In this work, we apply a sequence of concepts for mechanism reduction on one reaction mechanism including novel quality control. We introduce a moment-based accuracy rating method for species profiles. The concept is used for a necessity-based mechanism reduction utilizing 0D reactors. Thereafter a stochastic reactor model for internal combustion engines is applied to control the quality of the reduced reaction mechanism during the expansion phase of the engine. This phase is sensitive on engine out emissions, and is often not considered in mechanism reduction work. The proposed process allows to compile highly reduced reaction schemes for computational fluid dynamics application for internal combustion engine simulations. It is demonstrated that the resulting reduced mechanisms predict combustion and emission formation in engines with accuracies comparable to the original detailed scheme.

The use of chemical kinetic mechanisms in computer aided engineering tools for internal combustion engine simulations is of high importance for studying and predicting pollutant formation of conventional and alternative fuels. However, usage of complex reaction schemes is accompanied by high computational cost in 0-D, 1-D and 3-D computational fluid dynamics frameworks. The present work aims to address this challenge and allow broader deployment of detailed chemistry-based simulations, such as in multi-objective engine optimization campaigns. A fast-running tabulated chemistry solver coupled to a 0-D probability density function-based approach for the modelling of compression and spark ignition engine combustion is proposed. A stochastic reactor engine model has been extended with a progress variable-based framework, allowing the use of pre-calculated auto-ignition tables instead of solving the chemical reactions on-the-fly. As a first validation step, the tabulated chemistry-based solver is assessed against the online chemistry solver under constant pressure reactor conditions. Secondly, performance and accuracy targets of the progress variable-based solver are verified using stochastic reactor models under compression and spark ignition engine conditions. Detailed multicomponent mechanisms comprising up to 475 species are employed in both the tabulated and online chemistry simulation campaigns. The proposed progress variable-based solver proved to be in good agreement with the detailed online chemistry one in terms of combustion performance as well as engine-out emission predictions (CO, CO₂, NO and unburned hydrocarbons). Concerning computational performances, the newly proposed solver delivers remarkable speed-ups (up to four orders of magnitude) when compared to the online chemistry simulations. In turn, the new solver allows the stochastic reactor model to be computationally competitive with much lower order modeling approaches (i.e., Vibe-based models). It also makes the stochastic reactor model a feasible computer aided engineering framework of choice for multi-objective engine optimization campaigns.

A chemical species lumping approach for reduction of large hydrocarbons and oxygenated fuels is presented. The methodology is based on an a priori analysis of the Gibbs free energy of the isomer species which is then used as main criteria for the evaluation of lumped group. Isomers with similar Gibbs free energy are lumped assuming they present equal concentrations when applied to standard reactor conditions. Unlike several lumping approaches found in literature, no calculation results from the primary mechanism have been employed prior to the application of our chemical lumping strategy. An 807 species and 7807 individual reactions detailed mechanism comprising n‐decane, α‐methylnaphthalene and methyl decanoate has been used. The thermodynamic data have been analyzed and 74 isomer groups have been identified within the oxidation of n‐decane and methyl decanoate. The mechanism reduction has led to a mechanism size of 463 species and 7600 reactions. Thereafter the lumped mechanism has been checked under several reactor conditions and over a broad range of temperature, pressure, and equivalence ratio in order to quantify the accuracy of the proposed approach. In all cases, very good agreement between the predictions obtained using the lumped and the detailed mechanism has been observed with an overall absolute error below 12%. Effects of the lumping procedure on sensitivities and on isomer concentrations were considered to further demonstrate the validity of the proposed approach.