City Research Online

Abstract

Knowledge of fluid properties (e.g., density, viscosity, thermal conductivity) is critical for the design, testing, and development of fuel injection equipment (FIE) in academia and industry, especially at extreme temperature and pressure conditions where better spray performance leads to less soot emission. Currently, there is a lack of experimental data for fuels up to these conditions, and predictive approaches are required to model fuel properties. The objective of this research is to develop molecular-based techniques to predict transport and thermodynamic properties for well-characterized hydrocarbon mixtures and fuels up to high temperature and high pressure (HTHP) conditions. This PhD focuses on the development of pseudo-component techniques using the Perturbed-Chain Statistical Associating Fluid Theory (PC-SAFT) equation of state (EoS) for several well-characterized hydrocarbon mixtures and various fuels (e.g., rocket propellant, jet, kerosene, and diesel) with varying composition. The predictive techniques, which treat mixtures as a single pseudo-component, do not require binary interaction parameters. The methodology is first used to predict density, isothermal compressibility, and volumetric thermal expansion coefficient up to HTHP conditions for six hydrocarbon mixtures, two jet fuels, and four diesel fuels using two calculated or measured mixture properties as inputs (i.e., the number averaged molecular weight (MW) and the hydrogen to carbon (HN/CN) ratio). Density, isothermal compressibility, and volumetric thermal expansion coefficient are predicted up to 470 K and 3,500 bar for the hydrocarbon mixtures with 1, 4, and 7% mean absolute percent deviations (MAPDs), respectively. For fuels, density, isothermal compressibility, and volumetric thermal expansion coefficient are predicted with MAPDs of 1, 9, and 13 %, respectively. Entropy scaling based, pseudo-component techniques are also developed to predict viscosity and thermal conductivity up to HTHP conditions for mixtures and fuels using the number averaged MW and the HN/CN ratio. Viscosity and thermal conductivity are predicted less accurately when the mixture contains high concentrations of iso-alkanes. However, predictions for mixtures in this study are improved when a third input, a single data point at a chosen reference state, is used to fit one model parameter. For six hydrocarbon mixtures with varying concentrations, viscosity is predicted with MAPDs of 12 and 7% using the two-parameter and three-parameter models, respectively, from 293 to 353 K and up to 1,000 bar. For two different diesel fuels, viscosity is predicted with an MAPD of 21% using the two-parameter model and 9% using the three-parameter model from 323 to 423 K and up to 3,500 bar. For six different mixtures at conditions from 288 to 360 K and up to 4,500 bar, thermal conductivities are predicted with MAPDs of 16 and 3% using the two-parameter and three-parameter models, respectively. Thermal conductivities are predicted for three RP fuels and three jet fuels at conditions from 293 to 598 K and up to 700 bar with MAPDs of 14 and 2% using the two-parameter and three-parameter models, respectively. Finally, the viscosity pseudo-component technique is further analyzed, and a correlation is proposed to improve residual entropy predictions when using the two parameter model. This correlation, fit to ~700 hydrocarbon mixture data points, significantly improves viscosity predictions, reducing the two-parameter model MAPD from 12.0% to 9.2% for ~1,500 hydrocarbon mixture and fuel data points. The successful completion of this thesis expands the current field of hydrocarbon mixture and fuel property prediction up to the extreme operating conditions encountered by engineers in the petroleum industry. The developed techniques enable simple and accurate predictions without requiring expensive and time consuming experimental HTHP measurements for the design, testing, and development of fuel injection equipment (FIE).

Publication Type:	Thesis (Doctoral)
Subjects:	T Technology > TJ Mechanical engineering and machinery T Technology > TL Motor vehicles. Aeronautics. Astronautics
Departments:	Doctoral Theses School of Science & Technology > School of Science & Technology Doctoral Theses School of Science & Technology > Engineering

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