IDEAS home Printed from https://ideas.repec.org/a/spr/snopef/v5y2024i4d10.1007_s43069-024-00368-y.html
   My bibliography  Save this article

Design Optimization of Marine Propeller Using Elitist Particle Swarm Intelligence

Author

Listed:
  • Fahad Ali Khan

    (Pakistan Institute of Engineering & Applied Sciences)

  • Nadeem Shaukat

    (Pakistan Institute of Engineering & Applied Sciences
    Pakistan Institute of Engineering & Applied Sciences)

  • Ajmal Shah

    (Pakistan Institute of Engineering & Applied Sciences
    Pakistan Institute of Engineering & Applied Sciences)

  • Abrar Hashmi

    (Capital University of Sciences and Technology)

  • Muhammad Atiq Ur Rehman Tariq

    (Victoria University
    UET Lahore)

Abstract

Marine transportation is still the primary source of global transportation. The propeller, which is a critical component of the propulsion system, must be designed with multiple constraints and objectives to satisfy the need. Recent studies propose that utilizing an improved optimization algorithm and computational analysis would explore better designs than conventional methods. In the present study, the elitist particle swarm optimization (EPSO) technique is implemented to optimize the design of a marine propeller. Potsdam’s Conventional Propeller VP 1304 is used as a benchmark design case. Reynolds-averaged Navier–Stokes equation-based computational fluid dynamics (CFD) along with vortex lattice method (VLM) and fluid-structure interaction (FSI) model is used for computational analysis. The results obtained in this study are validated against the previously published experimental data. An optimized propeller design is proposed based on the elitist particle swarm optimization technique. It is observed that the proposed design shows improved open water performance for lower advance coefficient (J) values based on the given constraints. It is also observed that open water efficiency is improved by 7% for $$J=0.6$$ J = 0.6 compared to the original design. The one-way fluid-structure interaction analysis shows that the proposed design is structurally stable under open water test conditions.

Suggested Citation

  • Fahad Ali Khan & Nadeem Shaukat & Ajmal Shah & Abrar Hashmi & Muhammad Atiq Ur Rehman Tariq, 2024. "Design Optimization of Marine Propeller Using Elitist Particle Swarm Intelligence," SN Operations Research Forum, Springer, vol. 5(4), pages 1-28, December.
  • Handle: RePEc:spr:snopef:v:5:y:2024:i:4:d:10.1007_s43069-024-00368-y
    DOI: 10.1007/s43069-024-00368-y
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s43069-024-00368-y
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.

    File URL: https://libkey.io/10.1007/s43069-024-00368-y?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Humberto Verdejo & Victor Pino & Wolfgang Kliemann & Cristhian Becker & José Delpiano, 2020. "Implementation of Particle Swarm Optimization (PSO) Algorithm for Tuning of Power System Stabilizers in Multimachine Electric Power Systems," Energies, MDPI, vol. 13(8), pages 1-29, April.
    2. Yudong Zhang & Shuihua Wang & Genlin Ji, 2015. "A Comprehensive Survey on Particle Swarm Optimization Algorithm and Its Applications," Mathematical Problems in Engineering, Hindawi, vol. 2015, pages 1-38, October.
    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. Mariusz Korzeń & Maciej Kruszyna, 2023. "Modified Ant Colony Optimization as a Means for Evaluating the Variants of the City Railway Underground Section," IJERPH, MDPI, vol. 20(6), pages 1-15, March.
    2. Mohammad Soleimani Amiri & Rizauddin Ramli & Ahmad Barari, 2023. "Optimally Initialized Model Reference Adaptive Controller of Wearable Lower Limb Rehabilitation Exoskeleton," Mathematics, MDPI, vol. 11(7), pages 1-14, March.
    3. Byung-Ki Jeon & Eui-Jong Kim, 2021. "LSTM-Based Model Predictive Control for Optimal Temperature Set-Point Planning," Sustainability, MDPI, vol. 13(2), pages 1-14, January.
    4. Frédérique Bec & Heino Bohn Nielsen & Sarra Saïdi, 2020. "Mixed Causal–Noncausal Autoregressions: Bimodality Issues in Estimation and Unit Root Testing," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 82(6), pages 1413-1428, December.
    5. Yubin Cheon & Jaehyun Jung & Daeyeon Ki & Salman Khalid & Heung Soo Kim, 2024. "Optimization of MOSFET Copper Clip to Enhance Thermal Management Using Kriging Surrogate Model and Genetic Algorithm," Mathematics, MDPI, vol. 12(18), pages 1-21, September.
    6. Grzegorz Sroka & Mariusz Oszust, 2021. "Approximation of the Constant in a Markov-Type Inequality on a Simplex Using Meta-Heuristics," Mathematics, MDPI, vol. 9(3), pages 1-10, January.
    7. Genbao Liu & Tengfei Zhao & Hong Yan & Han Wu & Fuming Wang, 2022. "Evaluation of Urban Green Building Design Schemes to Achieve Sustainability Based on the Projection Pursuit Model Optimized by the Atomic Orbital Search," Sustainability, MDPI, vol. 14(17), pages 1-23, September.
    8. Zhaojuan Zhang & Wanliang Wang & Gaofeng Pan, 2020. "A Distributed Quantum-Behaved Particle Swarm Optimization Using Opposition-Based Learning on Spark for Large-Scale Optimization Problem," Mathematics, MDPI, vol. 8(11), pages 1-21, October.
    9. Mojgan Fayyazi & Paramjotsingh Sardar & Sumit Infent Thomas & Roonak Daghigh & Ali Jamali & Thomas Esch & Hans Kemper & Reza Langari & Hamid Khayyam, 2023. "Artificial Intelligence/Machine Learning in Energy Management Systems, Control, and Optimization of Hydrogen Fuel Cell Vehicles," Sustainability, MDPI, vol. 15(6), pages 1-38, March.
    10. Yanzheng Zhu & Yangbo Chen & Yanjun Zhao & Feng Zhou & Shichao Xu, 2023. "Application and Research of Liuxihe Model in the Simulation of Inflow Flood at Zaoshi Reservoir," Sustainability, MDPI, vol. 15(13), pages 1-14, June.
    11. Qiuping Ni & Yuanxiang Tang, 2023. "A Bibliometric Visualized Analysis and Classification of Vehicle Routing Problem Research," Sustainability, MDPI, vol. 15(9), pages 1-37, April.
    12. Chandrakant Nikam, Keval & Jathar, Laxmikant & Shelare, Sagar Dnyaneshwar & Shahapurkar, Kiran & Dambhare, Sunil & Soudagar, Manzoore Elahi M. & Mubarak, Nabisab Mujawar & Ahamad, Tansir & Kalam, M.A., 2023. "Parametric analysis and optimization of 660 MW supercritical power plant," Energy, Elsevier, vol. 280(C).
    13. Eid, Heba F. & Cuevas, Erik & Mansour, Romany F., 2024. "Autonomous bonobo optimization algorithm for power allocation in wireless networks," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 217(C), pages 294-310.
    14. Xin Peng & Hui Chen & Cong Guan, 2023. "Energy Management Optimization of Fuel Cell Hybrid Ship Based on Particle Swarm Optimization Algorithm," Energies, MDPI, vol. 16(3), pages 1-15, January.
    15. Wensheng Li & Fanke Yang & Liqiang Zhong & Hao Wu & Xiangyuan Jiang & Andrei V. Chukalin, 2023. "Attitude Control of UAVs with Search Optimization and Disturbance Rejection Strategies," Mathematics, MDPI, vol. 11(17), pages 1-16, September.
    16. Olukorede Tijani Adenuga & Senthil Krishnamurthy, 2023. "Economic Power Dispatch of a Grid-Tied Photovoltaic-Based Energy Management System: Co-Optimization Approach," Mathematics, MDPI, vol. 11(15), pages 1-22, July.
    17. Wael Korani & Malek Mouhoub, 2021. "Review on Nature-Inspired Algorithms," SN Operations Research Forum, Springer, vol. 2(3), pages 1-26, September.
    18. Anurag Gautam & Ibraheem & Gulshan Sharma & Mohammad F. Ahmer & Narayanan Krishnan, 2023. "Methods and Methodologies for Congestion Alleviation in the DPS: A Comprehensive Review," Energies, MDPI, vol. 16(4), pages 1-28, February.
    19. Cui, Huixia & Chen, Xiangyong & Guo, Ming & Jiao, Yang & Cao, Jinde & Qiu, Jianlong, 2023. "A distribution center location optimization model based on minimizing operating costs under uncertain demand with logistics node capacity scalability," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 610(C).
    20. Qian, Jing & Sun, Xiangyu & Zhong, Xiaohui & Zeng, Jiajun & Xu, Fei & Zhou, Teng & Shi, Kezhong & Li, Qingan, 2024. "Multi-objective optimization design of the wind-to-heat system blades based on the Particle Swarm Optimization algorithm," Applied Energy, Elsevier, vol. 355(C).

    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:spr:snopef:v:5:y:2024:i:4:d:10.1007_s43069-024-00368-y. 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.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.