IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v17y2023i1p26-d1303763.html
   My bibliography  Save this article

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
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/17/1/26/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/17/1/26/
    Download Restriction: no
    ---><---

    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. Liu, Zuming & Karimi, Iftekhar A., 2020. "Gas turbine performance prediction via machine learning," Energy, Elsevier, vol. 192(C).
    3. Du, Qiuwan & Li, Yunzhu & Yang, Like & Liu, Tianyuan & Zhang, Di & Xie, Yonghui, 2022. "Performance prediction and design optimization of turbine blade profile with deep learning method," Energy, Elsevier, vol. 254(PA).
    4. 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.
    5. 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).
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Jiang, Chiju & Zhang, Weihao & Li, Ya & Li, Lele & Wang, Yufan & Huang, Dongming, 2023. "Multi-scale Pix2Pix network for high-fidelity prediction of adiabatic cooling effectiveness in turbine cascade," Energy, Elsevier, vol. 265(C).
    2. Du, Qiuwan & Li, Yunzhu & Yang, Like & Liu, Tianyuan & Zhang, Di & Xie, Yonghui, 2022. "Performance prediction and design optimization of turbine blade profile with deep learning method," Energy, Elsevier, vol. 254(PA).
    3. Du, Qiuwan & Yang, Like & Li, Liangliang & Liu, Tianyuan & Zhang, Di & Xie, Yonghui, 2022. "Aerodynamic design and optimization of blade end wall profile of turbomachinery based on series convolutional neural network," Energy, Elsevier, vol. 244(PA).
    4. Mingliang Bai & Jinfu Liu & Yujia Ma & Xinyu Zhao & Zhenhua Long & Daren Yu, 2020. "Long Short-Term Memory Network-Based Normal Pattern Group for Fault Detection of Three-Shaft Marine Gas Turbine," Energies, MDPI, vol. 14(1), pages 1-22, December.
    5. Lee, Seung Joo & Shon, Byung Hoon & Jung, Chung Woo & Kang, Yong Tae, 2018. "A novel type solar assisted heat pump using a low GWP refrigerant (R-1233zd(E)) with the flexible solar collector," Energy, Elsevier, vol. 149(C), pages 386-396.
    6. Waqar Muhammad Ashraf & Ghulam Moeen Uddin & Syed Muhammad Arafat & Sher Afghan & Ahmad Hassan Kamal & Muhammad Asim & Muhammad Haider Khan & Muhammad Waqas Rafique & Uwe Naumann & Sajawal Gul Niazi &, 2020. "Optimization of a 660 MW e Supercritical Power Plant Performance—A Case of Industry 4.0 in the Data-Driven Operational Management Part 1. Thermal Efficiency," Energies, MDPI, vol. 13(21), pages 1-33, October.
    7. Li, Zuobiao & Wen, Fengbo & Wan, Chenxin & Zhao, Zhiyuan & Luo, Yuxi & Wen, Dongsheng, 2024. "A pyramid-style neural network model with alterable input for reconstruction of physics field on turbine blade surface from various sparse measurements," Energy, Elsevier, vol. 308(C).
    8. Sun, Shilin & Wang, Tianyang & Chu, Fulei, 2022. "In-situ condition monitoring of wind turbine blades: A critical and systematic review of techniques, challenges, and futures," Renewable and Sustainable Energy Reviews, Elsevier, vol. 160(C).
    9. Tavakol Aghaei, Vahid & Ağababaoğlu, Arda & Bawo, Biram & Naseradinmousavi, Peiman & Yıldırım, Sinan & Yeşilyurt, Serhat & Onat, Ahmet, 2023. "Energy optimization of wind turbines via a neural control policy based on reinforcement learning Markov chain Monte Carlo algorithm," Applied Energy, Elsevier, vol. 341(C).
    10. Li, Rui & Zhang, Jincheng & Zhao, Xiaowei, 2022. "Dynamic wind farm wake modeling based on a Bilateral Convolutional Neural Network and high-fidelity LES data," Energy, Elsevier, vol. 258(C).
    11. 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).
    12. Li, Yunzhu & Liu, Tianyuan & Wang, Yuqi & Xie, Yonghui, 2022. "Deep learning based real-time energy extraction system modeling for flapping foil," Energy, Elsevier, vol. 246(C).
    13. Li, Tenghui & Yang, Jin & Ioannou, Anastasia, 2024. "Data-driven control of wind turbine under online power strategy via deep learning and reinforcement learning," Renewable Energy, Elsevier, vol. 234(C).
    14. Li, Jingyi & Gallego-Schmid, Alejandro & Stamford, Laurence, 2024. "Integrated sustainability assessment of repurposing onshore abandoned wells for geothermal power generation," Applied Energy, Elsevier, vol. 359(C).
    15. Li, Zuobiao & Wen, Fengbo & Liu, Zhongqi & Luo, Yuxi & Zhao, Zhiyuan & Wen, Dongsheng & Wang, Songtao, 2025. "A novel dual attention network for sparse reconstruction of turbine blade surface fields," Energy, Elsevier, vol. 317(C).
    16. Cheng, Xianda & Zheng, Haoran & Yang, Qian & Zheng, Peiying & Dong, Wei, 2023. "Surrogate model-based real-time gas path fault diagnosis for gas turbines under transient conditions," Energy, Elsevier, vol. 278(PA).
    17. Cheng, Biyi & Yao, Yingxue, 2023. "Machine learning based surrogate model to analyze wind tunnel experiment data of Darrieus wind turbines," Energy, Elsevier, vol. 278(PA).
    18. Lei, Jianhong & Li, Jing & Wu, Shaohua & Li, Haoxing & Amaratunga, Gehan A.J. & Han, Xu & Yang, Wenming, 2024. "An advanced high dimensional model representation approach for internal combustion engine modeling and optimization," Energy, Elsevier, vol. 311(C).
    19. Da Lio, Luca & Manente, Giovanni & Lazzaretto, Andrea, 2017. "A mean-line model to predict the design efficiency of radial inflow turbines in organic Rankine cycle (ORC) systems," Applied Energy, Elsevier, vol. 205(C), pages 187-209.
    20. Mohan, Sooraj & Dinesha, P. & Campana, Pietro Elia, 2022. "ANN-PSO aided selection of hydrocarbons as working fluid for low-temperature organic Rankine cycle and thermodynamic evaluation of optimal working fluid," Energy, Elsevier, vol. 259(C).

    More about this item

    Keywords

    ;
    ;
    ;
    ;

    JEL classification:

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jeners:v:17:y:2023:i:1:p:26-:d:1303763. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.