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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.

Direct injection (DI) compressed natural gas (CNG) engines are emerging as a promising technology for highly efficient and low-emission engines. However, the design of DI systems for compressible gas is challenging due to supersonic flows and the occurrence of shocks. An outwardly opening poppet-type valve design is widely used for DI-CNG. The formation of a hollow cone gas jet resulting from this configuration, its subsequent collapse, and mixing is challenging to characterize using experimental methods. Therefore, numerical simulations can be helpful to understand the process and later to develop models for engine simulations. In this article, the results of high-fidelity large-eddy simulation (LES) of a stand-alone injector are discussed to understand the evolution of the hollow cone gas jet better.

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.