An Innovative Approach Combining Adaptive Mesh Refinement, the ECFM3Z Turbulent Combustion Model, and the TKI Tabulated Auto-Ignition Model for Diesel Engine CFD Simulations 2016-01-0604
The 3-Zones Extended Coherent Flame Model (ECFM3Z) and the Tabulated Kinetics for Ignition (TKI) auto-ignition model are widely used for RANS simulations of reactive flows in Diesel engines. ECFM3Z accounts for the turbulent mixing between one zone that contains compressed air and EGR and another zone that contains evaporated fuel. These zones mix to form a reactive zone where combustion occurs. In this mixing zone TKI is applied to predict the auto-ignition event, including the ignition delay time and the heat release rate. Because it is tabulated, TKI can model complex fuels over a wide range of engine thermodynamic conditions. However, the ECFM3Z/TKI combustion modeling approach requires an efficient predictive spray injection calculation. In a Diesel direct injection engine, the turbulent mixing and spray atomization are mainly driven by the liquid/gas coupling phenomenon that occurs at moving liquid/gas interfaces. For this time-dependent problem, Adaptive Mesh Refinement (AMR) becomes particularly important due to the dynamic nature of the spray and the migration of the liquid regions in the engine cylinder. A validation of combining these approaches is performed over a wide range of experimental Diesel engine operating conditions including varying loads and engine speeds, EGR rates, and injection timing. The results show clearly the ability of this combined approach to reproduce auto-ignition delays and heat release rates over the full set of experimental conditions.
Citation: Bohbot, J., Colin, O., Velghe, A., Michel, J. et al., "An Innovative Approach Combining Adaptive Mesh Refinement, the ECFM3Z Turbulent Combustion Model, and the TKI Tabulated Auto-Ignition Model for Diesel Engine CFD Simulations," SAE Technical Paper 2016-01-0604, 2016, https://doi.org/10.4271/2016-01-0604. Download Citation
Julien Bohbot, Olivier Colin, Anthony Velghe, Jean-Baptiste Michel, Mingjie Wang, P. K. Senecal, Eric Pomraning
IFP Energies Nouvelles, Convergent Science Inc.