IDEAS home Printed from https://ideas.repec.org/a/eee/energy/v210y2020ics0360544220315504.html
   My bibliography  Save this article

Novel regression models for wiebe parameters aimed at 0D combustion simulation in spark ignition engines

Author

Listed:
  • Giglio, Veniero
  • di Gaeta, Alessandro

Abstract

In the present work a novel predictive Wiebe-Based combustion Model (WBM) is proposed for simulation of the combustion process in a normally aspirated 1.6 L spark ignition (SI) engine. Unlike other approaches presented in literature, the novelty consists of: the considered set of Wiebe parameters, that is the angle at 50% of burned fuel, the combustion duration between 10% and 90% of burned fuel, and the form factor m; the nonlinear feature of the used correlations; the set of the involved engine variables, including particularly the laminar burning speed of the air/fuel mixture at combustion start. Based on a wide experimental database a Turbulent entrainment Combustion Model (TCM) is also set up, validated and embedded in a 1D simulation model of the engine. The parameters of the Wiebe function fitting the Mass Burned Fraction (MBF) development are estimated for each engine operating condition and then correlated to main engine variables. To assess to what extent the simpler WBM can be used in place of the TCM, simulations of the validated 1D engine model were carried out with both WBM and TCM and their performances compared in a wide range of engine operating conditions in terms of Brake Mean Effective Pressure (BMEP), Brake Specific Fuel Consumption (BSFC) and Carbon monoxide concentration (CO).

Suggested Citation

  • Giglio, Veniero & di Gaeta, Alessandro, 2020. "Novel regression models for wiebe parameters aimed at 0D combustion simulation in spark ignition engines," Energy, Elsevier, vol. 210(C).
    • Handle: RePEc:eee:energy:v:210:y:2020:i:c:s0360544220315504
    DOI: 10.1016/j.energy.2020.118442
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0360544220315504
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.energy.2020.118442?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. Maroteaux, Fadila & Saad, Charbel, 2013. "Diesel engine combustion modeling for hardware in the loop applications: Effects of ignition delay time model," Energy, Elsevier, vol. 57(C), pages 641-652.
    2. Liu, Jinlong & Dumitrescu, Cosmin E., 2019. "Single and double Wiebe function combustion model for a heavy-duty diesel engine retrofitted to natural-gas spark-ignition," Applied Energy, Elsevier, vol. 248(C), pages 95-103.
    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. Santiago Molina & Ricardo Novella & Josep Gomez-Soriano & Miguel Olcina-Girona, 2021. "New Combustion Modelling Approach for Methane-Hydrogen Fueled Engines Using Machine Learning and Engine Virtualization," Energies, MDPI, vol. 14(20), pages 1-21, October.
    2. Chen, Leiming & Xu, Zhaoping & Liu, Shuangshuang & Liu, Liang, 2022. "Dynamic modeling of a free-piston engine based on combustion parameters prediction," Energy, Elsevier, vol. 249(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. Hu, Deng & Wang, Hechun & Wang, Binbin & Shi, Mingwei & Duan, Baoyin & Wang, Yinyan & Yang, Chuanlei, 2022. "Calibration of 0-D combustion model applied to dual-fuel engine," Energy, Elsevier, vol. 261(PB).
    2. Vélez Godiño, José Antonio & Torres García, Miguel & Jiménez-Espadafor Aguilar, Francisco José, 2022. "Experimental analysis of late direct injection combustion mode in a compression-ignition engine fuelled with biodiesel/diesel blends," Energy, Elsevier, vol. 239(PA).
    3. Chen, Leiming & Xu, Zhaoping & Liu, Shuangshuang & Liu, Liang, 2022. "Dynamic modeling of a free-piston engine based on combustion parameters prediction," Energy, Elsevier, vol. 249(C).
    4. Kim, Seongsu & Kim, Junghwan, 2023. "Assessing fuel economy and NOx emissions of a hydrogen engine bus using neural network algorithms for urban mass transit systems," Energy, Elsevier, vol. 275(C).
    5. Santiago Molina & Ricardo Novella & Josep Gomez-Soriano & Miguel Olcina-Girona, 2021. "New Combustion Modelling Approach for Methane-Hydrogen Fueled Engines Using Machine Learning and Engine Virtualization," Energies, MDPI, vol. 14(20), pages 1-21, October.
    6. Diego Perrone & Angelo Algieri & Pietropaolo Morrone & Teresa Castiglione, 2021. "Energy and Economic Investigation of a Biodiesel-Fired Engine for Micro-Scale Cogeneration," Energies, MDPI, vol. 14(2), pages 1-28, January.
    7. Loganathan, S. & Leenus Jesu Martin, M. & Nagalingam, B. & Prabhu, L., 2018. "Heat release rate and performance simulation of DME fuelled diesel engine using oxygenate correction factor and load correction factor in double Wiebe function," Energy, Elsevier, vol. 150(C), pages 77-91.
    8. Faisal Lodi & Ali Zare & Priyanka Arora & Svetlana Stevanovic & Mohammad Jafari & Zoran Ristovski & Richard J. Brown & Timothy Bodisco, 2020. "Combustion Analysis of a Diesel Engine during Warm up at Different Coolant and Lubricating Oil Temperatures," Energies, MDPI, vol. 13(15), pages 1-21, August.
    9. Huang, Shuai & Li, Tie & Zhang, Zhifei & Wang, Linyan & Yu, Xiao & Zheng, Ming & Yang, Rundai & Zhao, Xinwu, 2021. "Influencing factors on the vibrational and rotational temperatures in the spark discharge channel," Energy, Elsevier, vol. 222(C).
    10. Thakkar, Kartikkumar & Kachhwaha, Surendra Singh & Kodgire, Pravin & Srinivasan, Seshasai, 2021. "Combustion investigation of ternary blend mixture of biodiesel/n-butanol/diesel: CI engine performance and emission control," Renewable and Sustainable Energy Reviews, Elsevier, vol. 137(C).
    11. Ruomiao Yang & Tianfang Xie & Zhentao Liu, 2022. "The Application of Machine Learning Methods to Predict the Power Output of Internal Combustion Engines," Energies, MDPI, vol. 15(9), pages 1-16, April.
    12. Doppalapudi, A.T. & Azad, A.K. & Khan, M.M.K., 2023. "Advanced strategies to reduce harmful nitrogen-oxide emissions from biodiesel fueled engine," Renewable and Sustainable Energy Reviews, Elsevier, vol. 174(C).
    13. Maroteaux, Fadila & Saad, Charbel, 2015. "Combined mean value engine model and crank angle resolved in-cylinder modeling with NOx emissions model for real-time Diesel engine simulations at high engine speed," Energy, Elsevier, vol. 88(C), pages 515-527.
    14. Duan, Xiongbo & Zhang, Shiheng & Liu, Yiqun & Li, Yangtang & Liu, Jingping & Lai, Ming-Chia & Deng, Banglin, 2020. "Numerical investigation the effects of the twin-spark plugs coupled with EGR on the combustion process and emissions characteristics in a lean burn natural gas SI engine," Energy, Elsevier, vol. 206(C).
    15. Vivek Pandey & Kiran Hanmanthrao Shahapurkar & Suresh Guluwadi & Getinet Asrat Mengesha & Bekele Gadissa & Nagaraj Ramalingayya Banapurmath & Chandramouli Vadlamudi & Sanjay Krishnappa & T. M. Yunus K, 2023. "Studies on the Performance of Engines Powered with Hydrogen-Enriched Biogas," Energies, MDPI, vol. 16(11), pages 1-13, May.
    16. Deng, Jiaolong & Guan, Chaoran & Wang, Tianshi & Liu, Xiaojing & Chai, Xiang, 2024. "Evaluation of start-up characteristics for heat pipe-cooled nuclear reactor coupled with recuperated open-air brayton cycle using hardware-in-the-loop," Energy, Elsevier, vol. 301(C).
    17. Andrea Massimo Marinoni & Angelo Onorati & Giacomo Manca Di Villahermosa & Simon Langridge, 2023. "Real Driving Cycle Simulation of a Hybrid Bus by Means of a Co-Simulation Tool for the Prediction of Performance and Emissions," Energies, MDPI, vol. 16(12), pages 1-29, June.
    18. Yanyan Zhang & Ziyuan Ma & Yan Feng & Ziyu Diao & Zhentao Liu, 2021. "The Effects of Ultra-Low Viscosity Engine Oil on Mechanical Efficiency and Fuel Economy," Energies, MDPI, vol. 14(8), pages 1-20, April.
    19. Xu, Zheng & Ji, Fenzhu & Ding, Shuiting & Zhao, Yunhai & Zhang, Xiangbo & Zhou, Yu & Zhang, Qi & Du, Farong, 2020. "High-altitude performance and improvement methods of poppet valves 2-stroke aircraft diesel engine," Applied Energy, Elsevier, vol. 276(C).
    20. Yongming Feng & Haiyan Wang & Ruifeng Gao & Yuanqing Zhu, 2019. "A Zero-Dimensional Mixing Controlled Combustion Model for Real Time Performance Simulation of Marine Two-Stroke Diesel Engines," Energies, MDPI, vol. 12(10), pages 1-19, May.

    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:210:y:2020:i:c:s0360544220315504. 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.