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

Mehran Ansari, Vahid Esfahanian, Mohammad Javad Izadi, Alireza Tavakoli, Hosein Bashi and Mohammad Kordi

Energy, 2024, vol. 304, issue C

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.

Keywords: Steam; Turbine; Wet steam; Loss; Symbolic regression; Baumann rule (search for similar items in EconPapers)
Date: 2024
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Persistent link: https://EconPapers.repec.org/RePEc:eee:energy:v:304:y:2024:i:c:s0360544224018693

DOI: 10.1016/j.energy.2024.132095

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