Predictive Engine Simulations based on a novel DoE/RANS approach with coefficient tabulation

Producing reliable in-cylinder simulations for quick turnaround engine development for industrial purposes is a challenging task. With the ongoing paradigm shift towards digital engineering, industry is forced to adjust its development and manufacturing processes away from prototyping and reliance on test bed results towards a virtual environment where optimization occurs before the actual hardware is available. In this work, an approach is presented, which can overcome one of the main disadvantages of RANS: the model coefficient tuning dependency. Using a Design of Experiments approach, it is shown that input parameters can be linked to ambient boundary conditions and therefore tabulated to eliminate lengthy tuning iterations between operating conditions.