A Tabulated-Chemistry Approach applied to a Quasi-Dimensional Combustion Model for a Fast and Accurate Knock Prediction in Spark-Ignition Engines 2019-01-0471
The description of knock phenomenon is a critical issue in a combustion model for Spark-Ignition (SI) engines. The accurate handling of various aspects, such as the impact of the fuel composition, the presence of residual gas or water in the burning mixture, the influence of cool flame heat release, etc., requires the solution of detailed or semi-detailed chemistry schemes for gasoline blends. Whichever is the modeling environment, either 3D or 0D, the on-line solution of a chemical kinetic scheme drastically affects the computational time.
In this paper, a procedure for an accurate and fast prediction of the hydrocarbons auto-ignition, applied to phenomenological SI engine combustion models, is proposed. It is based on a tabulated approach, operated on both ignition delay times and reaction rates. This technique, widely used in 3D calculations, is here extended to 0D models to overcome the inaccuracies typical of the most common ignition delay approaches, based on the Livengood-Wu integral solution. The aim is to combine the predictability of a detailed chemistry with an acceptable computational effort.
First, the tabulated technique is verified through comparisons with chemical solvers for various schemes in constant-pressure and constant-volume configurations. Then, it is coupled to a fractal combustion model, developed by the authors, to predict the knock occurrence in different SI engines, including both naturally-aspirated and turbocharged architectures. The model also takes into account the effects of cyclic dispersion and a not-uniform temperature distribution in the unburned zone.
Assessments with the experimentally identified Knock-Limited Spark Advance show that the knock model based on tabulated chemistry is able to well reproduce the essential features of the auto-ignition process in the analyzed engines, with a limited impact on the computational time.
Fabio Bozza, Vincenzo De Bellis, Luigi Teodosio