scholarly journals Supercritical and transcritical real-fluid mixing using the PC-SAFT EOS

2017 ◽  
Author(s):  
Carlos Rodriguez ◽  
Alvaro Vidal ◽  
Phoevos Koukouvinis ◽  
Manolis Gavaises
Keyword(s):  
Cfd Simulation ◽  
Liquid Density ◽  
Vapor Pressures ◽  
Statistical Associating Fluid Theory ◽  
Jet Mixing ◽  
Real Fluid ◽  
Wide Range ◽  
Fluid Theory ◽  
Theory Equation ◽  
Dimensional Simulation

A numerical framework has been developed to simulate the mixing of supercritical and transcritical fluids using anequation of state based on statistical associating fluid theory. In a Diesel engine the liquid fuel is injected into supercritical air. After the injection, the Diesel is heated over its critical temperature reaching a supercritical state. Modelling real-fluid effects is critical in order to properly characterize the air/fuel mixing in the combustion chamber. By using the PC-SAFT EoS (Perturbed Chain Statistical Association Fluid Theory Equation of State) real fluids effects can be taken into account in a CFD simulation. The PC-SAFT EoS shows best results than cubic EoS computing liquid density, compressibility, speed of sound, vapor pressures and density derivatives. Unlike cubic EoS, this model accounts for the shape and size of the molecules. Gas, liquid, supercritical and vapor-liquid equilibrium states can be simulated. PT FLASH (Isothermal Multiphase Flash Calculation) is applied to compute the phase diagram used by the code. Shock tube problems were conducted in a wide range of pressures and densities using n-dodecane to show the capability of the developed algorithm. The results were compared with the solution of an exact Riemann solver which has the PC-SAFT EoS implemented showing a high degree of agreement. In addition, a two-dimensional simulation of supercritical nitrogen jet mixing was carried out to checkthe multidimensional capability of the code.DOI: http://dx.doi.org/10.4995/ILASS2017.2017.5000

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

2018 ◽  
Vol 21 (7) ◽  
pp. 1118-1133 ◽  
Author(s):  
Alvaro Vidal ◽  
Carlos Rodriguez ◽  
Phoevos Koukouvinis ◽  
Manolis Gavaises ◽  
Mark A McHugh
Keyword(s):  
Equation Of State ◽  
Diesel Fuel ◽  
Group Contribution ◽  
Operating Conditions ◽  
Fuel Properties ◽  
Liquid Density ◽  
Theory Approach ◽  
Statistical Associating Fluid Theory ◽  
Diesel Fuels ◽  
Fluid Theory

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.

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