Dr Georgios Drosopoulos

The framework of three ongoing research efforts are presented below.

The description involves a) data-driven structural analysis and homogenization, b) multi-scale computational homogenization and c) analysis and optimization of nanocomposite materials.

a) Fusion of data mining and computational mechanics is a modern approach for the exploitation of available data within rigorous modeling. First steps in this direction have been focused on the usage of neural networks and other soft computing tools as metamodeling tools. This framework seems suitable for numerical homogenization techniques, where the lower-level analysis of a detailed Representative Volume Element (RVE) is replaced by a prediction based on a previously prepared database. Numerically prepared data are used here, although the method can be used with experimental data as well. In this case, the need for a constitutive description of the fine scale is bypassed, leading to data-driven analysis. Extraction of material properties from the database, required by the upper-level finite element analysis, can be based on backpropagation artificial neural networks. The method is applicable to composite materials and masonry structural systems.

b) Multi-scale computational homogenization stands for the exploitation of the microscopic scale of heterogeneous materials (masonry, concrete, composites) and the projection of average material properties in the macro, structural scale, in the framework of a concurrent analysis of both the macro and the microstructure. Within this method, the macroscopic constitutive behaviour is determined during simulation, after solving the microscopic problem and transferring the necessary information on the macroscopic scale. Related research efforts focus on applying unilateral contact in the microscopic scale of discrete composite materials, in a multi-scale framework. Damage across the boundaries of the Representative Volume Element (RVE) and the tensile resistance of the considered interfaces are also taken into account. In the macroscopic scale, the Extended Finite Element Method (XFEM) has been chosen for incorporating the microscopic damage, as a discontinuity, at the structural level.

c) Optimal design and analysis of graphene/fibre reinforced laminated nanocomposite plates with respect to maximizing the fundamental frequency or minimizing the weight is studied using numerical modelling and optimization. Optimal design solutions are given for different sets of design parameters. Purpose of this approach is to assess and compare different levels of optimization by means of a design efficiency index and as such to determine the effectiveness of different design parameters in maximizing the fundamental frequency or minimizing the weight. Optimization is implemented using a Sequential Quadratic Programming algorithm and the mechanical properties of graphene/fibre nanocomposite are determined via micromechanical relations. Vibration analysis is conducted by the finite element method using four-noded Mindlin plate elements. Results are obtained for simply supported (SSSS), clamped (CCCC) and simply supported-clamped boundary conditions for opposite edges (SCSC). It is observed that non-uniform distributions of graphene and fibre as well as fibre orientations are quite effective in improving the design efficiency.