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Degradation performance rapid prediction and multi-objective operation optimization of gas turbine blades

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  • Shi, Dongbo
  • Liao, Guangqing
  • Meng, Yue
  • Zhang, Di
  • Xie, Yonghui

Abstract

The performance degradation of high-temperature blades will affect the safety and efficiency of operation. In response to issues such as ignoring strength characteristics, slow degradation performance prediction, insufficient applicability and accuracy of traditional proxy models, and long operation optimization cycle, a degradation performance prediction model of turbine blades based on the back propagation neural network is established in this research. Furthermore, the efficiency-stress multi-objective operation optimization method based on genetic algorithm is proposed. The results show that the R-square value of the degradation performance neural network prediction model is greater than 0.9 for predicting characteristic parameters. The prediction speed is improved by 104 orders of magnitude compared to the traditional computational fluid dynamics method. The influence mechanisms of turbine outlet flow rate, cooling gas inlet total temperature, and cooling gas flow rate on the degradation performance are revealed. Under fouling extreme conditions and corrosion/wear extreme conditions, the operating parameters of turbine blades are optimized. The maximum values of the efficiency improvement, power increase and maximum von Mises stress reduction are 0.74 %, 2.13 MW and 43 MPa, respectively. This study can provide an effective and novel method for real-time degradation performance prediction and rapid operating condition optimization of turbine blades.

Suggested Citation

  • Shi, Dongbo & Liao, Guangqing & Meng, Yue & Zhang, Di & Xie, Yonghui, 2024. "Degradation performance rapid prediction and multi-objective operation optimization of gas turbine blades," Energy, Elsevier, vol. 305(C).
    • Handle: RePEc:eee:energy:v:305:y:2024:i:c:s0360544224019698
    DOI: 10.1016/j.energy.2024.132195
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    References listed on IDEAS

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    1. Yu, Bosheng & Cao, Li'ang & Xie, Daxing & Chen, Jinwei & Zhang, Huisheng, 2025. "Fault diagnosis of gas turbine based on feature fusion cascade neural network," Energy, Elsevier, vol. 321(C).

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