In this work, different methodologies were studied by means of finite element modelling (FEM) with the aim of predicting the mechanical behaviour of high and low relative density (q r ) polymeric foams. Virtual structures which resemble the real ones were created by various computer-based tools. These tools were employed to make up both "unit cells" and random structural units so-called "Representative Volume Elements" (RVE). Low q r foams are usually modelled as regular (tetrakaidecahedron) and irregular (Voronoi tessellations) 3D structures made of structural elements (beams and shells). These types of finite elements are only applicable if the ratio between longitudinal and axial dimensions exceeds a certain value and therefore, there is a practical relative density limit above which these elements are not suitable. Alternatively, virtual low q r foams were created by means of cellular automata which allow a close control of bubble growth and final cell character and do not show the previous limitation. Additionally, virtual high q r structures (q r > 0.5) consisting of isolated bubbles or cells were created by random incorporation of cell sets whose size distributions adjust to experimentally measured ones. A random sequential adsorption algorithm (RSA) which accurately controls final thickness of ligaments between cells was programmed for this purpose. FEM results of this kind of virtual foams are compared with experimentally tested mechanical properties. Moreover, impact of structural parameters (mean cell size) on elastic modulus and compressive collapse stress is critically assessed.

The design of a wind turbine implies the simulation of definite conditions as specified in the standards. Among those operational conditions, rare events such as extreme gusts or external faults are included, which may cause high structural loads. Such extreme design load cases usually drive the design of some of the main components of the wind turbine: tower, blades and mainframe. Two different strategies are hence presented to mitigate the loads, deriving from extreme load cases, on the basis of the detection of wind gusts by means of ad hoc synthesized artificial neural networks. This tool is embedded into the main control algorithm and allows it to detect the gust in advance, to anticipate the control reaction, and by doing so reducing extreme loads. One of the strategies performs a controlled stop when wind gust is detected. The other rides through wind gusts without stopping, i.e., without affecting the wind turbine normal operation. Aeroelastic simulations of the Alstom Wind's wind turbines using these techniques have shown significant reductions in the extreme loads for all standard IEC 61400-1, edition 2 DLC 1.6 cases. In particular, the overall ultimate loads are largely reduced for blade root and tower base bending moments, with a direct impact on the structural design of those components.

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