Francesco Grimaccia

Novel Speed-Bump Design and Optimization for Energy Harvesting From Traffic

In recent years, the increase in computational capability and development of innovative multiphysic techniques has determined a growing interest toward modeling and optimization in engineering system design for green energy applications. In this field, advanced soft computing techniques can be applied by engineers to several problems and to be used in an optimization process to find out the best design and, thus, to improve the system performance. These techniques also promise to give new impulse to research on renewable systems and, particularly in the last five years, on the so-called energy-harvesting devices (EHDs). This paper presents the optimization of a tubular permanent-magnet linear generator used for applications of energy harvesting from traffic. The optimization process is developed by means of hybrid evolutionary algorithms to reach the best overall system efficiency and the impact on the environment and transportation systems. Finally, an experimental validation of the designed EHD prototype is presented.

Differentiated Meta-PSO for rectangular ring antenna with proximity-coupled feed

Particle Swarm Optimization (PSO), a robust stochastic evolutionary computational technique, has been here successfully applied to different electromagnetic optimization problems. In order to fulfill the requirement of fast convergence to get results in a very short time, a variation of original method has been proposed using the so called differentiated Meta-PSO. This paper presents the effectiveness of Meta-PSO technique in optimizing complex multidimensional problem relating to antenna engineering design implementation. To verify the robustness of this optimization tool and develop a full wave analysis, all these concepts are integrated into a case study represented by a rectangular ring antenna with proximity-coupled microstrip feed line.

Multi-layer antenna design with evolutionary optimization

In the last decades several population-based heuristic search techniques have been developed for the optimization of combinatorial electromagnetic problems, all of them are modeled on the concepts of natural selection and evolution e.g. Genetic Algorithm (GA), Particle Swarm Optimization (PSO). Moreover, vast variations of original methods can be found in the literature. In this paper, with the intent of examine the reliability and convergence speed of those methods, some comparisons in optimizing a dual-ring microstrip antenna will be presented. Preliminary results are here presented to demonstrate the analysis of robustness and effectiveness of optimization tools.