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Development and validation of a full-range performance analysis model for a three-spool gas turbine with turbine cooling

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  • Song, Yin
  • Gu, Chun-wei
  • Ji, Xing-xing

Abstract

The performance analysis of a gas turbine is important for both its design and its operation. For modern gas turbines, the cooling flow introduces a noteworthy thermodynamic loss; thus, the determination of the cooling flow rate will clearly influence the accuracy of performance calculations. In this paper, a full-range performance analysis model is established for a three-spool gas turbine with an open-circuit convective blade cooling system. A hybrid turbine cooling model is embedded in the analysis to predict the amount of cooling air accurately and thus to remove the errors induced by the relatively arbitrary value of cooling air requirements in the previous research. The model is subsequently used to calculate the gas turbine performance; the calculation results are validated with detailed test data. Furthermore, multistage conjugate heat transfer analysis is performed for the turbine section. The results indicate that with the same coolant condition and flow rate as those in the performance analysis, the blade metal has been effectively cooled; in addition, the maximum temperature predicted by conjugate heat transfer analysis is close to the corresponding value in the cooling model. Hence, the present model provides an effective tool for analyzing the performance of a gas turbine with cooling.

Suggested Citation

  • Song, Yin & Gu, Chun-wei & Ji, Xing-xing, 2015. "Development and validation of a full-range performance analysis model for a three-spool gas turbine with turbine cooling," Energy, Elsevier, vol. 89(C), pages 545-557.
    • Handle: RePEc:eee:energy:v:89:y:2015:i:c:p:545-557
    DOI: 10.1016/j.energy.2015.06.015
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    Cited by:

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    2. Ba, Wei & Wang, Xiao-chen & Li, Xue-song & Ren, Xiao-dong & Gu, Chun-wei, 2019. "Definition of cycle based comprehensive efficiency of a cooled turbine," Energy, Elsevier, vol. 168(C), pages 601-608.
    3. Cheng, Xianda & Zheng, Haoran & Dong, Wei & Yang, Xuesen, 2023. "Performance prediction of marine intercooled cycle gas turbine based on expanded similarity parameters," Energy, Elsevier, vol. 265(C).
    4. Ma, Yujia & Liu, Jinfu & Zhu, Linhai & Li, Qi & Guo, Yaqiong & Liu, Huanpeng & Yu, Daren, 2022. "Multi-objective performance optimization and control for gas turbine Part-load operation Energy-saving and NOx emission reduction," Applied Energy, Elsevier, vol. 320(C).
    5. Lee, Jae Hong & Kim, Tong Seop & Kim, Eui-hwan, 2017. "Prediction of power generation capacity of a gas turbine combined cycle cogeneration plant," Energy, Elsevier, vol. 124(C), pages 187-197.
    6. Liu, Zuming & Karimi, Iftekhar A., 2020. "Gas turbine performance prediction via machine learning," Energy, Elsevier, vol. 192(C).
    7. 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).

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