Modelling a Dual-fuelled Multi-Cylinder HCCI Engine Using a PDF-based Engine Cycle Simulator

Authors: Amit Bhave, Markus Kraft*, L. Montorsi, and Fabian Mauss


Operating the HCCI engine with dual fuels with a large difference in auto-ignition characteristics (octane number) is one way to control the HCCI operation. The effect of octane number on combustion, emissions and engine performance in a 6 cylinder SCANIA truck engine, fuelled with n-heptane and isooctane, and running in HCCI mode, are investigated numerically and compared with measurements taken from Olsson et al. [SAE 2000-01-2867]. To correctly simulate the HCCI engine operation, we implement a probability density function (PDF) based stochastic reactor model (including detailed chemical kinetics and accounting for inhomogeneities in composition and temperature) coupled with GT-POWER, a 1-D fluid dynamics based engine cycle simulator. Such a coupling proves to be ideal for the understanding of the combustion phenomenon as well as the gas dynamics processes intrinsic to the engine cycle. The convective heat transfer in the engine cylinder is modeled as a stochastic jump process and accounts for the fluctuations and fluid-wall interaction effects. Curl's coalescence-dispersion model is used to describe turbulent mixing. A good agreement is observed between the predicted values and measurements for in-cylinder pressure, auto-ignition timing and CO, HC as well as NOx emissions for the base case. The advanced PDF-based engine cycle simulator clearly outperforms the widely used homogeneous model based full cycle engine simulator. The trends in combustion characteristics such as ignition crank angle degree and combustion duration with respect to varying octane numbers are predicted well as compared to measurements. The integrated model provides reliable predictions for in-cylinder temperature, CO, HC as well as NOx emissions over a wide range of octane numbers studied.

Keywords: homogeneous charge compression ignition (HCCI), probability density function (PDF), stochastic modelling, stochastic reactor model (SRM),

Associated Projects: Numerics and Engines

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