In spark-ignition engines, knock control represents one of the most critical issue to reach optimal thermal efficiency. The paper deals with the development of a methodology aimed at evaluating the most suited spark advance to achieve the right compromise between performance optimization and knocking occurrence (KLSA). The methodology is based on combustion simulation via a two-zone 0D model in which the cycle-by-cycle variation (CCV) is described by a model parameter that impacts on the turbulence level at inlet valve closing. The auto-ignition of the unburnt mixture is described by a thermodynamic equation derived from literature while knock intensity is evaluated by a stochastic approach. The method allows evaluating the percentage of knocking cycles, depending on the spark advance actuated. The validation has been carried out vs. experimental data collected at the engine test rig on a 4 cylinders turbocharged GDI engine, by comparing predicted and experimental MAPO.

A model based methodology for knock prediction in SI engines

Arsie I.;
2019-01-01

Abstract

In spark-ignition engines, knock control represents one of the most critical issue to reach optimal thermal efficiency. The paper deals with the development of a methodology aimed at evaluating the most suited spark advance to achieve the right compromise between performance optimization and knocking occurrence (KLSA). The methodology is based on combustion simulation via a two-zone 0D model in which the cycle-by-cycle variation (CCV) is described by a model parameter that impacts on the turbulence level at inlet valve closing. The auto-ignition of the unburnt mixture is described by a thermodynamic equation derived from literature while knock intensity is evaluated by a stochastic approach. The method allows evaluating the percentage of knocking cycles, depending on the spark advance actuated. The validation has been carried out vs. experimental data collected at the engine test rig on a 4 cylinders turbocharged GDI engine, by comparing predicted and experimental MAPO.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11367/90057
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