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A transport equation residual model incorporating refined G-equation and detailed chemical kinetics combustion models has been developed and implemented in the ERC KIVA-3V release2 code for Gasoline Direct Injection (GDI) engine simulations for better predictions of flame propagation. In the transport equation residual model a fictitious species concept is introduced to account for the residual gases in the cylinder, which have a great effect on the laminar flame speed. The residual gases include CO2, H2O and N2 remaining from the previous engine cycle or introduced using EGR. This pseudo species is described by a transport equation. The transport equation residual model differentiates between CO2 and H2O from the previous engine cycle or EGR and that which is from the combustion products of the current engine cycle.

Diesel engine in-cylinder combustion processes have been studied using computational models with particular attention to spray development, vaporization, fuel/air mixture formation and combustion. A thermodynamic zero-dimensional cycle analysis program was used to determine initial conditions for the multidimensional calculations. A modified version of the time-dependent, three-dimensional computational fluid dynamics code KIVA-II was used for the computations, with a detailed treatment for the spray calculations and a simplified model for combustion. The calculations were used to obtain an understanding of the potential predictive capabilities of the models. It was found that there is a strong sensitivity of the results to numerical grid resolution. With proper grid resolution, the calculations were found to reproduce experimental data for non- vaporizing and vaporizing sprays. However, for vaporizing sprays with combustion, extremely fine grids are needed.

Modeling ignition kernel development in spark ignition engines is crucial to capturing the sources of cyclic variability, both with RANS and LES simulations. Appropriate kernel modeling must ensure that energy transfer from the electrodes to the gas phase has the correct timing, rate and locations, until the flame surface is large enough to be represented on the mesh by the G-Equation level-set method. However, in most kernel models, geometric details driving kernel growth are missing: either because it is described as Lagrangian particles, or because its development is simplified, i.e., down to multiple spherical flames. This paper covers the geometric aspects of kernel development, which makes up the core of a Triangulated Lagrangian Ignition Kernel model. One (or multiple, if it restrikes) spark channel is initialized as a one-dimensional Lagrangian particle thread.

Improved atomization is important in fuel injection applications since atomization influences fuel-air mixing and vaporization rates. The present paper explores the use of low pressure gas/air injection and methods to achieve a dispersed two-phase flow to enhance the atomization process. Gas-driven twin-fluid atomization has been achieved by combining X-ray lithographic/micro-machining technology to mechanically disperse a driving gas into a liquid to be sprayed. This technique forces the gas through a designed pattern of micron sized holes thereby yielding a field of micro-bubbles immediately upstream of the < I mm. diameter discharge orifice. Precise control of both uniformity of hole diameter and inter-hole spacing is critical to producing a well dispersed bubbly flow. The results show that the method of gas injection influences the liquid breakup process. Results are given for steady-flow atomization with low pressure injection into ambient air.

It is well known that the accuracy of simulations of combustion processes in diesel and spark ignited (SI) engines depends on the initial conditions within the cylinder at intake valve closure (IVC). Residual gas affects the engine combustion processes through its influence on charge mass, temperature and dilution. In SI engines, there is little oxygen in the residual gas, and thus the dilution effect on flame propagation is more significant than in compression ignited (CI) engines. However, in CI engines, the ignition delay depends strongly on the in-cylinder gas temperature, which is proportional to the gas temperature at IVC. Furthermore, ignition delay is significantly affected by how much oxygen is present, which is also partly determined by the residual gas fraction. Therefore, it is of extreme importance to determine residual gas concentrations accurately.

In the Atomization regime, liquid jets breakup either within the nozzle or immediately upon entering the chamber gas and drops much smaller than the jet diameter are formed. The mechanism of Atomization, which is presently unknown, was investigated by the simultaneous use of two photographic techniques. The initial transient was observed with a 106 frames/s camera and the steady state by a technique similar to spark photography. The experiment range was: liquid pressure 500 to 2500 psia; five mixtures of water and glycerol to vary the liquid viscosity; air, nitrogen, helium, and xenon at up to 600 psia as chamber gases to separate gas pressure from gas density effects; and 14 nozzle designs. Not changed were the temperature (room value), the nozzle diameter (340 μ), and the surface tension (70 dyne/cm).

Gas-phase in-cylinder mixing was examined by two different methods. The first method for observing mixing involved planar Mie scattering measurements of the instantaneous number density of silicon oil droplets which were introduced to the in-cylinder flow. The local value of the number density was assumed to be representative of the local gas concentration. Because the objective was to observe the rate in which gas concentration gradients change, to provide gradients in number density, droplets were admitted into the engine through only one of the two intake ports. Air only flowed through the other port. Three different techniques were used in analyzing the droplet images to determine the spatially dependent particle number density. Direct counting, a filtering technique, and autocorrelation were used and compared. Further, numerical experiments were performed with the autocorrelation method to check its effectiveness for determination of particle number density.

In Equation 10, there should be a plus sign between the first summation term and the quantity in brackets. The acceleration of the participant's vehicle was varied from −0.04 m/s2 to +0.06 m/s2 linearly as a function of the angle of the gas pedal with a 0.1-second delay.