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Articles

Development of a mechanism-dynamic-selection turbulent premixed combustion model with application to gasoline engine combustion and emissions simulation

Pages 315-350 | Received 15 Jun 2020, Accepted 14 Dec 2020, Published online: 07 Jan 2021
 

Abstract

In order to overcome the drawback (tuning reaction rate coefficients) of using a single mechanism in the chemical-kinetics-based turbulent premixed combustion modelling approach, a mechanism-dynamic-selection turbulent premixed combustion model has been developed, validated, and successfully applied to the combustion and emissions simulation of spark ignition gasoline engines. In this new model, based on the local thermal and turbulent conditions of any preheat cell, a certain chemical kinetic mechanism which can achieve the target ratio of turbulent flame speed to laminar flame speed is selected. To realise this goal, based on the laminar flame speed sensitivity analysis of an improved eTRF (ethanol toluene reference fuel) base chemical kinetic mechanism, a derived mechanism library has been generated. Based on enough experimental and DNS (direct numerical simulation) data of ST/SL, a mechanism selection database (mechanism as a function of Borghi-Peters coordinates) has been constructed and implemented into a CFD (computational fluid dynamics) code, also a classification scheme for identifying unburned, preheat, flame front, and burned zones of a combustion domain has been developed and implemented into the CFD code. The developed mechanism-dynamic-selection turbulent premixed combustion model has been validated using the combustion in a spherical constant volume and applied to the prediction of combustion and emissions of a spark ignition gasoline engine under transient cold start process. Validation results show that, without tuning any model parameters, the base mechanism, the derived mechanism library, the mechanism selection function, the classification scheme, as well as a simulation setup strategy for modelling engine transient cold start process, have good prediction performances and are practical for engine combustion and emissions simulation.

Acknowledgements

The author thanks the following people: Yifeng Tang during his stay in Ford for helping develop the classification scheme of flame front; Yang Gao of Convergent Sciences Inc. for helping implement the new model into a CFD code; Foo-Chern Ting, Cindy Zhou, Claudia Iyer of Ford Motor Company for helping the engine simulation setups, submitting simulations, and post-processing engine simulation results; Peter Moilanen of Ford for providing the engine experimental data; Brad VanDerWege, Steven Wooldridge, and Jianwen Yi of Ford for helpful discussions; Prof. Ming Jia of Dalian University of Technology of China for providing a TRF mechanism based on the decoupling methodology.

Disclosure statement

I declare that I have no potential competing financial, professional, or personal interests that might have influenced the research, authorship, performance, or presentation of the work described in this manuscript.

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