IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v15y2022i20p7629-d943435.html
   My bibliography  Save this article

A Novel Model for Wind Turbines on Trains

Author

Listed:
  • Mario Hyman

    (FedEx Services, Collierville, TN 38017, USA)

  • Mohd Hasan Ali

    (Department of EECE, The University of Memphis, Memphis, TN 38152, USA)

Abstract

Wind turbines that are consistently exposed to the air displaced by moving trains have a high potential for energy generation. Researchers have developed mathematical models to simulate wind energy generation from turbines on moving trains but there are significant gaps in the developed model theory. Most models do not consider the negative effects that additional aerodynamic drag, increased weight, and modified dimensions can have on the train’s operation. To overcome the drawbacks of existing models, this work proposes a novel approach of modeling the wind turbines on trains by considering wind turbine exposure only when the train is decelerating or stationary. There are no models that consider all of these realistic physical effects as a function of time. Real-time analysis and power-system simulations showed that the proposed model could produce over 3 MJ of net energy for favorable train trips. The simulated load profile met the demand of a 1 KW generator connected to onboard electrical components. Some recommendations on possible future research on wind turbines on trains are explained.

Suggested Citation

  • Mario Hyman & Mohd Hasan Ali, 2022. "A Novel Model for Wind Turbines on Trains," Energies, MDPI, vol. 15(20), pages 1-15, October.
  • Handle: RePEc:gam:jeners:v:15:y:2022:i:20:p:7629-:d:943435
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/15/20/7629/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/1996-1073/15/20/7629/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Chan, C.M. & Bai, H.L. & He, D.Q., 2018. "Blade shape optimization of the Savonius wind turbine using a genetic algorithm," Applied Energy, Elsevier, vol. 213(C), pages 148-157.
    2. Wong, Kok Hoe & Chong, Wen Tong & Poh, Sin Chew & Shiah, Yui-Chuin & Sukiman, Nazatul Liana & Wang, Chin-Tsan, 2018. "3D CFD simulation and parametric study of a flat plate deflector for vertical axis wind turbine," Renewable Energy, Elsevier, vol. 129(PA), pages 32-55.
    3. Mauro, S. & Brusca, S. & Lanzafame, R. & Messina, M., 2019. "CFD modeling of a ducted Savonius wind turbine for the evaluation of the blockage effects on rotor performance," Renewable Energy, Elsevier, vol. 141(C), pages 28-39.
    4. Bethi, Rajagopal Vinod & Laws, Praveen & Kumar, Pankaj & Mitra, Santanu, 2019. "Modified Savonius wind turbine for harvesting wind energy from trains moving in tunnels," Renewable Energy, Elsevier, vol. 135(C), pages 1056-1063.
    5. Bai, H.L. & Chan, C.M. & Zhu, X.M. & Li, K.M., 2019. "A numerical study on the performance of a Savonius-type vertical-axis wind turbine in a confined long channel," Renewable Energy, Elsevier, vol. 139(C), pages 102-109.
    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. Reza Norouztabar & Seyed Soheil Mousavi Ajarostaghi & Seyed Sina Mousavi & Payam Nejat & Seyed Saeid Rahimian Koloor & Mohamed Eldessouki, 2022. "On the Performance of a Modified Triple Stack Blade Savonius Wind Turbine as a Function of Geometrical Parameters," Sustainability, MDPI, vol. 14(16), pages 1-26, August.
    2. Krzysztof Kołodziejczyk & Radosław Ptak, 2022. "Numerical Investigations of the Vertical Axis Wind Turbine with Guide Vane," Energies, MDPI, vol. 15(22), pages 1-14, November.
    3. Guo, Zijian & Liu, Tanghong & Xu, Kai & Wang, Junyan & Li, Wenhui & Chen, Zhengwei, 2020. "Parametric analysis and optimization of a simple wind turbine in high speed railway tunnels," Renewable Energy, Elsevier, vol. 161(C), pages 825-835.
    4. Lu Ma & Xiaodong Wang & Jian Zhu & Shun Kang, 2019. "Dynamic Stall of a Vertical-Axis Wind Turbine and Its Control Using Plasma Actuation," Energies, MDPI, vol. 12(19), pages 1-18, September.
    5. Jintao Zhang & Chao Wang & Wenhao Liu & Jianyang Zhu & Yangyang Yan & Hui Zhao, 2023. "Optimization of the Energy Capture Performance of the Lift-Drag Hybrid Vertical-Axis Wind Turbine Based on the Taguchi Experimental Method and CFD Simulation," Sustainability, MDPI, vol. 15(11), pages 1-20, May.
    6. M. Abdelsalam, Ali & Abdelmordy, M. & Ibrahim, K.A. & Sakr, I.M., 2023. "An investigation on flow behavior and performance of a wind turbine integrated within a building tunnel," Energy, Elsevier, vol. 280(C).
    7. Jiang, Tieliu & Zhao, Yuze & Wang, Shengwen & Zhang, Lidong & Li, Guohao, 2024. "Aerodynamic characterization of a H-Darrieus wind turbine with a Drag-Disturbed Flow device installation," Energy, Elsevier, vol. 292(C).
    8. Yin, Linfei & Zhang, Bin, 2021. "Time series generative adversarial network controller for long-term smart generation control of microgrids," Applied Energy, Elsevier, vol. 281(C).
    9. Cheng, Biyi & Du, Jianjun & Yao, Yingxue, 2022. "Machine learning methods to assist structure design and optimization of Dual Darrieus Wind Turbines," Energy, Elsevier, vol. 244(PA).
    10. Hassan, Syed Saddam ul & Javaid, M. Tariq & Rauf, Umar & Nasir, Sheharyar & Shahzad, Aamer & Salamat, Shuaib, 2023. "Systematic investigation of power enhancement of Vertical Axis Wind Turbines using bio-inspired leading edge tubercles," Energy, Elsevier, vol. 270(C).
    11. Haddad, Hassan Z. & Mohamed, Mohamed H. & Shabana, Yasser M. & Elsayed, Khairy, 2023. "Optimization of Savonius wind turbine with additional blades by surrogate model using artificial neural networks," Energy, Elsevier, vol. 270(C).
    12. Hao, Daning & Qi, Lingfei & Tairab, Alaeldin M. & Ahmed, Ammar & Azam, Ali & Luo, Dabing & Pan, Yajia & Zhang, Zutao & Yan, Jinyue, 2022. "Solar energy harvesting technologies for PV self-powered applications: A comprehensive review," Renewable Energy, Elsevier, vol. 188(C), pages 678-697.
    13. Fang, Zheng & Tan, Xing & Liu, Genshuo & Zhou, Zijie & Pan, Yajia & Ahmed, Ammar & Zhang, Zutao, 2022. "A novel vibration energy harvesting system integrated with an inertial pendulum for zero-energy sensor applications in freight trains," Applied Energy, Elsevier, vol. 318(C).
    14. Acarer, Sercan & Uyulan, Çağlar & Karadeniz, Ziya Haktan, 2020. "Optimization of radial inflow wind turbines for urban wind energy harvesting," Energy, Elsevier, vol. 202(C).
    15. Kisvari, Adam & Lin, Zi & Liu, Xiaolei, 2021. "Wind power forecasting – A data-driven method along with gated recurrent neural network," Renewable Energy, Elsevier, vol. 163(C), pages 1895-1909.
    16. Mohammadi, M. & Mohammadi, R. & Ramadan, A. & Mohamed, M.H., 2018. "Numerical investigation of performance refinement of a drag wind rotor using flow augmentation and momentum exchange optimization," Energy, Elsevier, vol. 158(C), pages 592-606.
    17. Masoumi, A.P. & Tavakolpour-Saleh, A.R. & Rahideh, A., 2020. "Applying a genetic-fuzzy control scheme to an active free piston Stirling engine: Design and experiment," Applied Energy, Elsevier, vol. 268(C).
    18. Hu, Wenyu & E, Jiaqiang & Tan, Yan & Zhang, Feng & Liao, Gaoliang, 2022. "Modified wind energy collection devices for harvesting convective wind energy from cars and trucks moving in the highway," Energy, Elsevier, vol. 247(C).
    19. Rahmatian, Mohammad Ali & Hashemi Tari, Pooyan & Mojaddam, Mohammad & Majidi, Sahand, 2022. "Numerical and experimental study of the ducted diffuser effect on improving the aerodynamic performance of a micro horizontal axis wind turbine," Energy, Elsevier, vol. 245(C).
    20. Zheng, Peng & Qi, Lingfei & Sun, Mengdie & Luo, Dabing & Zhang, Zutao, 2021. "A novel wind energy harvesting system with hybrid mechanism for self-powered applications in subway tunnels," Energy, Elsevier, vol. 227(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:gam:jeners:v:15:y:2022:i:20:p:7629-:d:943435. 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.