IDEAS home Printed from https://ideas.repec.org/a/eee/appene/v281y2021ics0306261919319087.html
   My bibliography  Save this article

Fuel property effects on knock propensity and thermal efficiency in a direct-injection spark-ignition engine

Author

Listed:
  • Yue, Zongyu
  • Som, Sibendu

Abstract

Engine knock remains one of the major barriers to further improvement in thermal efficiency of Direct-Injection Spark-Ignition (DISI) engines. While Research Octane Number and Motor Octane Number are often used as standard rating methods for knock resistance of fuels, the impacts of other fuel properties on knock propensity in modern engines such as heat of vaporization (HoV) and laminar flame speed (LFS) require better understanding in order to co-optimize fuels and engine designs to achieve higher thermal efficiency and lower CO2 emission. In the present study, computational fluid dynamics (CFD) is used to model a boosted DISI engine with a focus on knock prediction and fuel property effects. A level-set G-equation model is employed to capture turbulent premixed combustion, and is coupled with a transported Livengood-Wu (L-W) integral approach to predict auto-ignition in the unburnt region. A criterion associated with the L-W integral is developed to accurately predict knock onset and knock-limited spark-advance. This model is then applied to a sensitivity analysis of HoV and LFS on knock tendency and thermal efficiency. The pressure-temperature trajectory framework is applied and extended to study the fuel effects on auto-ignition process in the engine. An existing efficiency-based merit function, which is derived from experiments for boosted SI engines, is evaluated and improved based on the current CFD results.

Suggested Citation

  • Yue, Zongyu & Som, Sibendu, 2021. "Fuel property effects on knock propensity and thermal efficiency in a direct-injection spark-ignition engine," Applied Energy, Elsevier, vol. 281(C).
  • Handle: RePEc:eee:appene:v:281:y:2021:i:c:s0306261919319087
    DOI: 10.1016/j.apenergy.2019.114221
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0306261919319087
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.apenergy.2019.114221?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. d'Adamo, Alessandro & Breda, Sebastiano & Fontanesi, Stefano & Irimescu, Adrian & Merola, Simona Silvia & Tornatore, Cinzia, 2017. "A RANS knock model to predict the statistical occurrence of engine knock," Applied Energy, Elsevier, vol. 191(C), pages 251-263.
    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. Eckert, Jony Javorski & Silva, Fabrício L. & da Silva, Samuel Filgueira & Bueno, André Valente & de Oliveira, Mona Lisa Moura & Silva, Ludmila C.A., 2022. "Optimal design and power management control of hybrid biofuel–electric powertrain," Applied Energy, Elsevier, vol. 325(C).
    2. Zhu, Zengqiang & Mu, Zhiqiang & Wei, Yanju & Du, Ruiheng & Guan, Wei & Liu, Shenghua, 2022. "Cylinder-to-cylinder variation of knock and effects of mixture formation on knock tendency for a heavy-duty spark ignition methanol engine," Energy, Elsevier, vol. 254(PA).
    3. Jian Liu & Dingrui Zhang & Lingyun Hou & Jinhu Yang & Gang Xu, 2022. "Laminar Burning Speed of Aviation Kerosene at Low Pressures," Energies, MDPI, vol. 15(6), pages 1-11, March.
    4. Zongyu Yue & Haifeng Liu, 2023. "Advanced Research on Internal Combustion Engines and Engine Fuels," Energies, MDPI, vol. 16(16), pages 1-8, August.
    5. Liu, Junheng & Ma, Haoran & Liang, Wenwen & Yang, Jun & Sun, Ping & Wang, Xidong & Wang, Yongxu & Wang, Pan, 2022. "Experimental investigation on combustion characteristics and influencing factors of PODE/methanol dual-fuel engine," Energy, Elsevier, vol. 260(C).

    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. Lijia Zhong & Changwen Liu, 2019. "Numerical Analysis of End-Gas Autoignition and Pressure Oscillation in a Downsized SI Engine Using Large Eddy Simulation," Energies, MDPI, vol. 12(20), pages 1-20, October.
    2. Guardiola, C. & Pla, B. & Bares, P. & Barbier, A., 2018. "An analysis of the in-cylinder pressure resonance excitation in internal combustion engines," Applied Energy, Elsevier, vol. 228(C), pages 1272-1279.
    3. Alessandro d’Adamo & Clara Iacovano & Stefano Fontanesi, 2021. "A Data-Driven Methodology for the Simulation of Turbulent Flame Speed across Engine-Relevant Combustion Regimes," Energies, MDPI, vol. 14(14), pages 1-17, July.
    4. Wang, Chenyao & Zhang, Fujun & Wang, Enhua & Yu, Chuncun & Gao, Hongli & Liu, Bolan & Zhao, Zhenfeng & Zhao, Changlu, 2019. "Experimental study on knock suppression of spark-ignition engine fuelled with kerosene via water injection," Applied Energy, Elsevier, vol. 242(C), pages 248-259.
    5. Simona Silvia Merola & Adrian Irimescu & Silvana Di Iorio & Bianca Maria Vaglieco, 2017. "Effect of Fuel Injection Strategy on the Carbonaceous Structure Formation and Nanoparticle Emission in a DISI Engine Fuelled with Butanol," Energies, MDPI, vol. 10(7), pages 1-19, June.
    6. d'Adamo, A. & Breda, S. & Berni, F. & Fontanesi, S., 2019. "The potential of statistical RANS to predict knock tendency: Comparison with LES and experiments on a spark-ignition engine," Applied Energy, Elsevier, vol. 249(C), pages 126-142.
    7. Teodosio, Luigi & Pirrello, Dino & Berni, Fabio & De Bellis, Vincenzo & Lanzafame, Rosario & D'Adamo, Alessandro, 2018. "Impact of intake valve strategies on fuel consumption and knock tendency of a spark ignition engine," Applied Energy, Elsevier, vol. 216(C), pages 91-104.
    8. Cao, Jiale & Li, Tie & Huang, Shuai & Chen, Run & Li, Shiyan & Kuang, Min & Yang, Rundai & Huang, Yating, 2023. "Co-optimization of miller degree and geometric compression ratio of a large-bore natural gas generator engine with novel Knock models and machine learning," Applied Energy, Elsevier, vol. 352(C).
    9. Chen, Ceyuan & Pal, Pinaki & Ameen, Muhsin & Feng, Dengquan & Wei, Haiqiao, 2020. "Large-eddy simulation study on cycle-to-cycle variation of knocking combustion in a spark-ignition engine," Applied Energy, Elsevier, vol. 261(C).
    10. Engelmann, L. & Welch, C. & Schmidt, M. & Meller, D. & Wollny, P. & Böhm, B. & Dreizler, A. & Kempf, A., 2023. "A temporal fluid-parcel backwards-tracing method for Direct-Numerical and Large-Eddy Simulation employing Lagrangian particles," Applied Energy, Elsevier, vol. 342(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:eee:appene:v:281:y:2021:i:c:s0306261919319087. 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.elsevier.com/wps/find/journaldescription.cws_home/405891/description#description .

    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.