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Strategies to Define Surrogate Fuels for the Description of the Multicomponent Evaporation Behavior of Hydrocarbon Fuels
Technical Paper
2018-01-1692
ISSN: 0148-7191, e-ISSN: 2688-3627
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English
Abstract
The scope of this work is to propose a methodology to define multicomponent surrogate mixtures which describe the main evaporation characteristics of real gasoline fuels. Since real fuels are commonly complex mixtures with hundreds or thousands of hydrocarbons, their exact composition is generally not known. Only global characteristics are standardized. An accurate modeling of such complex mixtures in 3D-CFD requires the definition of a suitable surrogate. So far, surrogate mixtures have mostly been defined based on their combustion properties, such as ignition delay or burning velocity, irrespective of their evaporation characteristics. For this reason, in this work, a systematic study is carried out to develop a methodology to define mixtures of representative components that mimic the evaporation behavior of real fuels. Specifically, the following aspects are analyzed: the necessary number and type of the surrogate components, the definition of optimization targets representing the real fuel properties of interest (i.e. the vapor pressure and the distillation curve), the formulation of an appropriate numerical model to evaluate these quantities and the choice of a suitable optimization algorithm to obtain the optimal surrogate composition. It is shown that these different aspects can influence the surrogate definition, potentially leading to a non-optimal representation of the real fuel target properties. This investigation is carried out for four representative real fuels, for which experimental data on their vapor pressure and distillation curve are available. Finally, suitable surrogates are proposed for all fuels.
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Pati, A., Gierth, S., Haspel, P., Hasse, C. et al., "Strategies to Define Surrogate Fuels for the Description of the Multicomponent Evaporation Behavior of Hydrocarbon Fuels," SAE Technical Paper 2018-01-1692, 2018, https://doi.org/10.4271/2018-01-1692.Data Sets - Support Documents
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