Predictive CFD Auto-Tuning Approach for In-Cylinder Simulations of Two Small-Bore LDD Engines 2019-24-0033

Tightening emission regulations and accelerating production cycles force engine developers to shift their attention towards virtual engineering tools. When simulating in-cylinder processes in commercial LDD DI engine development, the trade-off between run time and accuracy is typically tipped towards the former. High-fidelity simulation approaches which require little tuning would be desirable but require excessive computing resources. For this reason, industry still favors low-fidelity simulation approaches and bridges remaining uncertainties with prototyping and testing. The problem with low-fidelity simulations is that simplifications in the form of sub models introduce multi variable tuning parameter dependencies which, if not understood, impair the predictive nature of CFD simulations.

In previous work, the authors have successfully developed a boundary condition dependent input parameter table. This parameter table showed outstanding results for lab-scale experiments for over 40 varying operating conditions. The objective in this paper is first to identify the necessary considerations to adjust for the inherent differences between lab-scale and real engine conditions and then implement this parameter table into industry relevant conditions. With this approach the appropriate simulation setup for a real EU6 diesel engine can be predefined by the boundary conditions without previous tuning iterations. The performance of the simulation will be assessed based on its capability to match experimental heat release and chamber pressure data. The approach shown here has the potential to remove the necessity of lengthy tuning iterations and lays the groundwork for novel auto-tuned and predictive in-cylinder simulations.