Modelling of Diesel fuel properties through its surrogates using Perturbed-Chain, Statistical Associating Fluid Theory

Abstract

The Perturbed-Chain, Statistical Associating Fluid Theory equation of state is utilised to model the effect of pressure and temperature on the density, volatility and viscosity of four Diesel surrogates; these calculated properties are then compared to the properties of several Diesel fuels. Perturbed-Chain, Statistical Associating Fluid Theory calculations are performed using different sources for the pure component parameters. One source utilises literature values obtained from fitting vapour pressure and saturated liquid density data or from correlations based on these parameters. The second source utilises a group contribution method based on the chemical structure of each compound. Both modelling methods deliver similar estimations for surrogate density and volatility that are in close agreement with experimental results obtained at ambient pressure. Surrogate viscosity is calculated using the entropy scaling model with a new mixing rule for calculating mixture model parameters. The closest match of the surrogates to Diesel fuel properties provides mean deviations of 1.7% in density, 2.9% in volatility and 8.3% in viscosity. The Perturbed-Chain, Statistical Associating Fluid Theory results are compared to calculations using the Peng–Robinson equation of state; the greater performance of the Perturbed-Chain, Statistical Associating Fluid Theory approach for calculating fluid properties is demonstrated. Finally, an eight-component surrogate, with properties at high pressure and temperature predicted with the group contribution Perturbed-Chain, Statistical Associating Fluid Theory method, yields the best match for Diesel properties with a combined mean absolute deviation of 7.1% from experimental data found in the literature for conditions up to 373°K and 500 MPa. These results demonstrate the predictive capability of a state-of-the-art equation of state for Diesel fuels at extreme engine operating conditions.

Numerical investigation of the aerodynamic breakup of a parallel moving droplet cluster

“…To give an idea about the grid size, the 3D-4 computational domain utilizes initially approximately 2.5 million cells, which increase up to 8.7 million at the end of the simulation, due to the use of the adaptive local grid refinement. The simulations have been performed with Diesel fuel as liquid with properties taken from [37], using a four component surrogate and the PC-SAFT equation of state; a pressure of 40bar and a temperature of 900K were considered for the estimation of gas properties, while a temperature of 335K was assumed for the liquid properties. These conditions correspond to those encountered in Diesel enginesas presented in Table 1, along with the corresponding references used for their estimation.…”

“…For a typical duration of t=1.5tsh, during which the droplet undergoes breakup as shown in Figure 5, less than 1% of the droplet mass has been evaporated, while the mean temperature of the droplet increases by about 8K. This change of liquid temperature results in a decrease to its surface tension and viscosity equal to approximately 3% and 11%, respectively (properties based on [39] and [37], respectively). The resulting non-dimensional numbers from the properties of Table 1 are: Oh=0.05, ε=51 and N=37.…”

Effect of Composition, Temperature, and Pressure on the Viscosities and Densities of Three Diesel Fuels

“…For example, Lin and Tavlarides 25 use the Benedict-Webb-Rubin 25,26 EoS with multiple pure-component parameters to predict HPHT densities for twenty diesel fuel surrogate mixtures containing one to fourteen compounds. In a different study Vidal et al 27 uses the PC-SAFT EoS to predict the HPHT densities of four surrogate mixtures containing four to nine compounds where pure component PC-SAFT parameters are calculated using a group contribution (GC) method 28 or a correlation based on component molecular weight 29,30 . Rokni et al 20 further reduce the number of compounds in the surrogate mixture by predicting HPHT diesel density using a single, pseudo-component technique based on the PC-SAFT EoS.…”

Multiphase Phenomena in Diesel Fuel Injection Systems

“…Also, fuel composition varies depending on the crude oil source and refinery processing. Hence, there has been increasing effort in measuring properties of fuel components and defining surrogates and models that can approximate the behaviour of actual fuels 60 . For defining adequate models for fuels and fuel components, it is necessary to describe phase Figure 24, for dodecane), beyond which there is no distinction between liquid and vapour phases and the latent heat of vaporization is zero.…”