This work presents a computational fluid dynamics (CFD) methodology to simulate diesel combustion in highpressure common-rail engines, which employ sophisticated injection strategies with rate shaping to reduce the in-cylinder pollutant formation and achieve the emission legislation EU6 and US Tier2 Bin 5. The physics of the mixture formation and its effects on combustion and, hence, on emission formation needs to be correctly described by the numerical tool. At the same time, in order to be used in a design and optimization stage of a new engine, the CFD methodology must be very efficient in terms of time requirements. For these purposes, the authors propose and assess the capabilities of the characteristic time-scale combustion model (CTC). The Shell autoignition model is used to predict autoignition, while the standard CTC model was improved by introducing a new expression to compute the characteristic time scale to be applied also in the presence of very high EGR rates. Soot emissions were predicted by the eight reaction step mechanism proposed by Fusco et al., and the extended Zeldovich mechanism was used to estimate NO x concentration. The proposed combustion model was implemented into the Lib-ICE code, which is a set of libraries and applications for internal combustion engine modeling developed by the authors under the OpenFOAM technology. Validation considers two different sets of operating modes, which are representative of common driving conditions and considered to be challenging for the proposed CFD methodology.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.