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Institute
BTU
Advanced Predictive Diesel Combustion Simulation Using Turbulence Model and Stochastic Reactor Model
(2017)
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 (SRM) 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 CFD 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.
In this work we apply various concepts of mechanism reduction with a PDF based method for species profile
conservation. The reduction process is kept time efficient by only using 0D and 1D reactors. To account for the expansion phase in internal combustion engines a stochastic engine tool is used to validate the reduction steps.
The use of complex reaction schemes is accompanied by high computational cost in 3D CFD simulations but is particularly important to predict pollutant emissions in internal combustion engine simulations. One solution to tackle this problem is to solve the chemistry prior the CFD run and store the chemistry information in look-up tables. The approach presented combines pre-tabulated progress variable-based source terms for auto-ignition as well as soot and NOx source terms for emission predictions. The method is coupled to the 3D CFD code CONVERGE v2.4 via user-coding and tested over various speed and load passenger-car Diesel engine conditions. This work includes the comparison between the combustion progress variable (CPV) model and the online chemistry solver in CONVERGE 2.4. Both models are compared by means of combustion and emission parameters. A detailed n-decane/α-methyl-naphthalene mechanism, comprising 189 species, is used for both online and tabulated chemistry simulations. The two chemistry solvers show very good agreement between each other and equally predict trends derived experimentally by means of engine performance parameters as well as soot and NOx engine-out emissions. The CPV model shows a factor 8 speed-up in run-time compared to the online chemistry solver without compromising the accuracy of the solution.