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Development of a new expression for predicting wet steam loss coefficient in steam turbines based on CFD and symbolic regression

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Listed:
  • Ansari, Mehran
  • Esfahanian, Vahid
  • Izadi, Mohammad Javad
  • Tavakoli, Alireza
  • Bashi, Hosein
  • Kordi, Mohammad

Abstract

A significant portion of the total losses in steam turbines is attributed to the thermodynamic losses of wet steam flow, which makes its prediction crucial in the design process. The Baumann rule has been used extensively in the literature, but its accuracy in some cases needs to be better due to the constant wet steam loss coefficient (α=1). To overcome the shortcomings of this rule, genetic symbolic regression is employed to derive a general expression for predicting wet steam loss for the first time. Therefore, based on Sobol sampling, 2080, two-dimensional CFD simulations for both reaction and impulse blades with different boundary conditions were performed. The data were employed to devise a new expression for wet steam loss prediction. The novel expression is verified using an in-house mean-line code on an industrial steam turbine. The new expression outperforms the Baumann rule by up to 2 % in efficiency prediction and 46 % in design pressure ratio prediction.

Suggested Citation

  • Ansari, Mehran & Esfahanian, Vahid & Izadi, Mohammad Javad & Tavakoli, Alireza & Bashi, Hosein & Kordi, Mohammad, 2024. "Development of a new expression for predicting wet steam loss coefficient in steam turbines based on CFD and symbolic regression," Energy, Elsevier, vol. 304(C).
    • Handle: RePEc:eee:energy:v:304:y:2024:i:c:s0360544224018693
    DOI: 10.1016/j.energy.2024.132095
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    References listed on IDEAS

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    1. Ansari, Mehran & Esfahanian, Vahid & Izadi, Mohammad Javad & Bashi, Hosein & Tavakoli, Alireza & Kordi, Mohammad, 2023. "Implementation of hot steam injection in steam turbine design: A novel mean-line method coupled with multi-objective optimization and neural network," Energy, Elsevier, vol. 283(C).
    2. Zhang, Guojie & Wang, Xiaogang & Jin, Zunlong & Dykas, Sławomir & Smołka, Krystian, 2023. "Numerical study of the loss and power prediction based on a modified non-equilibrium condensation model in a 200 MW industrial-scale steam turbine under different operation conditions," Energy, Elsevier, vol. 275(C).
    3. Han, Xu & Zeng, Wei & Han, Zhonghe, 2019. "Investigation of the comprehensive performance of turbine stator cascades with heating endwall fences," Energy, Elsevier, vol. 174(C), pages 1188-1199.
    4. Hou, Guolian & Wang, Junjie & Fan, Yuzhen, 2024. "Multistep short-term wind power forecasting model based on secondary decomposition, the kernel principal component analysis, an enhanced arithmetic optimization algorithm, and error correction," Energy, Elsevier, vol. 286(C).
    5. Vatanmakan, Masoud & Lakzian, Esmail & Mahpeykar, Mohammad Reza, 2018. "Investigating the entropy generation in condensing steam flow in turbine blades with volumetric heating," Energy, Elsevier, vol. 147(C), pages 701-714.
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    Cited by:

    1. Zhang, Shoubing & Jin, Zunlong & Zhang, Guojie, 2026. "Optimization of liquid droplet injection in steam turbine blades: Loss reduction, droplet size control, and performance enhancement," Energy, Elsevier, vol. 347(C).

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