Fluid-dynamic and numerical aspects in the simulation of direct CNG injection in spark-ignition engines

This paper presents a detailed discussion on the numerical simulation of the underexpanded gas efflux from an outward-opening poppet-valve injector into an engine combustion chamber. The aim of the paper is to optimize the numerical simulation strategy for direct gas injection, in view of its application to internal combustion (IC) engines. In the first part of the paper, the widely studied case of a two-dimensional compressible flow is examined, and the main guidelines for the development of an effective numerical model for compressed natural gas (CNG) direct injection simulation are given, with specific reference to IC engines. The second part of the paper is devoted to the description of the numerical model developed and validated by the authors within the Star-CD environment, which is characterized by the presence of two distinct meshes. The first is built manually and covers the region surrounding the injector exit, whereas the second one covers most of the engine chamber and is built using the Es-ICE tool. A careful grid-independence study has been carried out in both the first and second part of the paper, and the influence of the spatial discretization of the convective fluxes has been discussed as well. The analyses have shown that a resolution of 40 cells in the nozzle height should be adopted to describe the typical phenomena that characterize an underexpanded free jet, unless a second order scheme can be implemented. However, as far as the simulation of the jet penetration time-history and its mixing with the surrounding air is concerned, sufficiently accurate results can also be obtained by using 20 cells per nozzle diameter and the first-order upwind scheme. As for the direct injection engine model, 16 cells across the nozzle lift represent a good compromise between accuracy and reliability of the results and the required computational time. The model has been validated with the support of experimental PLIF images in an optical-access engine, and has shown overall good accuracy and reliability, thus suggesting it is suitable for mixture formation analysis. (C) 2014 Elsevier Ltd. All rights reserved.