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Gasoline-direct injection using multi-hole nozzles is prone to flash-boiling due to the transfer of thermal energy to the fuel combined with the sub-atmospheric pressures present in the cylinder during injection. Flash boiling is governed by a finite rate interphase heat-transfer mechanism and hence a thermal non-equilibrium model was used for simulations. Additionally, the fuel composition plays an important role in flash boiling and hence, any modeling of this phenomena must account for the type of fuel being used. In the current work, in addition to single component fuels, a non-ideal mixing model is used to calculate the properties of gasoline-ethanol blends. The flash boiling of the different single and multi-component fuels is compared and a parametric study is conducted to observe the importance of flash boiling. The purpose of this study is to use CFD calculations to propose dimensionless parameters that can help to understand how multiple time scales interact.

The internal flow in an injector is greatly affected by cavitation formation, and this in turn impacts the spray characteristics of diesel injectors. In the current work, the performance of the Homogeneous Relaxation Model (HRM) in simulating cavitation inside a diesel injector is evaluated. This model is based on the assumption of homogeneous flow, and was originally developed for flash boiling simulations. However, the model can potentially simulate the spectrum of vaporization mechanisms ranging from cavitation to flash boiling through the use of an empirical time scale which depends on the thermodynamic conditions of the injector fuel. A lower value of this time scale represents a lower deviation from thermal equilibrium conditions, which is an acceptable assumption for small-scale cavitating flows. Another important advantage is the ability of this model to be easily coupled with real fuel models.

Given the complexities of simulating fuel injection, most models contain empirical parameters. This work presents a method of automatically adjusting empirical model parameters in a computational fluid dynamics (CFD) sub-model in order to best agree with an ensemble of experimental measurements. The method is demonstrated by a multiphase flow simulation of flash-boiling fuel injector nozzles. This paper describes a framework to automatically set inputs, launch individual runs, read the output of these runs, and intelligently choose new input values based on the difference between calculated mass flow rates and experimental values, in order to minimize error. The Hooke-Jeeves search algorithm was chosen for the optimization process, since it is reasonably efficient, does not require calculating derivatives, and is robust. The scheme scales well when employed on computer clusters, where numerous calculations can be run simultaneously using a batch queuing system.