Simulation today for the engines of tomorrow

Traditional internal combustion (IC) engine combustion simulations involve computational fluid dynamics (CFD) models that use a simplified chemistry representation for fuel combustion. The chemistry included in the models ranges from just a few molecular species to about 50 species for diesel fuel, for example.

For conventional diesel and gasoline engines, these approaches have historically been good enough because the fluid-mixing effects dominated the chemical kinetics effects in predicting engine performance.

New engine designs present new simulation challenges

New, high-efficiency, low-emissions designs present technical challenges that are highly influenced by kinetics (e.g., dual-fuel engines, staged spray injections, premixed charge compression ignition (PCCI) combustion). What proved to be good enough for the design of yesterday’s engines is insufficient for today’s designs. A consistent failing of current CFD codes, as cited by the industry, shows that past solutions cannot be relied on to predict values or even to project accurate trends of critical combustion behaviors such as ignition, flame propagation and emissions.

This issue is exacerbated by the fact that the fuels landscape continues to evolve and become more complex. Where yesterday’s engines were designed for a single fuel type, such as diesel or gasoline, today’s engine specifications demand fuel flexibility while achieving lower emissions.

The Model Fuels Consortium (MFC) is an industry-led program, currently in its sixth year, which has developed both the detailed chemical mechanisms and the tools required to simulate real fuel behavior. While the MFC has been successful in developing fuel mechanisms that accurately simulate real fuel chemistry, it has proved the impracticality of reducing these mechanisms to the point that they can be incorporated into contemporary CFD simulations without a substantial loss in accuracy. MFC researchers have recognized that the focus should shift from trying to get reliable results with mechanisms so severely reduced that they cannot capture real fuel behavior, to enhancing the ability of simulation tools to use mechanisms with the necessary level of detail.

A Dept. of Energy (DOE) scientific laboratory recently acknowledged the critical role that advanced simulation has in developing engines for reduced greenhouse gas (GHG) emissions in a white paper entitled, “Predictive Simulation of Combustion Engine Performance in an Evolving Fuel Environment.”1

The paper points out that engine manufacturers must move to “change from a test-first culture to an analysis-led design process” and that “a predictive simulation toolkit would accelerate the market transformation to high-efficiency, clean power sources for transportation.” Kinetics is recognized as a critical area for advancement supporting the design of clean, fuel-flexible engines that reduce GHG emissions.

Another key area of concern in engine simulation has been spray modeling. The choice of the spray model can have a significant impact on a simulation’s time-to-solution and accuracy of results. Most of the spray models used presently are highly mesh dependent, which requires that valuable innovation time be spent on calibration versus experimental data or adding complexity to the mesh to find an acceptable combination of spray-model parameters and grid. Understanding how to do this calibration requires specific expertise and makes it difficult for widespread utilization of a predictive CFD methodology across the organization.

The lack of reliability in combustion simulations is likely caused by a lack of detail in the way the fuel-spray and combustion kinetics are represented. Because the industry has been limited in the amount of chemistry detail it could practically incorporate into a CFD simulation, work has focused on turbulence-mixing phenomena, use of approximate combustion models and meshing. But, because of the increasing challenges in today’s engine design environment, attention is now focused on improving the quality of the modeling of the spray and kinetic phenomena.