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The reduction of cycle-to-cycle variations (CCV) is a prerequisite for the development and control of spark-ignition engines with increased efficiency and reduced engine-out emissions. To this end, Large-Eddy Simulations (LES) can improve the understanding of stochastic in-cylinder phenomena during the engine design process, if the employed modeling approach is sufficiently accurate. In this work, an inhouse code has been used to investigate CCV in a direct-injected spark ignition (DISI) engine under fuel-lean conditions with respect to a stoichiometric baseline operating point. It is shown that the crank angle when a characteristic fuel mass fraction is burned, e.g. MFB50, correlates with the equivalence ratio computed as a local average in the vicinity of the spark plug. The lean operating point exhibits significant CCV, which are shown to be correlated also with the in-cylinder subfilter-scale (SFS) kinetic energy.

The performance of Gasoline Direct Injection (GDI) engines is governed by multiple physical processes such as the internal nozzle flow and the mixing of the liquid stream with the gaseous ambient environment. A detailed knowledge of these processes even for complex injectors is very important for improving the design and performance of combustion engines all the way to pollutant formation and emissions. However, many processes are still not completely understood, which is partly caused by their restricted experimental accessibility. Thus, high-fidelity simulations can be helpful to obtain further understanding of GDI injectors. In this work, advanced simulation and experimental methods are combined in order to study the spray characteristics of two different 3-hole GDI injectors.

Although spark-ignited engines have a considerable development history, the relevant flow physics and geometry design implications are still not fully understood. One reason is the lack of experimental and numerical methods with sufficiently high resolution or capabilities of capturing stochastic phenomena which could be used as part of the development cycle. More recently, Large-Eddy simulation (LES) has been identified as a promising technique to establish a better understanding of in-cylinder flow variations. However, simulations of engine configurations are challenging due to resolution as well as modeling requirements and computational cost for these unsteady multi-physics problems. LES on full engine geometries can even be prohibitively expensive. For this reason, the size of the computational LES domain is here reduced to the region of physical interest and boundary conditions are obtained from a RANS simulation of the whole experimental flow domain.

Large-eddy simulation (LES) can be a very important tool to support and accelerate the energy transition to green technologies and thus play a significant role in the fight against climate change. However, especially LES of reactive flows is still challenging, e.g., with respect to emission prediction, and perfect subfilter models do not yet exist. Recently, new subfilter models based on physics-informed generative adversarial networks (GANs), called physics-informed enhanced super-resolution GANs (PIESRGANs), have been developed and successfully applied to a wide range of flows, including decaying turbulence, sprays, and finite-rate-chemistry flows. This technique, based on AI super-resolution, allows for the systematic derivation of accurate subfilter models from direct numerical simulation (DNS) data, which is critical, e.g., for the development of efficient energy devices based on advanced fuels.