IDEAS home Printed from https://ideas.repec.org/a/eee/energy/v306y2024ics0360544224023259.html
   My bibliography  Save this article

Investigating the effect of fuel properties and environmental parameters on low-octane gasoline-like fuel spray combustion and emissions using machine learning-global sensitivity analysis method

Author

Listed:
  • Ma, Yinjie
  • Yang, Dong
  • Xie, Deyi
  • E, Jiaqiang

Abstract

The gasoline compression ignition mode is an advanced combustion mode that achieves high efficiency with low emissions. This study constructs a global sensitivity analysis (GSA) by coupling machine learning (ML) algorithms and conducts a comprehensive investigation into the statistical correlations between research octane number (RON), fuel sensitivity (S), three initial ambient parameters, and the spray combustion characteristics of gasoline-like fuels. Eighty sets of CFD numerical simulation data were used for training the machine learning model. Results show liquid length (LL) is most sensitive to changes in ambient pressure while ambient temperature significantly influences vapor penetration length (VPL). Regarding combustion behaviors, ignition delay (ID) and flame lift-off length (LOL) show the highest sensitivity to ambient temperature. The occurrence of distinct ignition points in the domain also advances from 1.75 ms to 0.42 ms as the ambient temperature rises from 1000K to 1200K. Additionally, the ambient temperature has higher-order interaction effects with ambient pressure on LOL. The production of NOx in the field is also more closely correlated with ambient temperature, with higher temperatures promoting its generation. Furthermore, ambient temperature exhibits a positive interaction effect with both ambient pressure and oxygen concentration on NOx emissions.

Suggested Citation

  • Ma, Yinjie & Yang, Dong & Xie, Deyi & E, Jiaqiang, 2024. "Investigating the effect of fuel properties and environmental parameters on low-octane gasoline-like fuel spray combustion and emissions using machine learning-global sensitivity analysis method," Energy, Elsevier, vol. 306(C).
    • Handle: RePEc:eee:energy:v:306:y:2024:i:c:s0360544224023259
    DOI: 10.1016/j.energy.2024.132551
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0360544224023259
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.energy.2024.132551?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. Xu, Leilei & Bai, Xue-Song & Li, Changle & Tunestål, Per & Tunér, Martin & Lu, Xingcai, 2019. "Combustion characteristics of gasoline DICI engine in the transition from HCCI to PPC: Experiment and numerical analysis," Energy, Elsevier, vol. 185(C), pages 922-937.
    2. Wang, Libing & Wu, Zengyang & Ahmed, Ahfaz & Badra, Jihad A. & Sarathy, S. Mani & Roberts, William L. & Fang, Tiegang, 2019. "Auto-ignition of direct injection spray of light naphtha, primary reference fuels, gasoline and gasoline surrogate," Energy, Elsevier, vol. 170(C), pages 375-390.
    3. Yang, Jian & Ma, Yinjie & Fu, Jianqin & Shu, Jun & Liu, Jingping, 2019. "Parametric study of gasoline properties on combustion characteristics of gasoline compression engines using reaction kinetics simulation and density-based global sensitivity analysis," Applied Energy, Elsevier, vol. 255(C).
    4. Wang, Yong & Ma, Yinjie & Xie, Deyi & Yu, Zhenhuan & E, Jiaqiang, 2021. "Numerical study on the influence of gasoline properties and thermodynamic conditions on premixed laminar flame velocity at multiple conditions," Energy, Elsevier, vol. 233(C).
    5. Liu, Jinlong & Huang, Qiao & Ulishney, Christopher & Dumitrescu, Cosmin E., 2021. "Machine learning assisted prediction of exhaust gas temperature of a heavy-duty natural gas spark ignition engine," Applied Energy, Elsevier, vol. 300(C).
    6. Mavromatidis, Georgios & Orehounig, Kristina & Carmeliet, Jan, 2018. "Uncertainty and global sensitivity analysis for the optimal design of distributed energy systems," Applied Energy, Elsevier, vol. 214(C), pages 219-238.
    7. Noh, Hyun Kwon & No, Soo-Young, 2017. "Effect of bioethanol on combustion and emissions in advanced CI engines: HCCI, PPC and GCI mode – A review," Applied Energy, Elsevier, vol. 208(C), pages 782-802.
    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. Jordan, Matthias & Millinger, Markus & Thrän, Daniela, 2020. "Robust bioenergy technologies for the German heat transition: A novel approach combining optimization modeling with Sobol’ sensitivity analysis," Applied Energy, Elsevier, vol. 262(C).
    2. Kale, Aneesh Vijay & Krishnasamy, Anand, 2023. "Experimental study of homogeneous charge compression ignition combustion in a light-duty diesel engine fueled with isopropanol–gasoline blends," Energy, Elsevier, vol. 264(C).
    3. Jonathan Dumas & Antoine Dubois & Paolo Thiran & Pierre Jacques & Francesco Contino & Bertrand Cornélusse & Gauthier Limpens, 2022. "The Energy Return on Investment of Whole-Energy Systems: Application to Belgium," Biophysical Economics and Resource Quality, Springer, vol. 7(4), pages 1-34, December.
    4. Zhang, Jiyuan & Tang, Hailong & Chen, Min, 2019. "Linear substitute model-based uncertainty analysis of complicated non-linear energy system performance (case study of an adaptive cycle engine)," Applied Energy, Elsevier, vol. 249(C), pages 87-108.
    5. Waibel, Christoph & Evins, Ralph & Carmeliet, Jan, 2019. "Co-simulation and optimization of building geometry and multi-energy systems: Interdependencies in energy supply, energy demand and solar potentials," Applied Energy, Elsevier, vol. 242(C), pages 1661-1682.
    6. Àlex Alonso & Jordi de la Hoz & Helena Martín & Sergio Coronas & Pep Salas & José Matas, 2020. "A Comprehensive Model for the Design of a Microgrid under Regulatory Constraints Using Synthetical Data Generation and Stochastic Optimization," Energies, MDPI, vol. 13(21), pages 1-26, October.
    7. Ren, Fukang & Wei, Ziqing & Zhai, Xiaoqiang, 2021. "Multi-objective optimization and evaluation of hybrid CCHP systems for different building types," Energy, Elsevier, vol. 215(PA).
    8. Qyyum, Muhammad Abdul & Duong, Pham Luu Trung & Minh, Le Quang & Lee, Sanggyu & Lee, Moonyong, 2019. "Dual mixed refrigerant LNG process: Uncertainty quantification and dimensional reduction sensitivity analysis," Applied Energy, Elsevier, vol. 250(C), pages 1446-1456.
    9. Zhong, Shengyuan & Zhao, Jun & Li, Wenjia & Li, Hao & Deng, Shuai & Li, Yang & Hussain, Sajjad & Wang, Xiaoyuan & Zhu, Jiebei, 2021. "Quantitative analysis of information interaction in building energy systems based on mutual information," Energy, Elsevier, vol. 214(C).
    10. Xu, Leilei & Bai, Xue-Song & Li, Yaopeng & Treacy, Mark & Li, Changle & Tunestål, Per & Tunér, Martin & Lu, Xingcai, 2020. "Effect of piston bowl geometry and compression ratio on in-cylinder combustion and engine performance in a gasoline direct-injection compression ignition engine under different injection conditions," Applied Energy, Elsevier, vol. 280(C).
    11. Luerssen, Christoph & Verbois, Hadrien & Gandhi, Oktoviano & Reindl, Thomas & Sekhar, Chandra & Cheong, David, 2021. "Global sensitivity and uncertainty analysis of the levelised cost of storage (LCOS) for solar-PV-powered cooling," Applied Energy, Elsevier, vol. 286(C).
    12. Abokersh, Mohamed Hany & Vallès, Manel & Cabeza, Luisa F. & Boer, Dieter, 2020. "A framework for the optimal integration of solar assisted district heating in different urban sized communities: A robust machine learning approach incorporating global sensitivity analysis," Applied Energy, Elsevier, vol. 267(C).
    13. Urbano, Eva M. & Martinez-Viol, Victor & Kampouropoulos, Konstantinos & Romeral, Luis, 2021. "Energy equipment sizing and operation optimisation for prosumer industrial SMEs – A lifetime approach," Applied Energy, Elsevier, vol. 299(C).
    14. Wu, Raphael & Sansavini, Giovanni, 2020. "Integrating reliability and resilience to support the transition from passive distribution grids to islanding microgrids," Applied Energy, Elsevier, vol. 272(C).
    15. 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).
    16. Liu, Liuchen & Cui, Guomin & Chen, Jiaxing & Huang, Xiaohuang & Li, Di, 2022. "Two-stage superstructure model for optimization of distributed energy systems (DES) part I: Model development and verification," Energy, Elsevier, vol. 245(C).
    17. Ashour, Mahmoud K. & Eldrainy, Yehia A. & Elwardany, Ahmed E., 2020. "Effect of cracked naphtha/biodiesel/diesel blends on performance, combustion and emissions characteristics of compression ignition engine," Energy, Elsevier, vol. 192(C).
    18. Su, Ziyi & Li, Xiaofeng, 2022. "Extraction of key parameters and simplification of sub-system energy models using sensitivity analysis in subway stations," Energy, Elsevier, vol. 261(PA).
    19. Urbano, Eva M. & Martinez-Viol, Victor & Kampouropoulos, Konstantinos & Romeral, Luis, 2022. "Risk assessment of energy investment in the industrial framework – Uncertainty and Sensitivity Analysis for energy design and operation optimisation," Energy, Elsevier, vol. 239(PA).
    20. Zhou, Mengmeng & Wang, Shuai & Luo, Kun & Fan, Jianren, 2022. "Three-dimensional modeling study of the oxy-fuel co-firing of coal and biomass in a bubbling fluidized bed," Energy, Elsevier, vol. 247(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:energy:v:306:y:2024:i:c:s0360544224023259. 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.