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In recent years, there has been rapid progress in characterizing the detailed chemical kinetics associated with the oxidation of liquid hydrocarbons and their blends. However adding these fuel models to the industrial engineer's toolkit has proven a major challenge due to issues associated with high CPU cost and the poor suitability of many of the most promising and well known fuel models to IC engine applications. This paper demonstrates the state-of-the-art in the analysis and modelling of current and future transportation fuels or fuel blends for internal combustion engine applications. First-of-all, a benchmarking of eleven representative fuel models (39 to 1034 species in size) is carried out at engine/engine-like operating conditions by adopting the standard Research Octane and Cetane Number test data for comparison. Next, methods to construct a fuel model for a commercial fuel are outlined using a simple, yet robust surrogate mapping technique.

Ethanol fuel blends with gasoline for spark ignition (SI) internal combustion engines are widely used on account of their advantages in terms of fuel economy and emissions reduction potential. The focus of this paper is to study the effects of these blends on combustion characteristics such as in-cylinder pressure profiles, gas-phase emissions (e.g., unburned hydrocarbons, NOx) and particulates (e.g., particulate matter and particle number) using both measurement campaigns and digital engineering workflows. Nineteen load-speed operating points in a 1L 3-cylinder GDI SI engine were measured and modelled. The measurements for in-cylinder pressure and emissions were repeated at each operating point for three types of fuel: gasoline (E0, 0% by volume of ethanol blend), E10 (10 % by volume of ethanol blend) and E20 (20% by volume of ethanol blend).

In recent decades, “physics-based” gas-dynamics simulation tools have been employed to reduce development timescales of IC engines by enabling engineers to carry out parametric examinations and optimisation of alternative engine geometry and operating strategy configurations using desktop PCs. However to date, these models have proved inadequate for optimisation of in-cylinder combustion and emissions characteristics thus extending development timescales through additional experimental development efforts. This research paper describes how a Stochastic Reactor Model (SRM) with reduced chemistry can be employed to successfully determine in-cylinder pressure, heat release and emissions trends from a diesel fuelled engine operated in compression ignition direct injection mode using computations which are completed in 147 seconds per cycle.

Employing detailed chemistry into modern engine simulation technologies has potential to enhance the robustness and predictive power of such tools. Specifically this means significant advancements in the ability to compute the onset of ignition, low and high temperature heat release, local extinction, knocking, exhaust gas emissions formation etc. resulting in a set of tools which can be employed to carry out virtual engineering studies and add additional insight into common IC engine development activities such as computing IMEP, identifying safe/feasible operating ranges, minimizing exhaust gas emissions and optimizing operating strategy. However the adoption of detailed chemistry comes at a greater computational cost, this paper investigates the means to retain computational robustness and ease of use whist reducing computational timescales.

This paper builds upon recent publication (SAE Technical Paper 2011-01-1388, 2011, doi:10.4271/2011-01-1388) and outlines the on-going development of an advanced simulator for virtual engine mapping and optimization of engine performance, combustion and emissions characteristics. The model is further advanced through development of new sub-models for turbulent mixing, multiple injection events, variable injection pressures, engine breathing and gas exchange, as well as particulates formation and oxidation. The result is a simulator which offers engine design and performance data typically associated with 1D thermodynamic engine cycle simulations but with the "physics-based" model robustness usually associated with 3D CFD methods. This combination then enables efficient optimization of engine design with respect to engine performance, combustion characteristics and exhaust gas emissions.