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The increasing need for cleaner and more efficient combustion systems has promoted a paradigm shift in the automotive industry. Virtual hardware and engine calibration screening at the early development stage, has become the most effective way to reduce the time necessary to bring new products to market. Virtual engine development processes need to provide realistic engine combustion rate responses for the entire engine map and for different engine calibrations. Quasi Dimensional (Q-D) combustion models have increasingly been used to predict engine performance at multiple operating conditions. The physics-based Q-D turbulence models necessary to correctly model the engine combustion rate within the Q-D combustion model framework are a computationally efficient means of capturing the effect of port and combustion chamber geometry on performance. A rigorous method of correlating the effect of air motion on combustion parameters such as heat release is required to enable novel geometric architectures to be assessed to deliver future improvements in engine performance.A previously assessed process using a combination of a 0-D combustion Stochastic Reactor Model (SRM), provided by LOGESoft, a 1-D engine system model and non-combusting, 'cold' CFD is used. The approach uses a single baseline CFD run and a user developed scalar mixing time (τSRM) response to quickly predict the Rate of Heat Release (RoHR). In this work, the physically-based response for τSRM has been further developed to consider the effect of Variable Valve Timing (VVT) for a variety of engine operating conditions. Cold CFD and 1-D engine simulations have initially been carried out to investigate changes in Turbulent Kinetic Energy (k) and its dissipation (ε) caused by VVT changes, allowing the engine Rate of Heat Release (RoHR) to be predicted. The change in the intake flow velocity was correlated to the scalar mixing time, τSRM resulting in a good engine RoHR prediction at the explored conditions.

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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.