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Abradable materials are used to decrease the gas consumption of aircraft engines by minimizing the gap between the blade tips and the stator. The key idea consists in using the blades themselves to machine the gap on the abradable coating. The best compromise between soft and hard coating properties has to be reached to avoid blades wear and prevent coating erosion by gas flux and particles. The plastic parameters of abradable coating were identified by using an optimization process directly connected to FEA. The first order optimization method (conjugate gradient strategy + golden section algorithm) was applied to achieve the optimal solution. A good agreement was found between experimental and numerical results. The plastic parameters were used to study the hardness variability of abradable materials with the coating thickness. Surprisingly, a minimum hardness value was found while it was expected that hardness should be always decreasing with thickness. It has been demonstrated that this minimum is produced by the boundary conditions influence on hardness measurement. This research work was completed within the Seal-Coat project funded by the European Commission under the FP5 Growth Program.

A new approach for continuum structural topology optimization using genetic algorithms is presented in this paper. The proposed approach is based on a representation by adjacency where the principle is founded on the concept of connectivity of finite elements, considered as cells. This principle is expressed by an adjacency matrix similar to that used in the graph theory. The encoding of the structure solutions uses this matrix by transforming it into a binary string. The research of optimal solution, i.e. the optimal material distribution, is interpreted in this approach by the determination of the connectivity of elements (cells). Using density variable, the approach has some common points with the homogenization techniques. The proposed approach is tested with simple benchmark applications.