao, Qinbo & Wu, Hang & Ma, Shixi & Zhang, Huisheng, 2020. "Fault diagnosis of gas turbine based on partly interpretable convolutional neural networks," Energy, Elsevier, vol. 200(C).
    10. Yunpeng Cao & Xinran Lv & Guodong Han & Junqi Luan & Shuying Li, 2019. "Research on Gas-Path Fault-Diagnosis Method of Marine Gas Turbine Based on Exergy Loss and Probabilistic Neural Network," Energies, MDPI, vol. 12(24), pages 1-17, December.
    11. Kim, Min Jae & Kim, Jeong Ho & Kim, Tong Seop, 2018. "The effects of internal leakage on the performance of a micro gas turbine," Applied Energy, Elsevier, vol. 212(C), pages 175-184.
    12. Xin Li & Fengrong Bi & Lipeng Zhang & Xiao Yang & Guichang Zhang, 2022. "An Engine Fault Detection Method Based on the Deep Echo State Network and Improved Multi-Verse Optimizer," Energies, MDPI, vol. 15(3), pages 1-17, February.
    13. Kiki Ayu & Akilu Yunusa-Kaltungo, 2020. "A Holistic Framework for Supporting Maintenance and Asset Management Life Cycle Decisions for Power Systems," Energies, MDPI, vol. 13(8), pages 1-32, April.
    14. Huang, Yufeng & Tao, Jun & Zhao, Junyi & Sun, Gang & Yin, Kai & Zhai, Junyi, 2023. "Graph structure embedded with physical constraints-based information fusion network for interpretable fault diagnosis of aero-engine," Energy, Elsevier, vol. 283(C).
    15. Ming Cheng & Qiang Zhang & Yue Cao, 2024. "An Early Warning Model for Turbine Intermediate-Stage Flux Failure Based on an Improved HEOA Algorithm Optimizing DMSE-GRU Model," Energies, MDPI, vol. 17(15), pages 1-16, July.
    16. Cheng, Xianda & Zheng, Haoran & Yang, Qian & Zheng, Peiying & Dong, Wei, 2023. "Surrogate model-based real-time gas path fault diagnosis for gas turbines under transient conditions," Energy, Elsevier, vol. 278(PA).
    17. Bai, Mingliang & Yang, Xusheng & Liu, Jinfu & Liu, Jiao & Yu, Daren, 2021. "Convolutional neural network-based deep transfer learning for fault detection of gas turbine combustion chambers," Applied Energy, Elsevier, vol. 302(C).
    18. Shun Dai & Xiaoyi Zhang & Mingyu Luo, 2024. "A Novel Data-Driven Approach for Predicting the Performance Degradation of a Gas Turbine," Energies, MDPI, vol. 17(4), pages 1-17, February.
    19. Jinfu Liu & Zhenhua Long & Mingliang Bai & Linhai Zhu & Daren Yu, 2021. "A Comparative Study on Fault Detection Methods for Gas Turbine Combustion Systems," Energies, MDPI, vol. 14(2), pages 1-31, January.
    20. Sanuri Ishak & Chong Tak Yaw & Siaw Paw Koh & Sieh Kiong Tiong & Chai Phing Chen & Talal Yusaf, 2021. "Fault Classification System for Switchgear CBM from an Ultrasound Analysis Technique Using Extreme Learning Machine," Energies, MDPI, vol. 14(19), pages 1-21, October.

    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:198:y:2020:i:c:s0360544220304874. 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.