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A Design Optimization of Organic Rankine Cycle Turbine Blades with Radial Basis Neural Network

Author

Listed:
  • Jong-Beom Seo

    (Korea Research Institute of Ships & Ocean Engineering, Daejeon 34103, Republic of Korea)

  • Hosaeng Lee

    (Korea Research Institute of Ships & Ocean Engineering, Daejeon 34103, Republic of Korea)

  • Sang-Jo Han

    (Department of Mechanical and Automotive Engineering, Seoul National University of Science and Technology, Seoul 01811, Republic of Korea)

Abstract

In the present study, a 100 kW organic Rankine cycle is suggested to recover heat energy from commercial ships. A radial-type turbine is employed with R1233zd(E) and back-to-back layout. To improve the performance of an organic Rankine power system, the efficiency of the turbine is significant. With the conventional approach, the optimization of a turbine requires a considerable amount of time and involves substantial costs. By combining design of experiments, an artificial neural network, and Latin hypercube sampling, it becomes possible to reduce costs and achieve rapid optimization. A radial basis neural network with machine learning technique, known for its advantages of being fast and easily applicable, has been implemented. Using such an approach, an increase in efficiency greater than 1% was achieved with minimal design changes at the first and second turbines.

Suggested Citation

  • Jong-Beom Seo & Hosaeng Lee & Sang-Jo Han, 2023. "A Design Optimization of Organic Rankine Cycle Turbine Blades with Radial Basis Neural Network," Energies, MDPI, vol. 17(1), pages 1-18, December.
    • Handle: RePEc:gam:jeners:v:17:y:2023:i:1:p:26-:d:1303763
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    References listed on IDEAS

    as
    1. Dawo, Fabian & Fleischmann, Jonas & Kaufmann, Florian & Schifflechner, Christopher & Eyerer, Sebastian & Wieland, Christoph & Spliethoff, Hartmut, 2021. "R1224yd(Z), R1233zd(E) and R1336mzz(Z) as replacements for R245fa: Experimental performance, interaction with lubricants and environmental impact," Applied Energy, Elsevier, vol. 288(C).
    2. Guillaume, Ludovic & Legros, Arnaud & Desideri, Adriano & Lemort, Vincent, 2017. "Performance of a radial-inflow turbine integrated in an ORC system and designed for a WHR on truck application: An experimental comparison between R245fa and R1233zd," Applied Energy, Elsevier, vol. 186(P3), pages 408-422.
    3. Sun, Haiying & Qiu, Changyu & Lu, Lin & Gao, Xiaoxia & Chen, Jian & Yang, Hongxing, 2020. "Wind turbine power modelling and optimization using artificial neural network with wind field experimental data," Applied Energy, Elsevier, vol. 280(C).
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