This work presents an analysis methodology based on the use of the Finite Element Method (FEM) nowadays considered one of the main numerical tools for solving Boundary Value Problems (BVPs). The proposed methodology, so-called cg-FEM (Cartesian grid FEM), has been implemented for fast and accurate numerical analysis of 2D linear elasticity problems. The traditional FEM uses geometry-conforming meshes; however, in cg-FEM the analysis mesh is not conformal to the geometry. This allows for defining very efficient mesh generation techniques and using a robust integration procedure, to accurately integrate the domain’s geometry. The hierarchical data structure used in cg-FEM together with the Cartesian meshes allow for trivial data sharing between similar entities. The cg-FEM methodology uses advanced recovery techniques to obtain an improved solution of the displacement and stress fields (for which a discretization error estimator in energy norm is available) that will be the output of the analysis. All this results in a substantial increase in accuracy and computational efficiency with respect to the standard FEM. cg-FEM has been applied in structural shape optimization showing robustness and computational efficiency in comparison with FEM solutions obtained with a commercial code, despite the fact that cg-FEM has been fully implemented in MATLAB.

The quality of current collection becomes a limiting factor when the aim is to increase the speed of the present railway systems. In this work an attempt is made to improve current collection quality optimising catenary geometry by means of a genetic algorithm (GA). As contact wire height and dropper spacing are thought to be highly influential parameters, they are chosen as the optimisation variables. The results obtained show that a GA can be used to optimise catenary geometry to improve current collection quality measured in terms of the standard deviation of the contact force. Furthermore, it is highlighted that apart from the usual pre-sag, other geometric parameters should also be taken into account when designing railway catenaries.

Some industrial processes are modelled by parametric partial differential equations. Integrating computational modelling and data assimilation into the control process requires obtaining a solution of the numerical model at the characteristic frequency of the process (real-time). This paper introduces a computational strategy allowing to efficiently exploit measurements of those industrial processes, providing the solution of the model at the required frequency. This is particularly interesting in the framework of control algorithms that rely on a model involving a set of parameters. For instance, the curing process of a composite material is modelled as a thermo-mechanical problem whose corresponding parameters describe the thermal and mechanical behaviours. In this context, the information available (measurements) is used to update the parameters of the model and to produce new values of the control variables (data assimilation). The methodology presented here is devised to ensure the possibility of having a response in real-time of the problem and therefore the capability of integrating it in the control scheme. The Proper Generalized Decomposition is used to describe the solution in the multi-parametric space. The real-time data assimilation requires a further simplification of the solution representation that better fits the data (reconstructed solution) and it provides an implicit parameter identification. Moreover, the analysis of the assimilated data sensibility with respect to the points where the measurements are taken suggests a criterion to locate of the sensors.Peer ReviewedPostprint (author’s final draft

In situ consolidation of pre-impregnated thermoplastic composite requires as much as possible intimate contact as well as an appropriate molecular diffusion across the interface, where the intimate contact takes place. The last depends on the temperature that influences the molecular mobility. Thus, in order to evaluate the consolidation, first, we must describe accurately the composite surfaces and then, when placed in contact for manufacturing the laminate, a high-resolution simulation of the resulting thermal model must be accomplished to have access to the thermal history at the rough contact surface. In this paper, we proceed in three steps: (i) first the pre-preg surface is assimilated to a random process, a fractional Brownian motion, that can be characterized by two main descriptors, the topothesy and the fractal dimension; then, (ii) assuming the rough surface is represented by a Cantor set, the different parameters describing the fractal are determined from the statistical surface description; and finally, (iii) the resulting Cantor set representation will be considered as the geometrical support of a high-resolution thermal analysis accomplished within the proper generalized decomposition framework that allows exploring scales never until now attained.