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Analysis of Knock Tendency in a Small VVA Turbocharged Engine Based on Integrated 1D-3D Simulations and Auto-Regressive Technique
ISSN: 1946-3936, e-ISSN: 1946-3944
Published April 01, 2014 by SAE International in United States
Citation: Fontanesi, S., Severi, E., Siano, D., Bozza, F. et al., "Analysis of Knock Tendency in a Small VVA Turbocharged Engine Based on Integrated 1D-3D Simulations and Auto-Regressive Technique," SAE Int. J. Engines 7(1):72-86, 2014, https://doi.org/10.4271/2014-01-1065.
In the present paper, two different methodologies are adopted and critically integrated to analyze the knock behavior of a last generation small size spark ignition (SI) turbocharged VVA engine. Particularly, two full load operating points are selected, exhibiting relevant differences in terms of knock proximity. On one side, a knock investigation is carried out by means of an Auto-Regressive technique (AR model) to process experimental in-cylinder pressure signals. This mathematical procedure is used to estimate the statistical distribution of knocking cycles and provide a validation of the following 1D-3D knock investigations.
On the other side, an integrated numerical approach is set up, based on the synergic use of 1D and 3D simulation tools. The 1D engine model is developed within the commercial software GT-Power™. It is used to provide time-varying boundary conditions (BCs) for the 3D code, Star-CD™. Particularly, information between the two simulation tools are at first exchanged under motored conditions to tune an “in-house developed” turbulence sub-model included in the 1D software. 1D results are then validated against the experimental data under fired full load operations, by employing a further “in-house developed” combustion sub-model. BCs are finally passed back to the 3D code to carry out a detailed knock analysis for two full load points, namely 2100 and 4000 rpm. In particular, the knock intensity is predicted, for experimentally actuated and earlier spark advances, and the results are qualitatively compared to the AR model outcomes.