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

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  • 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
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    References listed on IDEAS

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    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).
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