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Design optimization workflow and performance analysis for contoured endwalls of axial turbines

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  • Kadhim, Hakim T.
  • Rona, Aldo

Abstract

Advances in computer-based optimization techniques can be used to enhance the efficiency of energy conversions processes, such as by reducing the aerodynamic loss in thermal power plant turbomachines. One viable approach for reducing this flow energy loss is by endwall contouring. This paper implements a design optimization workflow for the casing geometry of a 1.5 stage axial turbine, towards mitigating secondary flows. Two different parametric casing surface definitions are used in the optimization process. The first method is a new nonaxisymmetric casing design using a novel surface definition. The second method is an established diffusion design technique. The designs are tested on a three-dimensional axial turbine RANS model. Computer-based optimization of the surface topology is demonstrated towards automating the design process. This is implemented using Automated Process and Optimization Workbench (APOW) software. Kriging is used to accelerate the optimization process. The optimization and its sensitivity analysis give confidence that a good predictive ability is obtained by the Kriging surrogate model used in the prototype design process tested in this work. A flow analysis confirms the positive impact of the optimized casing groove design on the stage isentropic efficiency compared to the diffusion design and compared to the benchmark axisymmetric design.

Suggested Citation

  • Kadhim, Hakim T. & Rona, Aldo, 2018. "Design optimization workflow and performance analysis for contoured endwalls of axial turbines," Energy, Elsevier, vol. 149(C), pages 875-889.
    • Handle: RePEc:eee:energy:v:149:y:2018:i:c:p:875-889
    DOI: 10.1016/j.energy.2018.02.001
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    References listed on IDEAS

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    1. Song, Yanping & Sun, Xiaojing & Huang, Diangui, 2017. "Preliminary design and performance analysis of a centrifugal turbine for Organic Rankine Cycle (ORC) applications," Energy, Elsevier, vol. 140(P1), pages 1239-1251.
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    Cited by:

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    2. Perera, A.T.D. & Wickramasinghe, P.U. & Nik, Vahid M. & Scartezzini, Jean-Louis, 2019. "Machine learning methods to assist energy system optimization," Applied Energy, Elsevier, vol. 243(C), pages 191-205.
    3. Wang, Yanhong & Cao, Lihua & Li, Xingcan & Wang, Jiaxing & Hu, Pengfei & Li, Bo & Li, Yong, 2020. "A novel thermodynamic method and insight of heat transfer characteristics on economizer for supercritical thermal power plant," Energy, Elsevier, vol. 191(C).
    4. Li, Jinxing & Liu, Tianyuan & Wang, Yuqi & Xie, Yonghui, 2022. "Integrated graph deep learning framework for flow field reconstruction and performance prediction of turbomachinery," Energy, Elsevier, vol. 254(PC).
    5. Li, Jinxing & Liu, Tianyuan & Zhu, Guangya & Li, Yunzhu & Xie, Yonghui, 2023. "Uncertainty quantification and aerodynamic robust optimization of turbomachinery based on graph learning methods," Energy, Elsevier, vol. 273(C).

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