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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.

In this work, we present an unsteady flamelet progress variable approach for diesel engine CFD combustion modeling. The progress variable is based on sensible enthalpy integrated over the flamelet and describes the transient flamelet ignition process. By using an unsteady flamelet library for the progress variable, the impact of local effects, for example variations in the turbulence field, effects of wall heat transfer etc. on the autoignition chemistry can be considered on a cell level. The coupling between the unsteady flamelet library and the transport equation for total enthalpy follows the ideas of the representative interactive flamelet approach. Since the progress variable gives a direct description of the state in the flamelet, the method can be compared to having a flamelet in each computational cell in the CFD grid.

A stochastic model based on a probability density function (PDF) was developed for the investigation of different conditions that determine knock in spark ignition (SI) engine, with focus on the turbulent mixing. The model used is based on a two-zone approach, where the burned and unburned gases are described as stochastic reactors. By using a stochastic ensemble to represent the PDF of the scalar variables associated with the burned and the unburned gases it is possible to investigate phenomena that are neglected by the regular existing models (as gas non-uniformity, turbulence mixing, or the variable gas-wall interaction). Two mixing models are implemented for describing the turbulent mixing: the deterministic interaction by exchange with the mean (IEM) model and the stochastic coalescence/ dispersal (C/D) model. Also, a stochastic jump process is employed for modeling the irregularities in the heat transfer.

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.