Nowadays, the improvement of the efficiency of the combustion engines in the automotive field is the main key towards greenhouse gas (GHG) emission reduction. The performance of a combustion engine is tightly related to the friction force between the cylinder liner and the piston rings. It is believed that this friction can be significantly reduced by optimising the surface topography of the cylinder.
Traditionally, deterministic models or expensive experimental tests are used to solve these issues. We study here a numerical optimisation approach of this surface, the main purpose being the friction loss reduction, and without prejudice to the oil consumption. This approach consists in four numerical tools, described as follows.
A previous work in this field by Decencière and Jeulin using mathematical morphology techniques provided us with surface analysis, decomposition and simulation tools. We use these tools here for the analysis, filtering, decomposition or correction of the liner surface images.
A tool for texture simulation under constraints has been developed in order to generate new liner surface textures, characterised by better friction and oil consumption performances than those presented by a reference surface (M50). These new textures constitute the search space for latter optimisation tasks.
The 3D incompressible viscous flow of a fluid dragged by a smooth plate over a rough surface is studied within the framework of a friction prediction tool for the hydrodynamic contact between the liner and the segments. Navier-Stokes and Reynolds equations are used for the numerical resolution of the flow. The algorithms are tested by analysing and comparing their results with analytically known flows and the physical model will be validated by the experimental results provided by our partners. This 3D tool will enable us to compute local or global physical measures like friction power loss, drag, load or fluid flow and to come to significant results concerning the influence of the surface topography on the contact tribological performance.
Finally, the parametric optimisation of the liner surface texture is performed, over textures generated by the simulation tool and using as objective function the friction prediction tool, in order to find out useful information about the optimal values taken by the parameters of the new simulated textures. In parallel, stochastic optimisation tools have been developed in order to operate an exhaustive shape optimisation task of the elementary motifs encountered in the periodic texture definition. In the end, new liner surface textures are proposed for experimental tests.
KEY-WORDS: image processing, mathematical morphology, hydrodynamic friction, Navier-Stokes, fluid dynamics, CFD, incompressible flow, stochastic optimisation.