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. Highfidelity 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.
Producing reliable in-cylinder simulations for quick-turnaround engine development for industrial purposes is a challenging task for modern computational fluid dynamics, mostly because of the tuning effort required for the sub-models used in the various frameworks (the Reynolds-averaged Navier–Stokes and large eddy simulation). Tuning is required because of the need for modern engines to operate under a wider range of conditions and fuels. In this article, we suggest a novel methodology based on automated simulation parameter optimisation that is capable of delivering a priori a coefficient matrix for each operating condition. This approach produces excellent results for multiple comparison metrics like liquid and vapour penetration lengths, radial and axial mass fraction and temperature distributions. In this article, we also show for the first time that input model coefficients can potentially be linked to ambient boundary conditions in a physically consistent manner. Changes in injection pressure, charge pressure and charge density are considered. This paves the way for the tabulation of the constants in order to eliminate lengthy tuning iterations between operating conditions and move towards adaptive simulations as the piston moves changing the in-cylinder conditions. An additional discussion is performed for the validity range of existent models given that in recent years there has been a shift towards more extreme thermodynamic conditions in the injection stage (reaching the limits of transcritical flows). Although in this work the framework was implemented in the Reynolds-averaged Navier–Stokes context because this is the tool of preference of digital engineering currently by automotive industries, the approach can be easily extended in large eddy simulation.
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