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Effects of Damaged Rotor Blades on the Aerodynamic Behavior and Heat-Transfer Characteristics of High-Pressure Gas Turbines

Author

Listed:
  • Thanh Dam Mai

    (Department of Mechanical Engineering, Chung-Ang University, Seoul 06911, Korea)

  • Jaiyoung Ryu

    (Department of Mechanical Engineering, Chung-Ang University, Seoul 06911, Korea
    Department of Intelligent Energy and Industry, Chung-Ang University, Seoul 06911, Korea)

Abstract

Gas turbines are critical components of combined-cycle power plants because they influence the power output and overall efficiency. However, gas-turbine blades are susceptible to damage when operated under high-pressure, high-temperature conditions. This reduces gas-turbine performance and increases the probability of power-plant failure. This study compares the effects of rotor-blade damage at different locations on their aerodynamic behavior and heat-transfer properties. To this end, we considered five cases: a reference case involving a normal rotor blade and one case each of damage occurring on the pressure and suction sides of the blades’ near-tip and midspan sections. We used the Reynolds-averaged Navier-Stokes equation coupled with the k − ω SST γ turbulence model to solve the problem of high-speed, high-pressure compressible flow through the GE-E 3 gas-turbine model. The results reveal that the rotor-blade damage increases the heat-transfer coefficients of the blade and vane surfaces by approximately 1% and 0.5%, respectively. This, in turn, increases their thermal stresses, especially near the rotor-blade tip and around damaged locations. The four damaged-blade cases reveal an increase in the aerodynamic force acting on the blade/vane surfaces. This increases the mechanical stress on and reduces the fatigue life of the blade/vane components.

Suggested Citation

  • Thanh Dam Mai & Jaiyoung Ryu, 2021. "Effects of Damaged Rotor Blades on the Aerodynamic Behavior and Heat-Transfer Characteristics of High-Pressure Gas Turbines," Mathematics, MDPI, vol. 9(6), pages 1-21, March.
    • Handle: RePEc:gam:jmathe:v:9:y:2021:i:6:p:627-:d:517891
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    References listed on IDEAS

    as
    1. Liu, Zuming & Karimi, Iftekhar A., 2020. "Gas turbine performance prediction via machine learning," Energy, Elsevier, vol. 192(C).
    2. 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).
    3. Jae-Sung Oh & Taehak Kang & Seokgyun Ham & Kwan-Sup Lee & Yong-Jun Jang & Hong-Sun Ryou & Jaiyoung Ryu, 2019. "Numerical Analysis of Aerodynamic Characteristics of Hyperloop System," Energies, MDPI, vol. 12(3), pages 1-17, February.
    4. Myung Gon Choi & Jaiyoung Ryu, 2018. "Numerical Study of the Axial Gap and Hot Streak Effects on Thermal and Flow Characteristics in Two-Stage High Pressure Gas Turbine," Energies, MDPI, vol. 11(10), pages 1-15, October.
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