IDEAS home Printed from https://ideas.repec.org/a/eee/energy/v258y2022ics0360544222017418.html
   My bibliography  Save this article

A molecular dynamics study of evaporation of multicomponent stationary and moving fuel droplets in multicomponent ambient gases under supercritical conditions

Author

Listed:
  • Gong, Yifei
  • Ma, Xiao
  • Luo, Kai Hong
  • Xu, Hongming
  • Shuai, Shijin

Abstract

The evaporation of a six-component fuel droplet under supercritical conditions is investigated using molecular dynamics (MD) simulations. The focus here is on effects of multicomponent ambient gases and the relative motion between the droplet and the ambient. The ambient pressure ranges from 8 MPa to 36 MPa and the ambient temperature ranges from 750 K to 3600 K. In the lower range of the temperature and pressure, the average displacement increment (ADI) per fuel atom gradually increases with time and the classic evaporation is observed. In the higher range of the temperature and pressure, the ADI profile has a unimodal distribution with time and the diffusive mixing between the droplet and the ambient gases dominates. Based on the ADI profile of fuel atoms, a criterion (τ0.9P) for mode transition from evaporation to diffusion is proposed. Among the ambient gases investigated, the mode transition is the most difficult in the nitrogen ambient but the easiest in combustion exhaust gases. For multicomponent fuel droplets close to or in diffusion mode, with higher relative velocities, the relative difference between evaporation rates for light/heavy fuel components is reduced. This study demonstrates that supercritical conditions alone are insufficient for mode transition of evaporation.

Suggested Citation

  • Gong, Yifei & Ma, Xiao & Luo, Kai Hong & Xu, Hongming & Shuai, Shijin, 2022. "A molecular dynamics study of evaporation of multicomponent stationary and moving fuel droplets in multicomponent ambient gases under supercritical conditions," Energy, Elsevier, vol. 258(C).
    • Handle: RePEc:eee:energy:v:258:y:2022:i:c:s0360544222017418
    DOI: 10.1016/j.energy.2022.124838
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0360544222017418
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.energy.2022.124838?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. Liu, Yuanbin & Hong, Weixiang & Cao, Bingyang, 2019. "Machine learning for predicting thermodynamic properties of pure fluids and their mixtures," Energy, Elsevier, vol. 188(C).
    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. Liu, Shanke & Yang, Yan & Yu, Lijun & Cao, Yu & Liu, Xinyi & Yao, Anqi & Cao, Yaping, 2023. "Self-heating optimization of integrated system of supercritical water gasification of biomass for power generation using artificial neural network combined with process simulation," Energy, Elsevier, vol. 272(C).
    2. Navarkar, Abhishek & Hasti, Veeraraghava Raju & Deneke, Elihu & Gore, Jay P., 2020. "A data-driven model for thermodynamic properties of a steam generator under cycling operation," Energy, Elsevier, vol. 211(C).
    3. Noushabadi, Abolfazl Sajadi & Lay, Ebrahim Nemati & Dashti, Amir & Mohammadi, Amir H. & Chofreh, Abdoulmohammad Gholamzadeh & Goni, Feybi Ariani & Klemeš, Jiří Jaromír, 2023. "Insights into modelling and evaluation of thermodynamic and transport properties of refrigerants using machine-learning methods," Energy, Elsevier, vol. 262(PA).

    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:258:y:2022:i:c:s0360544222017418. 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.