Engine knock is an important phenomenon that needs consideration in the development of gasoline-fueled engines. In our days, this development is supported using numerical simulation tools to further understand and predict in-cylinder processes. In this work, a model tool chain which uses a detailed chemical reaction scheme is proposed to predict the auto-ignition behavior of fuels with different octane ratings and to evaluate the transition from harmless auto-ignitive deflagration to knocking combustion. In our method, the auto-ignition characteristics and the emissions are calculated using a gasoline surrogate reaction scheme containing pathways for oxidation of ethanol, toluene, n-heptane, iso-octane and their mixtures. The combustion is predicted using a combination of the G-equation based flame propagation model utilizing tabulated laminar flame speeds and well-stirred reactors in the burned and unburned zone in three-dimensional Reynolds-averaged Navier-Stokes. Based on the detonation theory by Bradley et al., the character and the severity of the auto-ignition event are evaluated. Using the suggested tool chain, the impact of fuel properties can be efficiently studied, the transition from harmless deflagration to knocking combustion can be illustrated and the severity of the autoignition event can be quantified.

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In this paper, a partially stirred stochastic reactor model is presented as an alternative for the modeling of 17 biomass pyrolysis and gasification. Instead of solving transport equations in all spatial dimensions as in CFD 18 simulations, the description of state variables and mixing processes is based on a probability density function, making 19 this approach computationally efficient. The virtual stochastic particles, an ensemble of flow elements consisting of 20 porous solid biomass particles and surrounding gas, mimic the turbulent exchange of heat and mass in practical 21 systems without the computationally expensive resolution of spatial dimensions. Each stochastic particle includes 22 solid phase, pore gas and bulk gas interaction. The reactor model is coupled with a chemical mechanism for both 23 surface and gas phase reactions. A Monte Carlo algorithm with operator splitting is employed to obtain the numerical 24 solution. Modeling an entrained flow gasification reactor demonstrates the applicability of the model for biomass 25 fast pyrolysis and gasification. The results are compared with published experiments and detailed CFD simulations. 26 The stochastic reactor model is able to predict all major species in the product gas composition very well for only a 27 fraction of the computational time as needed for comprehensive CFD. 28 29