Flamelet

Direct-injection spark-ignition (DISI) engines are regarded as a promising technology for the reduction of fuel consumption and improvement of engine thermal efficiency. However, due to direct injection, the shortened fuel-air mixing duration leads to a spatial gradient of the equivalence ratio, and these locally rich regions cause the formation of particulate matter. In the current study, numerical investigations on pollutant formation in a DISI engine were performed using combined flamelet models for premixed and diffusion flames. The G-equation model for partially premixed combustion was improved by incorporating the laminar flamelet library. Gasoline fuel was represented as a ternary mixture of gasoline surrogate and its laminar flame speeds were obtained under a wide range of engine operating conditions. For the flame propagation in a partially premixed condition, the presumed shape of the probability density function approach was adopted, whereas the burned gas compositions were determined from the steady laminar flamelet library. Aided by the detailed composition of the burned gas, this model is able to predict the major pollutants, i.e., carbon-monoxide and nitric-oxide. The phenomenological model was implemented to simulate the particulate matter formation. The numerical analyses were conducted under variation in the engine operating conditions while varying the engine speed, intake pressure and ignition timing. The simulation results were well matched with the experimental results from the single-cylinder DISI engine. The oxidation of carbon monoxide was captured near the stoichiometric region as a development of the secondary diffusion flame, whereas the NO emission levels are in good agreement with the experimental data while the spatial distributions are in accordance with the mixture and temperature fields.

SAE 2015-01-0375.
Diesel combustion is a very complex process, involving a reacting, turbulent and multi-phase flow. Furthermore, heavy duty engines operate mainly at medium and high loads, where injection durations are very long and cylinder pressure is high. Within such context, proper CFD tools are necessary to predict mixing controlled combustion, heat transfer and, eventually, flame wall interaction which might result from long injection durations and high injection pressures. In particular, detailed chemistry seems to be necessary to estimate correctly ignition under a wide range of operating conditions and formation of rich combustion products which might lead to soot formation. This work is dedicated to the identification of suitable methodologies to predict combustion in heavy-duty diesel engines using detailed chemistry. To this end, two different approaches were implemented to model flame propagation process into the Lib-ICE code, which both employ detailed chemistry and turbulence chemistry interaction. In the first one, the Diesel spray flame is assumed to be an ensemble of different diffusion flames which are sequentially created during the injection process. Each of them is evolving in the mixture fraction space according to the conditionally averaged value of the scalar dissipation rate. The second one employs tabulated kinetics from perfectly stirred reactor calculations: transport equations are solved for mixture fraction, progress variable and their variances. Their source terms come from spray and the table. From these variables, it is possible to estimate the chemical composition in each computational cell, assuming suitable probability density functions both in mixture fraction and progress variable space for the chemical species. The above described approaches were then validated with two different sets of experimental data. First, constant-volume conditions allowed to make clear distinctions between the models in terms of auto-ignition, flame structure and flame stabilization process. Finally, simulations were carried out for a heavy-duty diesel engine with four different relevant operating loads.
Comparisons between computed and experimental data of in-cylinder pressure, heat release rate and wall heat flux allowed to identify the most suitable approaches among the tested ones and to even identify possible future improvements.