IDEAS home Printed from https://ideas.repec.org/a/eee/appene/v280y2020ics0306261920314057.html
   My bibliography  Save this article

Large-Eddy simulation of lean and ultra-lean combustion using advanced ignition modelling in a transparent combustion chamber engine

Author

Listed:
  • d'Adamo, A.
  • Iacovano, C.
  • Fontanesi, S.

Abstract

The need for Internal Combustion Engines (ICEs) to face near-future challenges of higher efficiency and reduced emissions is leading to a renewed interest towards lean-combustion. Several operational issues are associated to lean combustion, such as an abrupt increase of combustion cycle-by-cycle variability (CCV), leading to unbearable levels of Indicated Mean Effective Pressure (IMEP) variation and to misfiring cycles. Significant potential in the wide-scale establishment of lean combustion might come from Large Eddy Simulation (LES), which is able to elucidate the relationships between local physical processes (e.g. velocity magnitude, Air to Fuel Ratio (AFR), etc.) and early combustion progress (e.g. 1%) in unprecedented manners. To this aim, an improved ignition model for LES is proposed in the paper. Two premixed propane-air lean strategies are selected from the wide TCC-III database. A lean-stable (λ=1.10, also named lean) and lean-unstable (λ=1.43, also named ultra-lean) conditions are simulated, highlighting the model capability to well reproduce the sudden rise in CCV for increased mixture dilution. Explanations are given for the observed behaviour and a hierarchical quest for CCV dominant factors is presented. Finally, the different role of local flow field is highlighted for the two cases, and the comparison of optical acquisitions of OH* emission against simulated flame iso-surface up to 1% burn duration reinforce the simulation fidelity. The study shows the investigation possibilities of innovative combustion strategies given by advanced LES simulations. The understanding of turbulent combustion dynamics and the knowledge of the related lean-burn instabilities are key enabler for the exploration of new efficient lean-burn operations.

Suggested Citation

  • d'Adamo, A. & Iacovano, C. & Fontanesi, S., 2020. "Large-Eddy simulation of lean and ultra-lean combustion using advanced ignition modelling in a transparent combustion chamber engine," Applied Energy, Elsevier, vol. 280(C).
    • Handle: RePEc:eee:appene:v:280:y:2020:i:c:s0306261920314057
    DOI: 10.1016/j.apenergy.2020.115949
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0306261920314057
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.apenergy.2020.115949?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. Zhang, H.G. & Han, X.J. & Yao, B.F. & Li, G.X., 2013. "Study on the effect of engine operation parameters on cyclic combustion variations and correlation coefficient between the pressure-related parameters of a CNG engine," Applied Energy, Elsevier, vol. 104(C), pages 992-1002.
    2. 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.
    3. 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).
    4. Wadekar, S. & Janas, P. & Oevermann, M., 2019. "Large-eddy simulation study of combustion cyclic variation in a lean-burn spark ignition engine," Applied Energy, Elsevier, vol. 255(C).
    5. Yong, Jia Ying & Ramachandaramurthy, Vigna K. & Tan, Kang Miao & Mithulananthan, N., 2015. "A review on the state-of-the-art technologies of electric vehicle, its impacts and prospects," Renewable and Sustainable Energy Reviews, Elsevier, vol. 49(C), pages 365-385.
    6. Zhou, Feng & Fu, Jianqin & Ke, Wenhui & Liu, Jingping & Yuan, Zhipeng & Luo, Baojun, 2017. "Effects of lean combustion coupling with intake tumble on economy and emission performance of gasoline engine," Energy, Elsevier, vol. 133(C), pages 366-379.
    7. Xu, Zidan & Zhang, Yahui & Di, Huanyu & Shen, Tielong, 2019. "Combustion variation control strategy with thermal efficiency optimization for lean combustion in spark-ignition engines," Applied Energy, Elsevier, vol. 251(C), pages 1-1.
    8. Tsuboi, Seima & Miyokawa, Shinji & Matsuda, Masayoshi & Yokomori, Takeshi & Iida, Norimasa, 2019. "Influence of spark discharge characteristics on ignition and combustion process and the lean operation limit in a spark ignition engine," Applied Energy, Elsevier, vol. 250(C), pages 617-632.
    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. Wu, Jingtao & Zhang, Zhehao & Kang, Zhe & Deng, Jun & Li, Liguang & Wu, Zhijun, 2022. "An assessment methodology for fuel/water consumption co-optimization of a gasoline engine with port water injection," Applied Energy, Elsevier, vol. 310(C).
    2. Liu, Zuowen & Zhang, Zhongjie & Rao, Shunlu & Zheng, Zhaolei, 2024. "Study of water injection on suppressing knock in a high compression ratio and supercharged hybrid gasoline engine," Energy, Elsevier, vol. 287(C).
    3. Cheng, Jiaying & Liu, Bofan & Zhu, Tong, 2024. "Characterizing combustion instability in non-premixed methane combustion using internal flue gas recirculation," Applied Energy, Elsevier, vol. 370(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. Gong, Changming & Yi, Lin & Zhang, Zilei & Sun, Jingzhen & Liu, Fenghua, 2020. "Assessment of ultra-lean burn characteristics for a stratified-charge direct-injection spark-ignition methanol engine under different high compression ratios," Applied Energy, Elsevier, vol. 261(C).
    2. Yurii Gutarevych & Vasyl Mateichyk & Jonas Matijošius & Alfredas Rimkus & Igor Gritsuk & Oleksander Syrota & Yevheniy Shuba, 2020. "Improving Fuel Economy of Spark Ignition Engines Applying the Combined Method of Power Regulation," Energies, MDPI, vol. 13(5), pages 1-19, March.
    3. Thomas Lauer & Jens Frühhaber, 2020. "Towards a Predictive Simulation of Turbulent Combustion?—An Assessment for Large Internal Combustion Engines," Energies, MDPI, vol. 14(1), pages 1-26, December.
    4. Zhen, Xudong & Tian, Zhi & Wang, Yang & Xu, Meng & Liu, Daming & Li, Xiaoyan, 2022. "Knock analysis of bio-butanol in TISI engine based on chemical reaction kinetics," Energy, Elsevier, vol. 239(PC).
    5. Wei, Haiqiao & Zhang, Ren & Chen, Lin & Pan, Jiaying & Wang, Xuan, 2021. "Effects of high ignition energy on lean combustion characteristics of natural gas using an optical engine with a high compression ratio," Energy, Elsevier, vol. 223(C).
    6. Liu, Zuowen & Zheng, Zhaolei, 2024. "The effect of ignition energy on the lean combustion limitation in high compression ratio engines," Energy, Elsevier, vol. 301(C).
    7. Das, Himadry Shekhar & Tan, Chee Wei & Yatim, A.H.M., 2017. "Fuel cell hybrid electric vehicles: A review on power conditioning units and topologies," Renewable and Sustainable Energy Reviews, Elsevier, vol. 76(C), pages 268-291.
    8. Tan, Kang Miao & Padmanaban, Sanjeevikumar & Yong, Jia Ying & Ramachandaramurthy, Vigna K., 2019. "A multi-control vehicle-to-grid charger with bi-directional active and reactive power capabilities for power grid support," Energy, Elsevier, vol. 171(C), pages 1150-1163.
    9. Giovanni Cecere & Adrian Irimescu & Simona Silvia Merola & Luciano Rolando & Federico Millo, 2022. "Lean Burn Flame Kernel Characterization for Different Spark Plug Designs and Orientations in an Optical GDI Engine," Energies, MDPI, vol. 15(9), pages 1-17, May.
    10. Asaad Mohammad & Ramon Zamora & Tek Tjing Lie, 2020. "Integration of Electric Vehicles in the Distribution Network: A Review of PV Based Electric Vehicle Modelling," Energies, MDPI, vol. 13(17), pages 1-20, September.
    11. Wang, Ying & Xiao, Fan & Zhao, Yuwei & Li, Dongchang & Lei, Xiong, 2015. "Study on cycle-by-cycle variations in a diesel engine with dimethyl ether as port premixing fuel," Applied Energy, Elsevier, vol. 143(C), pages 58-70.
    12. Al-Zareer, Maan & Dincer, Ibrahim & Rosen, Marc A., 2019. "Comparative assessment of new liquid-to-vapor type battery cooling systems," Energy, Elsevier, vol. 188(C).
    13. Nunes, Pedro & Brito, M.C., 2017. "Displacing natural gas with electric vehicles for grid stabilization," Energy, Elsevier, vol. 141(C), pages 87-96.
    14. Liu, Hu-Chen & You, Xiao-Yue & Xue, Yi-Xi & Luan, Xue, 2017. "Exploring critical factors influencing the diffusion of electric vehicles in China: A multi-stakeholder perspective," Research in Transportation Economics, Elsevier, vol. 66(C), pages 46-58.
    15. Chen, Mingyi & Yu, Yue & Ouyang, Dongxu & Weng, Jingwen & Zhao, Luyao & Wang, Jian & Chen, Yin, 2024. "Research progress of enhancing battery safety with phase change materials," Renewable and Sustainable Energy Reviews, Elsevier, vol. 189(PA).
    16. Tarhan, Burak & Yetik, Ozge & Karakoc, Tahir Hikmet, 2021. "Hybrid battery management system design for electric aircraft," Energy, Elsevier, vol. 234(C).
    17. Wu, Yingwen & Ji, Yangjian, 2023. "Identifying firm-specific technology opportunities from the perspective of competitors by using association rule mining," Journal of Informetrics, Elsevier, vol. 17(2).
    18. García-Vázquez, Carlos A. & Llorens-Iborra, Francisco & Fernández-Ramírez, Luis M. & Sánchez-Sainz, Higinio & Jurado, Francisco, 2017. "Comparative study of dynamic wireless charging of electric vehicles in motorway, highway and urban stretches," Energy, Elsevier, vol. 137(C), pages 42-57.
    19. Das, H.S. & Rahman, M.M. & Li, S. & Tan, C.W., 2020. "Electric vehicles standards, charging infrastructure, and impact on grid integration: A technological review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 120(C).
    20. Cassetta, Ernesto & Marra, Alessandro & Pozzi, Cesare & Antonelli, Paola, 2017. "Emerging technological trajectories and new mobility solutions. A large-scale investigation on transport-related innovative start-ups and implications for policy," Transportation Research Part A: Policy and Practice, Elsevier, vol. 106(C), pages 1-11.

    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:280:y:2020:i:c:s0306261920314057. 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.