IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v15y2022i19p6898-d920423.html
   My bibliography  Save this article

Method and a Device for Testing the Friction Force in Precision Pairs of Injection Apparatus of the Self-Ignition Engines

Author

Listed:
  • Jan Monieta

    (Maritime University of Szczecin, Wały Chrobrego 1-2, 70-500 Szczecin, Poland)

Abstract

This article reviews the state of the knowledge and technology in the field of friction-loss measurements in internal combustion piston engines. The dependencies that describe the loss of energy in combustion engines and injection apparatus are presented. Currently, very little can be found in the literature on the study of frictional forces in injection apparatus, but mainly in the piston–cylinder group, so this work significantly fills that gap. The aim of this article is to construct a device and to develop a method for assessing the technical state of injector nozzles to minimize friction losses in internal combustion engines at the stages of evaluation, design, production and operation. This article presents a stand for determining the maximum friction forces due to gravity loading by water-jet control. This article also presents test results on the maximum friction force between a needle and a body of injector nozzles in piston combustion engines on a designed and purpose-built stand outside of the combustion engine. Various designs and injector nozzles are made from various types of alloy steel for marine and automotive piston internal combustion engines fueled with distillation or residual fuels, and are tested. The research concerned conventional elements for the injection apparatus as well as electronically controlled subsystems. Precision pairs of injection equipment are selected for the tests: new ones are employed after the storage period and operated in natural conditions. The elements dismantled from the internal combustion engines are tested in the presence of fuel or calibration oil of similar properties. The maximum static frictional forces under the hydrostatic loading are measured, alongside the parameters for the dynamic movement of the nozzle needles from bodies of the injector nozzle as time, speed, acceleration and dynamic force. The influence of the angular position of the needle in relation to the bodies of the precision pairs conventional internal combustion engines, the diametral clearance between the nozzle body and needle, and the surface conditions on the values of the maximum friction force are also presented. Errors in shape and position result in the uniqueness of the friction force at the mutual angular position of the needle in relation to the nozzle body, and the decrease in diametral clearance and deterioration of the surface state increase the friction losses. A model was elaborated of the influence of various factors on the value of the maximum friction force.

Suggested Citation

  • Jan Monieta, 2022. "Method and a Device for Testing the Friction Force in Precision Pairs of Injection Apparatus of the Self-Ignition Engines," Energies, MDPI, vol. 15(19), pages 1-19, September.
  • Handle: RePEc:gam:jeners:v:15:y:2022:i:19:p:6898-:d:920423
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/15/19/6898/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/1996-1073/15/19/6898/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Zhijian Wang & Shijin Shuai & Zhijie Li & Wenbin Yu, 2021. "A Review of Energy Loss Reduction Technologies for Internal Combustion Engines to Improve Brake Thermal Efficiency," Energies, MDPI, vol. 14(20), pages 1-18, October.
    2. Yan, Ran & Wang, Shuaian & Du, Yuquan, 2020. "Development of a two-stage ship fuel consumption prediction and reduction model for a dry bulk ship," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 138(C).
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Jan Monieta & Lech Kasyk, 2023. "Application of Machine Learning to Classify the Technical Condition of Marine Engine Injectors Based on Experimental Vibration Displacement Parameters," Energies, MDPI, vol. 16(19), pages 1-21, September.

    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. Meng, Bin & Chen, Shuiyang & Haralambides, Hercules & Kuang, Haibo & Fan, Lidong, 2023. "Information spillovers between carbon emissions trading prices and shipping markets: A time-frequency analysis," Energy Economics, Elsevier, vol. 120(C).
    2. Kiriakos Alexiou & Efthimios G. Pariotis & Helen C. Leligou & Theodoros C. Zannis, 2022. "Towards Data-Driven Models in the Prediction of Ship Performance (Speed—Power) in Actual Seas: A Comparative Study between Modern Approaches," Energies, MDPI, vol. 15(16), pages 1-18, August.
    3. Yang, Dong & Liao, Shiguan & Venus Lun, Y.H & Bai, Xiwen, 2023. "Towards sustainable port management: Data-driven global container ports turnover rate assessment," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 175(C).
    4. Nguyen, Son & Fu, Xiuju & Ogawa, Daichi & Zheng, Qin, 2023. "An application-oriented testing regime and multi-ship predictive modeling for vessel fuel consumption prediction," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 177(C).
    5. Tayfun Uyanık & Yunus Yalman & Özcan Kalenderli & Yasin Arslanoğlu & Yacine Terriche & Chun-Lien Su & Josep M. Guerrero, 2022. "Data-Driven Approach for Estimating Power and Fuel Consumption of Ship: A Case of Container Vessel," Mathematics, MDPI, vol. 10(22), pages 1-21, November.
    6. Wen Yi & Robyn Phipps & Hans Wang, 2020. "Sustainable Ship Loading Planning for Prefabricated Products in the Construction Industry," Sustainability, MDPI, vol. 12(21), pages 1-12, October.
    7. Yan, Ran & Yang, Dong & Wang, Tianyu & Mo, Haoyu & Wang, Shuaian, 2024. "Improving ship energy efficiency: Models, methods, and applications," Applied Energy, Elsevier, vol. 368(C).
    8. Filom, Siyavash & Amiri, Amir M. & Razavi, Saiedeh, 2022. "Applications of machine learning methods in port operations – A systematic literature review," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 161(C).
    9. Wu, Lingxiao & Wang, Shuaian & Laporte, Gilbert, 2021. "The Robust Bulk Ship Routing Problem with Batched Cargo Selection," Transportation Research Part B: Methodological, Elsevier, vol. 143(C), pages 124-159.
    10. Yu, Jingjing & Tang, Guolei & Song, Xiangqun, 2022. "Collaboration of vessel speed optimization with berth allocation and quay crane assignment considering vessel service differentiation," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 160(C).
    11. Yan, Ran & Wang, Shuaian & Psaraftis, Harilaos N., 2021. "Data analytics for fuel consumption management in maritime transportation: Status and perspectives," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 155(C).
    12. Zongyu Yue & Haifeng Liu, 2023. "Advanced Research on Internal Combustion Engines and Engine Fuels," Energies, MDPI, vol. 16(16), pages 1-8, August.
    13. Ruan, Zhang & Huang, Lianzhong & Wang, Kai & Ma, Ranqi & Wang, Zhongyi & Zhang, Rui & Zhao, Haoyang & Wang, Cong, 2024. "A novel prediction method of fuel consumption for wing-diesel hybrid vessels based on feature construction," Energy, Elsevier, vol. 286(C).
    14. Shang, Gang & Xu, Liyun & Tian, Jinzhu & Cai, Dongwei & Xu, Zhun & Zhou, Zhuo, 2023. "A real-time green construction optimization strategy for engineering vessels considering fuel consumption and productivity: A case study on a cutter suction dredger," Energy, Elsevier, vol. 274(C).
    15. Tayfun Uyanık & Nur Najihah Abu Bakar & Özcan Kalenderli & Yasin Arslanoğlu & Josep M. Guerrero & Abderezak Lashab, 2023. "A Data-Driven Approach for Generator Load Prediction in Shipboard Microgrid: The Chemical Tanker Case Study," Energies, MDPI, vol. 16(13), pages 1-20, June.
    16. Philip Cammin & Jingjing Yu & Stefan Voß, 2023. "Tiered prediction models for port vessel emissions inventories," Flexible Services and Manufacturing Journal, Springer, vol. 35(1), pages 142-169, March.
    17. Ran Yan & Wen Yi & Shuaian Wang, 2022. "Predicting Maximum Work Duration for Construction Workers," Sustainability, MDPI, vol. 14(17), pages 1-12, September.
    18. Beullens, Patrick & Ge, Fangsheng & Hudson, Dominic, 2023. "The economic ship speed under time charter contract—A cash flow approach," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 170(C).
    19. Tan, Zhijia & Zeng, Xianyang & Shao, Shuai & Chen, Jihong & Wang, Hua, 2022. "Scrubber installation and green fuel for inland river ships with non-identical streamflow," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 161(C).
    20. Wang, Shuaian & Yan, Ran, 2023. "Fundamental challenge and solution methods in prescriptive analytics for freight transportation," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 169(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:19:p:6898-:d:920423. 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.