Composition Analysis and Viscosity Prediction of Complex Fuel Mixtures Using a Molecular-Based Approach

Abstract

The automobile industry currently faces the challenge of developing a new generation of diesel motor engines that satisfy both increasingly stringent emission regulations and reduces specific fuel consumption. The performance of diesel engines, seen in terms of emissions and specific fuel consumption, generally improves with increasing fuel-injection pressure. The design of the next generation of diesel fuel injection systems requires the knowledge of the thermophysical properties, in particular viscosity, of a wide-type of diesel fuels at pressures up to 300 MPa or more. The objective of the present work is to demonstrate that it is possible to predict the viscosity of any petroleum-based diesel fuel, using, exclusively, its molar fraction distribution as provided by multidimensional gas chromatography techniques. The precise knowledge of the fuel chemical constituents allows the understanding of the influence of the different hydrocarbon families on the fluid viscosity by means of molecular dynamics simulations. The accuracy of the Anisotropic United Atom force-field was tested and was found to be in agreement with experimental viscosities obtained with a new vibrating wire device at different temperatures and pressures up to 300 MPa. Finally, the experimental and simulated viscosities have been compared with improved group contribution method that has been coupled with gas chromatography experimental measurements for a viscosity prediction that was estimated to be of less than 18% of mean absolute deviation.

Viscosity of heavy n -alkanes and diffusion of gases therein based on molecular dynamics simulations and empirical correlations

“…Some limitations were identified when the model was applied to asymmetric mixtures of methane with toluene. Aquing et al [26] have performed composition analysis and viscosity predictions of complex fuel mixtures using an anisotropic united atom force-field approach. Recently, coarse-grained molecular models have been used successfully to predict the viscosity of medium size n-alkanes [27,28].…”

Chemoinformatics at IFP Energies Nouvelles: Applications in the Fields of Energy, Transport, and Environment

“…Insofar as quantitative predictions can be obtained, such techniques represent an interesting manner to predict property values for complex fluids, all the more when pressure and temperature conditions make experiments hazardous. For instance, we proposed works dealing with simulations of gasoline and diesel fuels for pressure and/or temperature conditions encountered within injectors, and showed that molecular simulation is appropriate to overcome limits of experimental devices . Similarly, numerical simulations are interesting tools when the considered molecules are toxic for human, which is typically the case of systems containing H 2 S .…”

Entropy scaling based viscosity predictions for hydrocarbon mixtures and diesel fuels up to extreme conditions

“…At high pressures, compositional variance between fuels can lead to significant differences in viscosity. For example, the viscosity of two different diesel fuels reported by Aquing et al [13] and Schaschke et al [12] at 323 K differ by more than 120% at 1,800 bar and 200% at 2,400 bar. Models fit to viscosity data for one diesel sample cannot be expected to accurately represent the viscosity of another diesel of different composition.…”

“…Recently, several researchers demonstrated advantages when incorporating advanced equations of state (EoS), such as the Perturbed-Chain Statistical Associating Fluid Theory (PC-SAFT) within CFD [22][23][24]. Given that complex mixtures (e.g., diesel and biodiesel fuels, crude oils, bitumens, heavy oils) are often composed of hundreds of different compounds [13,[25][26][27][28][29], CFD simulation of every fuel compound would be computationally intensive. Often, a smaller number of components are chosen as a surrogate to closely match the thermophysical properties of the mixture [13,[30][31][32][33][34][35][36].…”