Overview
In Ordered ignition engines (MAC) of conventional technology, the flame is likely to reach the walls in places as soon as 30% of the feed is consumed, resulting in a high proportion of fuel that burns in the nearby walls. A large number of technical developments proposed to improve the energy efficiency of MAC - and particularly the downsizing and the increasing of the compression ratio, which tend to increase the confinement of the charge - are likely to further increase this proportion. With the aim to use the 3D combustion simulation for the design of these engines, it is absolutely necessary to have an adequate and efficient model of the phenomenon of flame-wall interaction.
The project INTERMAC proposed to develop a new modeling approach to predict the RANS parietal heat flow based on the consideration of the flame-wall interaction. The objective was to develop and validate this new model to have a simulation approach with better predictability incorporating flame effects on the flow of heat to the walls.
To achieve this, much of the work done during the project was devoted to the implementation of several experimental databases specifically designed for the study of the phenomenon of flame-wall interaction.
Funding
Results
The main outcome of Intermac project was to demonstrate, the potential of the new Alpha model to better predict the parietal heat flow including flow peaks generated when the flame interacts with the walls. In addition, the results obtained with the new Alpha model helped to highlight the flow peaks potentially generate large temperature gradients in the chamber walls. The control of temperature gradients at the walls is now a key in the design and dimensioning of new generation combustion chambers (downsized spark ignition engines). The new model developed under the project, the Alpha model, is potentially a significant source of improvement of simulation tools for a better prediction of heat flow. It is also noteworthy that the experimental databases and associated elements of understanding are significant results. A first model validation was conducted through experiments carried out under the project, but these experimental data will also be important information for the community elements for understanding these complex phenomena.