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

Author

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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)

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%.

Suggested Citation

  • Paweł Ziółkowski & Łukasz Witanowski & Stanisław Głuch & Piotr Klonowicz & Michel Feidt & Aimad Koulali, 2024. "Example of Using Particle Swarm Optimization Algorithm with Nelder–Mead Method for Flow Improvement in Axial Last Stage of Gas–Steam Turbine," Energies, MDPI, vol. 17(12), pages 1-29, June.
    • Handle: RePEc:gam:jeners:v:17:y:2024:i:12:p:2816-:d:1411032
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    References listed on IDEAS

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    1. Fan, Shu-Kai S. & Zahara, Erwie, 2007. "A hybrid simplex search and particle swarm optimization for unconstrained optimization," European Journal of Operational Research, Elsevier, vol. 181(2), pages 527-548, September.
    2. Yong Ma & Aiming Zhang & Lele Yang & Chao Hu & Yue Bai, 2019. "Investigation on Optimization Design of Offshore Wind Turbine Blades based on Particle Swarm Optimization," Energies, MDPI, vol. 12(10), pages 1-18, May.
    3. Pedroso, Daniel Travieso & Machin, Einara Blanco & Proenza Pérez, Nestor & Braga, Lúcia Bollini & Silveira, José Luz, 2017. "Technical assessment of the Biomass Integrated Gasification/Gas Turbine Combined Cycle (BIG/GTCC) incorporation in the sugarcane industry," Renewable Energy, Elsevier, vol. 114(PB), pages 464-479.
    4. Zhonghe Han & Wei Zeng & Xu Han & Peng Xiang, 2018. "Investigating the Dehumidification Characteristics of Turbine Stator Cascades with Parallel Channels," Energies, MDPI, vol. 11(9), pages 1-17, September.
    5. Badur, Janusz & Bryk, Mateusz, 2019. "Accelerated start-up of the steam turbine by means of controlled cooling steam injection," Energy, Elsevier, vol. 173(C), pages 1242-1255.
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    Cited by:

    1. Ziółkowski, Paweł & Witanowski, Łukasz & Klonowicz, Piotr & Mikielewicz, Dariusz, 2024. "High-speed multi-stage gas-steam turbine with flow bleeding in a novel thermodynamic cycle for decarbonizing power generation," Renewable Energy, Elsevier, vol. 237(PB).

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