Example of Using Particle Swarm Optimization Algorithm with Nelder–Mead Method for Flow Improvement in Axial Last Stage of Gas–Steam Turbine

Paweł Ziółkowski (), Łukasz Witanowski, Stanisław Głuch, Piotr Klonowicz, Michel Feidt and Aimad Koulali
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Paweł Ziółkowski: Institute of Energy, Faculty of Mechanical Engineering and Ship Technology, Gdańsk University of Technology, 80-233 Gdańsk, Poland
Łukasz Witanowski: Institute of Fluid-Flow Machinery Polish Academy of Sciences, 80-231 Gdańsk, Poland
Stanisław Głuch: Institute of Energy, Faculty of Mechanical Engineering and Ship Technology, Gdańsk University of Technology, 80-233 Gdańsk, Poland
Piotr Klonowicz: Institute of Fluid-Flow Machinery Polish Academy of Sciences, 80-231 Gdańsk, Poland
Michel Feidt: Laboratory of Energetics & Theoretical & Applied Mechanics (LEMTA), CNRS, Lorraine University, F-54000 Nancy, France
Aimad Koulali: Institute of Energy, Faculty of Mechanical Engineering and Ship Technology, Gdańsk University of Technology, 80-233 Gdańsk, Poland

Energies, 2024, vol. 17, issue 12, 1-29

Abstract: This article focuses principally on the comparison baseline and the optimized flow efficiency of the final stage of an axial turbine operating on a gas–steam mixture by applying a hybrid Nelder–Mead and the particle swarm optimization method. Optimization algorithms are combined with CFD calculations to determine the flowpaths and thermodynamic parameters. The working fluid in this study is a mixture of steam and gas produced in a wet combustion chamber, therefore the new turbine type is currently undergoing theoretical research. The purpose of this work is to redesign and examine the last stage of the gas–steam turbine’s flow characteristics. Among the optimized variables, there are parameters characterizing the shape of the endwall contours within the rotor domain. The values of the maximized objective function, which is the isentropic efficiency of the turbine stage, are found from the 3D RANS computation of the flowpath geometry changing during the improvement scheme. The optimization process allows the stage efficiency to be increased by almost 4 percentage points. To achieve high-quality results, a mesh of over 20 million elements is used, where the percentage error in efficiency between the previous and current mesh sizes drops below 0.05%.

Keywords: optimization; gas–steam cycle; axial turbine; CFD; last stage of low pressure; twisted blade; hybrid algorithm (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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