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Influence of Considering Non-Ideal Thermodynamics on Droplet Evaporation and Spray Formation (for Gasoline Direct Injection Engine Conditions) Using VSB2 Spray Model
Technical Paper
2018-01-0181
ISSN: 0148-7191, e-ISSN: 2688-3627
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Abstract
This work utilizes previously developed VSB2 (VSB2 Stochastic Blob and Bubble) multicomponent fuel spray model to study significance of using non-ideal thermodynamics for droplet evaporation under direct injection engine like operating conditions. Non-ideal thermodynamics is used to account for vapor-liquid equilibrium arising from evaporation of multicomponent fuel droplets. In specific, the evaporation of ethanol/iso-octane blend is studied in this work. Two compositions of the blend are tested, E-10 and E-85 respectively (the number denotes percentage of ethanol in blend). The VSB2 spray model is implemented into OpenFoam CFD code which is used to study evaporation of the blend in constant volume combustion vessel. Liquid and vapor penetration lengths for the E-10 case are calculated and compared with the experiment. The simulation results show reasonable agreement with the experiment. Simulation is performed with two methods- ideal and non-ideal thermodynamics respectively. For liquid penetration, the two methods show a small but evident difference. For vapor penetration, there is no significant difference. Radial fuel vapor mass fraction distribution (for both components) is obtained from simulation and compared for E-10 and E-85 cases. It is seen that for E-85 case, the difference in predictions between ideal and non-ideal thermodynamics case is significantly higher than that of E10 case. It is therefore inferred that ideal thermodynamics is not sufficient to predict vapor liquid equilibrium, especially for higher ethanol content in the blend.
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Pandian Muthuramalingam, V. and Karlsson, A., "Influence of Considering Non-Ideal Thermodynamics on Droplet Evaporation and Spray Formation (for Gasoline Direct Injection Engine Conditions) Using VSB2 Spray Model," SAE Technical Paper 2018-01-0181, 2018, https://doi.org/10.4271/2018-01-0181.Data Sets - Support Documents
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