IDEAS home Printed from https://ideas.repec.org/a/eee/energy/v292y2024ics036054422400286x.html
   My bibliography  Save this article

Study of RP-3/n-butanol fuel spray characteristics and ANN prediction of spray tip penetration

Author

Listed:
  • Zhang, Zhicheng
  • Wei, Shengli
  • Zhang, Shaobang
  • Ni, Shidong

Abstract

The blending of aviation fuel with biomass is currently an effective way to meet the demand for sustainable aviation fuel. This study tested the physical parameters of fuel blends of 0∼30 % volume fraction n-butanol with RP-3 aviation kerosene, and the macroscopic characteristics of the spray penetration tip (STP), spray cone angle (SCA), and spray area (SA) of the blends were investigated by computational fluid dynamics at different injection pressures (100–160 MPa) and ambient pressures (0.8–1.5 MPa). A model using artificial neural networks (ANN) was built to forecast the STP of the fuel. The findings indicate that the blending of n-butanol has little impact on the spray characteristics of the fuel. The STP and SA of the blended fuel rise but have a small effect on the SCA as injection pressure increases. The STP and SA of the fuel gradually decrease, while the SCA becomes larger as ambient pressure increases. NET9 which has 6 input layers, 9 hidden layers and 1 output layer with the best performance (RMSE: 0.46213, R2: 0.99969) was selected and the prediction error of NET9 was less than 3 %. This indicates that the model has the best prediction ability and can be used in subsequent research.

Suggested Citation

  • Zhang, Zhicheng & Wei, Shengli & Zhang, Shaobang & Ni, Shidong, 2024. "Study of RP-3/n-butanol fuel spray characteristics and ANN prediction of spray tip penetration," Energy, Elsevier, vol. 292(C).
    • Handle: RePEc:eee:energy:v:292:y:2024:i:c:s036054422400286x
    DOI: 10.1016/j.energy.2024.130515
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S036054422400286X
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.energy.2024.130515?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. Bohl, Thomas & Tian, Guohong & Smallbone, Andrew & Roskilly, Anthony P., 2017. "Macroscopic spray characteristics of next-generation bio-derived diesel fuels in comparison to mineral diesel," Applied Energy, Elsevier, vol. 186(P3), pages 562-573.
    2. Tian, Junjian & Liu, Yu & Bi, Haobo & Li, Fengyu & Bao, Lin & Han, Kai & Zhou, Wenliang & Ni, Zhanshi & Lin, Qizhao, 2022. "Experimental study on the spray characteristics of octanol diesel and prediction of spray tip penetration by ANN model," Energy, Elsevier, vol. 239(PA).
    3. Kim, Hyung Jun & Park, Su Han & Lee, Chang Sik, 2016. "Impact of fuel spray angles and injection timing on the combustion and emission characteristics of a high-speed diesel engine," Energy, Elsevier, vol. 107(C), pages 572-579.
    4. Verger, Thibault & Azimov, Ulugbek & Adeniyi, Oladapo, 2022. "Biomass-based fuel blends as an alternative for the future heavy-duty transport: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 161(C).
    5. Taghavifar, Hadi & Khalilarya, Shahram & Jafarmadar, Samad, 2014. "Diesel engine spray characteristics prediction with hybridized artificial neural network optimized by genetic algorithm," Energy, Elsevier, vol. 71(C), pages 656-664.
    6. Feng, Zehao & Zhan, Cheng & Tang, Chenglong & Yang, Ke & Huang, Zuohua, 2016. "Experimental investigation on spray and atomization characteristics of diesel/gasoline/ethanol blends in high pressure common rail injection system," Energy, Elsevier, vol. 112(C), pages 549-561.
    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. Tian, Junjian & Liu, Yu & Bi, Haobo & Li, Fengyu & Bao, Lin & Han, Kai & Zhou, Wenliang & Ni, Zhanshi & Lin, Qizhao, 2022. "Experimental study on the spray characteristics of octanol diesel and prediction of spray tip penetration by ANN model," Energy, Elsevier, vol. 239(PA).
    2. Chang, Mengzhao & Park, Suhan, 2023. "Predictions and analysis of flash boiling spray characteristics of gasoline direct injection injectors based on optimized machine learning algorithm," Energy, Elsevier, vol. 262(PA).
    3. Zhou, Yifan & Qi, Wenyuan & Zhang, Yuyin, 2020. "Investigation on cyclic variation of diesel spray and a reconsideration of penetration model," Energy, Elsevier, vol. 211(C).
    4. Taghavifar, Hamid & Mardani, Aref & Hosseinloo, Ashkan Haji, 2015. "Appraisal of artificial neural network-genetic algorithm based model for prediction of the power provided by the agricultural tractors," Energy, Elsevier, vol. 93(P2), pages 1704-1710.
    5. Bahri, Bahram & Shahbakhti, Mahdi & Aziz, Azhar Abdul, 2017. "Real-time modeling of ringing in HCCI engines using artificial neural networks," Energy, Elsevier, vol. 125(C), pages 509-518.
    6. Pos, Radboud & Wardle, Robert & Cracknell, Roger & Ganippa, Lionel, 2017. "Spatio-temporal evolution of diesel sprays at the early start of injection," Applied Energy, Elsevier, vol. 205(C), pages 391-398.
    7. Oni, Babalola Aisosa & Sanni, Samuel Eshorame & Orodu, David Oyinkepreye & Ogunkunle, Temitope Fred, 2022. "Comparing the effects of Juliflora biodiesel doped with nano-additives on the performance of a compression ignition (CI) engine: Part A," Energy, Elsevier, vol. 244(PA).
    8. Hazar, Hanbey & Gul, Hakan, 2016. "Modeling analysis of chrome carbide (Cr3C2) coating on parts of combustion chamber of a SI engine," Energy, Elsevier, vol. 115(P1), pages 76-87.
    9. Ooi, Jong Boon & Ismail, Harun Mohamed & Tan, Boon Thong & Wang, Xin, 2018. "Effects of graphite oxide and single-walled carbon nanotubes as diesel additives on the performance, combustion, and emission characteristics of a light-duty diesel engine," Energy, Elsevier, vol. 161(C), pages 70-80.
    10. Elsayed Abdelhameed & Hiroshi Tashima, 2022. "EGR and Emulsified Fuel Combination Effects on the Combustion, Performance, and NOx Emissions in Marine Diesel Engines," Energies, MDPI, vol. 16(1), pages 1-22, December.
    11. Muteeb Ul Haq & Ali Turab Jafry & Saad Ahmad & Taqi Ahmad Cheema & Munib Qasim Ansari & Naseem Abbas, 2022. "Recent Advances in Fuel Additives and Their Spray Characteristics for Diesel-Based Blends," Energies, MDPI, vol. 15(19), pages 1-30, October.
    12. Leonid Plotnikov & Nikita Grigoriev, 2021. "Modernization of the Mechanical Fuel System of a Diesel Locomotive Engine through Physical and Numerical Modeling," Energies, MDPI, vol. 14(24), pages 1-15, December.
    13. Kim, Jun-Soo & Choi, Jae-Hyuk, 2023. "Feasibility study on bio-heavy fuel as an alternative for marine fuel," Renewable Energy, Elsevier, vol. 219(P2).
    14. Asgari, Behrad & Amani, Ehsan, 2017. "A multi-objective CFD optimization of liquid fuel spray injection in dry-low-emission gas-turbine combustors," Applied Energy, Elsevier, vol. 203(C), pages 696-710.
    15. Shameer, P. Mohamed & Ramesh, K., 2018. "Assessment on the consequences of injection timing and injection pressure on combustion characteristics of sustainable biodiesel fuelled engine," Renewable and Sustainable Energy Reviews, Elsevier, vol. 81(P1), pages 45-61.
    16. Fu, Wei & Li, Fengyu & Meng, Kesheng & Liu, Yanju & Shi, Weidong & Lin, Qizhao, 2019. "Experiment and analysis of spray characteristics of biodiesel blending with di-n-butyl ether in a direct injection combustion chamber," Energy, Elsevier, vol. 185(C), pages 77-89.
    17. Mohammad I. Jahirul & Farhad M. Hossain & Mohammad G. Rasul & Ashfaque Ahmed Chowdhury, 2021. "A Review on the Thermochemical Recycling of Waste Tyres to Oil for Automobile Engine Application," Energies, MDPI, vol. 14(13), pages 1-18, June.
    18. Chen, Hao & Wang, Yu & Zuo, Mingsheng & Zhang, Chao & Jia, Ninghong & Liu, Xiliang & Yang, Shenglai, 2022. "A new prediction model of CO2 diffusion coefficient in crude oil under reservoir conditions based on BP neural network," Energy, Elsevier, vol. 239(PC).
    19. Xu Zheng & Nan Zhou & Quan Zhou & Yi Qiu & Ruijun Liu & Zhiyong Hao, 2020. "Experimental Investigation on the High-frequency Pressure Oscillation Characteristics of a Combustion Process in a DI Diesel Engine," Energies, MDPI, vol. 13(4), pages 1-25, February.
    20. Venu, Harish & Raju, V. Dhana & Subramani, Lingesan, 2019. "Combined effect of influence of nano additives, combustion chamber geometry and injection timing in a DI diesel engine fuelled with ternary (diesel-biodiesel-ethanol) blends," Energy, Elsevier, vol. 174(C), pages 386-406.

    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:eee:energy:v:292:y:2024:i:c:s036054422400286x. 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: Catherine Liu (email available below). General contact details of provider: http://www.journals.elsevier.com/energy .

    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.