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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.

The automotive market’s need for ever cleaner and more efficient powertrains, delivered to market in the shortest possible time, has prompted a revolution in digital engineering. Virtual hardware screening and engine calibration, before hardware is available is a highly time and cost-effective way of reducing development and validation testing and shortening the time to bring product to market. Model-based development workflows, to be predictive, need to offer realistic combustion rate responses to different engine characteristics such as port and fuel injector geometry. The current approach relies on a combination of empirical, phenomenological and experienced derived tools with poor accuracy outside the range of experimental data used to validate the tool chain, therefore making the exploration of unconventional solutions challenging.

The Direct Numerical Simulation (DNS) approach to solving the fundamental transport equations down to the smallest scales of motion is favorable should the requirement be a truly predictive solution of fluid dynamic problems, but the simulation run times are unacceptable for most practical industrial applications. Despite the steadily increasing computational capabilities, Reynolds Averaged Navier-Stokes (RANS) based frameworks remain the most commercially viable option for high volume sectors, like automotive. The sub models within RANS simplify the description of key physical phenomena and include several numerical constants. These so-called “tuning constants” introduce multivariable dependencies that are almost impossible to untangle with local sensitivity studies.