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An Effective Method to Model the Combustion Process in Spark Ignition Engines

Journal Article
ISSN: 1946-3936, e-ISSN: 1946-3944
Published April 25, 2022 by SAE International in United States
An Effective Method to Model the Combustion Process in Spark Ignition
Citation: Beccari, S. and Pipitone, E., "An Effective Method to Model the Combustion Process in Spark Ignition Engines," SAE Int. J. Engines 16(2):131-145, 2023,
Language: English


A numerical simulation is a fundamental tool in the design and optimization procedure of an Internal Combustion (IC) engine; since combustion is the process that mostly influences the engine performance, efficiency and emissions, an effective combustion submodel is fundamental. A simple, nonpredictive way to simulate the combustion evolution is to implement a mathematical function that reproduces the mass fraction burned (MFB) profile that is characterized by a sigmoidal trend; the most used for this purpose is the Wiebe function. In this article the authors propose a different mathematical model, a Dose-Response (DR) type function that shows some benefits when compared to the Wiebe function, in particular, a better interpolation of experimental MFB profiles in which the combustion extinction phase represents a large fraction of the whole combustion duration; this happens, for example, in Spark Ignition (SI) engines with a noncentral location of the spark plug, which produces an asymmetric combustion propagation and, in turn, an asymmetric derivative of the experimental MFB profile. In this article both the traditional Wiebe and the proposed DR function have been calibrated by means of experimental MFB profiles obtained from a supercharged SI engine fueled with natural gas; the two calibrated functions have been implemented in a zero-dimensional (0-D) SI engine model and compared in terms of Indicated Mean Effective Pressure (IMEP) prediction reliability. The proposed DR function allowed both a better MFB profile interpolation and a better IMEP prediction for all the operating conditions tested (different engine speed and supercharging pressure), with a maximum prediction error of 2.1% compared with 2.9% of the Wiebe function.