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Interactions between in-cylinder combustion and emission aftertreatment need to be understood for optimizing the overall powertrain system. Numerical investigations can aid this process. For this purpose, simple and numerically fast, but still accurate models are needed for in-cylinder combustion and exhaust aftertreatment. The chemical processes must be represented in sufficient detail to predict engine power, fuel consumption, and tailpipe emission levels of NOx, soot, CO and unburned hydrocarbons. This paper reports on a new transient one-dimensional catalyst model. This model makes use of a detailed kinetic mechanism to describe the catalytic reactions. A single-channel or a set of representative channels are used in the presented approach. Each channel is discretized into a number of cells. Each cell is treated as a perfectly stirred reactor (PSR) with a thin film layer for washcoat treatment. Heat and mass transport coefficients are calculated from Nusselt and Sherwood laws.

The ability to predict cyclic variations is certainly useful in studying engine operating regimes, especially under unstable operating conditions where one single cycle may differ from another substantially and a single simulation may give rather misleading results. PDF based models such as Stochastic Reactor Models (SRM) are able to model cyclic variations, but these may be overpredicted if discretization is too coarse. The range of cyclic variations and the dependence of the ability to correctly assess their mean values on the number of cycles simulated were investigated. In most cases, the average values were assessed correctly on the basis of as few as 10 cycles, but assessing the complete range of cyclic variations could require a greater number of cycles. In studying average values, variations due too coarse discretization being employed are smaller than variations originating from changes in physical parameters, such as heat transfer and mixing parameters.

Today numerical models are a major part of the diesel engine development. They are applied during several stages of the development process to perform extensive parameter studies and to investigate flow and combustion phenomena in detail. The models are divided by complexity and computational costs since one has to decide what the best choice for the task is. 0D models are suitable for problems with large parameter spaces and multiple operating points, e.g. engine map simulation and parameter sweeps. Therefore, it is necessary to incorporate physical models to improve the predictive capability of these models. This work focuses on turbulence and mixing modeling within a 0D direct injection stochastic reactor model. The model is based on a probability density function approach and incorporates submodels for direct fuel injection, vaporization, heat transfer, turbulent mixing and detailed chemistry.

The three-way catalytic converter (TWC) is the most common catalyst for gasoline engine exhaust gas after treatment. The reduction of carbon monoxide (CO), nitrogen oxides (NOx) and unburned hydrocarbons (HC) is achieved via oxidation of carbon monoxide and hydrocarbons, and reduction of nitrogen oxides. These conversion effects were simulated in previous works using single-channel approaches and detailed kinetic models. In addition to the single-channel model multiple representative catalyst channels are used in this work to take heat transfer between the channels into account. Furthermore, inlet temperature distribution is considered. Each channel is split into a user given number of cells and each cell is treated like a perfectly stirred reactor (PSR). The simulation is validated against an experimental four-stroke engine setup with emission outputs fed into a TWC.

The introduction of a physics-based zero-dimensional stochastic reactor model combined with tabulated chemistry enables the simulation-supported development of future compression-ignited engines. The stochastic reactor model mimics mixture and temperature inhomogeneities induced by turbulence, direct injection and heat transfer. Thus, it is possible to improve the prediction of NOx emissions compared to common mean-value models. To reduce the number of designs to be evaluated during the simulation-based multi-objective optimization, genetic algorithms are proven to be an effective tool. Based on an initial set of designs, the algorithm aims to evolve the designs to find the best parameters for the given constraints and objectives. The extension by response surface models improves the prediction of the best possible Pareto Front, while the time of optimization is kept low.

Spray modeling is among the main aspects of mixture formation and combustion in internal combustion engines. It plays a major role in pollutant formation and energy efficiency although adequate modeling is still under development. Strong grid dependence is observed in the droplet-based stochastic spray model commonly used. As an alternative, an interactive model called 'SprayLet' is being developed for spray simulations based on one-dimensional integrated equations for the gas and liquid phases, resulting from cross-sectionally averaging of multi-dimensional transport equations to improve statistical convergence. The formulated one-dimensional cross-section averaged system is solved independently of the CFD program to provide source terms for mass, momentum and heat transfer between the gas and liquid phases. The transport processes take place in a given spray cone where the nozzle exit is automatically resolved.