The Dual Flame Model (DFM) : A Phenomenological 0D Diesel Combustion Model to Predict Pollutant Emissions

Paper #:
  • 2015-24-2388

  • 2015-09-06
  • 10.4271/2015-24-2388
Rudloff, J., Dulbecco, A., and Font, G., "The Dual Flame Model (DFM) : A Phenomenological 0D Diesel Combustion Model to Predict Pollutant Emissions," SAE Technical Paper 2015-24-2388, 2015,
IFP Energies nouvelles (IFPEN) has a large experience in the development of engine simulation platforms. During the last decade, the Dual Flame Model (DFM), a physical 0-dimensional (0D) combustion model designed for Diesel applications, was developed and continuously improved. The DFM formalism allows to represent quite precisely the in-cylinder combustion process scenario, by accounting for the first order relevant physics impacting fuel oxidation. First of all, this allows to account for the impact of engine actuators on combustion (e.g. injection systems performing complex injection strategies, Low Pressure and High Pressure EGR loops,…) and then to describe the pollutant emissions formation processes, being chemical kinetics strongly dependent on the in-cylinder thermochemical conditions.The aim of this communication is to present the potential of using the DFM model in the different stages of a Diesel engine development process for pollutant emissions optimization. For this, a new automatic multi-step calibration approach developed at IFPEN will be detailed and its potential will be illustrated thanks to several applications. In a second time, the representativeness of the DFM combustion process description and its potential to predict pollutant emissions to single-parameter variations of engine control actuators will be investigated. To do that, an original model evaluation methodology based on DOE (Design Of Experiments) is presented. Accordingly, the physical response of the DFM, obtained by post-processing the results of the virtual DOE database is compared to the one given by an experimental mirror-DOE database. This permits to validate from one side the physical bases of the DFM and to put in evidence the axes of improvement of the model.
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