IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v15y2023i5p4088-d1078461.html
   My bibliography  Save this article

Determining the Influence of a Magnetic Field on the Vibration and Fuel Consumption of a Heavy Diesel Engine

Author

Listed:
  • Yousef Darvishi

    (Department of Biosystems Engineering, University of Tehran, Tehran 3391653755, Iran)

  • Seyed Reza Hassan-Beygi

    (Department Agro-Technology, College of Abouraihan, University of Tehran, Tehran 3391653755, Iran)

  • Jafar Massah

    (Department Agro-Technology, College of Abouraihan, University of Tehran, Tehran 3391653755, Iran)

  • Marek Gancarz

    (Faculty of Production and Power Engineering, University of Agriculture in Kraków, Balicka 116 B, 30-149 Kraków, Poland
    Institute of Agrophysics Polish Academy of Sciences, Doświadczalna 4, 20-290 Lublin, Poland)

  • Arkadiusz Bieszczad

    (Faculty of Production and Power Engineering, University of Agriculture in Kraków, Balicka 116 B, 30-149 Kraków, Poland)

  • Hamed Karami

    (Department of Petroleum Engineering, College of Engineering, Knowledge University, Erbil 44001, Iraq)

Abstract

Most of the fuels used in internal combustion engines are liquid fuels. The magnetic behavior of fuel leads to a change in the interaction of hydrocarbon and oxygen molecules. This study aimed to evaluate the fuel consumption and engine vibration (time domain) of the Perkins A63544 diesel engine using magnetized fuel. The vibration of an internal combustion engine can cause failure in engine components and discomfort and injury to users. Engine vibration behavior changes due to changes in fuel types and engine combustion. Therefore, in this study, the vibration behavior of the tractor engine (Perkins model, four-stroke, direct injection diesel) was evaluated in stationary mode at different engine speeds due to changes in fuel types. Three accelerometers (CTC AC102 model) were used to measure the vibration acceleration. The fuels used included diesel as a normal control and fuels that had been subjected to magnetic field intensities of 1000, 2000, 3000, and 4000 gauss. The longitudinal, vertical, and lateral vibration signals with 5 levels of engine speed were measured. The results illustrated that the vibration root mean square (RMS) values were essentially ( p < 0.01) affected by the engine speed, fuel type, and their interactions. It was found that for the 4000-gauss magnetized fuel, the average vibration acceleration using the five velocity settings reduced by 15%, 15.30%, 12.40%, 12.35%, and 15.38% compared to the respective control fuels. The results showed that engine fuel consumption and specific fuel consumption decreased by 2.3% using the 4000-gauss magnetized fuel compared with the normal control fuel.

Suggested Citation

  • Yousef Darvishi & Seyed Reza Hassan-Beygi & Jafar Massah & Marek Gancarz & Arkadiusz Bieszczad & Hamed Karami, 2023. "Determining the Influence of a Magnetic Field on the Vibration and Fuel Consumption of a Heavy Diesel Engine," Sustainability, MDPI, vol. 15(5), pages 1-12, February.
  • Handle: RePEc:gam:jsusta:v:15:y:2023:i:5:p:4088-:d:1078461
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/15/5/4088/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2071-1050/15/5/4088/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Zhou, Shangyong & Gao, Jiancun & Luo, Zhenmin & Hu, Shoutao & Wang, Le & Wang, Tao, 2022. "Role of ferromagnetic metal velvet and DC magnetic field on the explosion of a C3H8/air mixture-effect on reaction mechanism," Energy, Elsevier, vol. 239(PC).
    2. Xin Liu & Na Xie & Jiandang Xue & Mengyuan Li & Chenyang Zheng & Junfeng Zhang & Yanzhou Qin & Yan Yin & Dario R. Dekel & Michael D. Guiver, 2022. "Magnetic-field-oriented mixed-valence-stabilized ferrocenium anion-exchange membranes for fuel cells," Nature Energy, Nature, vol. 7(4), pages 329-339, April.
    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. Wen, Hu & Yan, Li & Jin, Yongfei & Wang, Zhipeng & Guo, Jun & Deng, Jun, 2023. "Coalbed methane concentration prediction and early-warning in fully mechanized mining face based on deep learning," Energy, Elsevier, vol. 264(C).
    2. Ramesh K. Singh & John C. Douglin & Lanjie Jiang & Karam Yassin & Simon Brandon & Dario R. Dekel, 2023. "CoO x -Fe 3 O 4 /N-rGO Oxygen Reduction Catalyst for Anion-Exchange Membrane Fuel Cells," Energies, MDPI, vol. 16(8), pages 1-18, April.

    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:jsusta:v:15:y:2023:i:5:p:4088-:d:1078461. 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.