Development of a Computationally Efficient Progress Variable Approach for a Direct Injection Stochastic Reactor Model

Journal article, 2017

A novel 0-D Probability Density Function (PDF) based approach for the modelling of Diesel combustion using tabulated chemistry is presented. The Direct Injection Stochastic Reactor Model (DI-SRM) by Pasternak et al. has been extended with a progress variable based framework allowing the use of a pre-calculated auto-ignition table. Auto-ignition is tabulated through adiabatic constant pressure reactor calculations. The tabulated chemistry based implementation has been assessed against the previously presented DI-SRM version by Pasternak et al. where chemical reactions are solved online. The chemical mechanism used in this work for both, online chemistry run and table generation, is an extended version of the scheme presented by Nawdial et al. The main fuel species are n-decane, α-methylnaphthalene and methyl-decanoate giving a size of 463 species and 7600 reactions. A single-injection part-load heavy-duty Diesel engine case with 28 % EGR fueled with regular Diesel is investigated with both tabulated and online chemistry. Comparisons between the two approaches are presented by means of overall engine performance and engine-out emission predictions and in equivalence ratio-temperature space. The new implementation delivers reasonably good agreement with the online chemistry one. The methodology presented in this paper allows for the use of detailed chemistry in the DI-SRM with high computational efficiency and thus facilitates the use of the DI-SRM in the engine development process.