Towards Clean Propulsion with Synthetic Fuels: Computational Aspects and Analysis

In order to support sustainable powertrain concepts, synthetic fuels show significant potential to be a promising solution for future mobility. It was found that $$\mathrm {CO_2}$$ emissions during the combustion process of synthetic fuels can be reduced compared to conventional fuels and that sustainable fuel production pathways exists. Furthermore, it is possible to burn some synthetic fuels soot-free, which indirectly also eliminates the well-known soot- $$\mathrm {NO}_x$$ tradeoff. However, in order to use the full potential of the new fuels, optimization of currently used injection systems needs to be performed. This is still challenging since fundamental properties are not known and pollutant formation is a multi-physics, multi-scale process. Therefore, the high-fidelity simulation framework CIAO is improved and optimized for predictive simulations of multiphase, reactive injections in complex geometries. Due to the large separation of scales, these simulations are only possible with current supercomputers. This work discusses the computational performance of the high-fidelity simulations especially focusing on vectorization, scaling, and input/output (I/O) on Hazel Hen (Cray XC40) supercomputer at the High Performance Computing Center Stuttgart (HLRS). Moreover, the impact of different internal nozzle flow initial conditions is shown, the effect of different chemical mechanisms studied, and the predictability of soot emissions investigated. The Spray A case defined by the Engine Combustion Network (ECN) is used as the target case due to the availability of experimental data for this injector.