In-Cylinder Mixing Rate Measurements and CFD Analyses

<div class="htmlview paragraph">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.</div> <div class="htmlview paragraph">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.</div> <div class="htmlview paragraph">Results from the three techniques for determining particle number density showed similar trends. The filtering and autocorrelation methods produced higher resolution than was obtainable with the direct counting methods.</div> <div class="htmlview paragraph">Engine speed, and throttle position influenced the particle number density observed in the cylinder. For example, at the motored speed of 500 rpm, the particle number density exhibited a maximum in the center of the cylinder. At a motored engine speed of 1500 rpm, there is evidence for a strong wall jet with a high number density existing well into the compression stroke.</div> <div class="htmlview paragraph">The second step in observing mixing in-cylinder involved the use of KIVA to predict changes in gas concentrations in-cylinder while mimicking the experiment. KIVA was also used to identify the source of changes in concentration. In other words, using KIVA, it is possible to identify whether a change in concentration in a given cell or cells was a result of large scale motion, or smaller-scale diffusional processes.</div> <div class="htmlview paragraph">There were significant differences in the concentrations predicted by KIVA and those obtained from the experiment. KIVA predicted highly symmetrical flow structures, with the flows from each of the ports remaining largely unmixed.</div>