A multi-dimensional Computational Fluid Dynamics (CFD) code with detailed chemistry, the KIVA-CHEMKIN-GA code, was employed in this study, where Genetic Algorithms (GA) were used to optimize heavy-duty diesel engine operating parameters. A two-stage combustion (TSC) concept was explored to optimize the combustion process at high speed (1737 rev/min) and medium load (57% load). Two combustion modes were combined in this concept. The first stage is ideally Homogeneous Charge Compression Ignition (HCCI) combustion and the second stage is diffusion combustion under high temperature and low oxygen concentration conditions. This can be achieved for example by optimization of two-stage combustion using multiple injection or sprays from two different injectors.
The present optimization study was split into two parts: early injection event optimization, the purpose of which was to prepare a homogeneous mixture for the HCCI combustion, and late injection event optimization, the purpose of which was to find optimum engine operating parameters and to optimize engine performance. An inhomogeneity assessment concept is proposed to evaluate the mixture quality for HCCI combustion. This concept can be used in early injection event optimization prior to ignition.
As a limiting benchmark case, the late injection event optimization was conducted assuming a homogeneous mixture has already been formed in the cylinder before ignition occurs. Four engine operating parameters were optimized: Intake Valve Closure (IVC) timing, Exhaust Gas Recirculation (EGR) ratio, Start of Late Injection (SOLI) timing and the fraction of fuel in HCCI combustion. Parametric studies were also conducted to investigate the effects of these parameters on engine performance. The results showed that combining late IVC timing, late SOLI and a medium EGR level, two-stage combustion was able to achieve low engine-out emissions. The TSC concept shows great potential to meet future ultra-low emission standards.