An improved TRF mechanism for a new turbulent premixed combustion model with application to engine combustion CFD

Author:

Yang Shiyou1ORCID

Affiliation:

1. Research and Advanced Engineering, Ford Motor Company, Dearborn, MI, USA

Abstract

In the present work, an existing TRF (toluene reference fuel) chemical kinetic mechanism has been improved. In the improvement, the existing TRF mechanism was found to have unphysically higher laminar flame speeds at temperatures greater than 750 K which are typical in engine compression-combustion operating conditions. Eight dominant reactions which mainly control the laminar flame speeds under higher temperatures ( T > 750 K) have been identified. Based on a recently published power-law laminar flame speed correlation which is able to provide good predictions under a wide range of engine conditions, correct reaction rate constants for the eight high-temperature dominant reactions have been determined for physical laminar flame speeds. The identification and improvement of the eight high-temperature dominant reactions are the first novelty of this work. In order to simulate ethanol and gasoline blends, a latest ethanol sub-mechanism has been implemented into the TRF mechanism. The improved TRF mechanism was validated using available experimental data in the literature. Based on the improved TRF mechanism, a new mechanism library has been generated, which can be used for an improved mechanism-dynamic-selection turbulent premixed combustion model in which turbulent diffusivity is constructed as a function of local turbulence/thermodynamics conditions. The dynamic turbulent diffusivity sub-model is the second novelty of this work. The improved TRF mechanism and its combination with the improved mechanism-dynamic-selection turbulent premixed combustion model were successfully applied to the prediction of combustion and emissions of GTDI (gasoline turbocharged direct injection) engines under both warm-up and transient cold start operating conditions, indicating that the improved models in this work are beneficial for predictive engine combustion CFD (computational fluid dynamics).

Publisher

SAGE Publications

Subject

Mechanical Engineering,Ocean Engineering,Aerospace Engineering,Automotive Engineering

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