Optimizing Gaseous Fuel-Air Mixing in Direct Injection Engines Using an RNG Based k-ε Model 980135
Direct injection of natural gas under high pressure conditions has emerged as a promising option for improving engine fuel economy and emissions. However, since the gaseous injection technology is new, limited experience exists as to the optimum configuration of the injection system and associated combustion chamber design. The present study uses KIVA-3 based, multidimensional modeling to improve the understanding and assist the optimization of the gaseous injection process. Compared to standard k-ε models, a Renormalization Group Theory (RNG) based k-ε model  has been found to be in better agreement with experiments in predicting gaseous penetration histories for both free and confined jet configurations. Hence, this validated RNG model is adopted here to perform computations in realistic engine geometries. The parameters explored include the effects of piston crown shape, injector targeting, glow-plug presence, injection velocity, injection timing, number of injector holes, and initial swirl ratio on mixing. Insight generated from these studies provides guidelines on designing a combustion chamber and its associated fuel injection system for optimum gaseous fuel-air mixing.