Pedro Pereira

High temperature superconducting (HTS) machines are recognized to offer several advantageous features when comparing to conventional ones. Amongst these, highlights the decrease in weight and volume of the machines, due to increased current density in conductors or the absence of iron slots' teeth; or the decrease in AC losses and consequent higher efficiency of the machines, even accounting for cryogenics. These concepts have been already demonstrated and some machines have even achieved commercial stage. In this paper, several alternative approaches are applied to electrical motors employing HTS materials. The first one is an all superconducting linear motor, where copper conductors and permanent magnets are replaced by Bi-2223 windings and trapped flux magnets, taking advantage of stable levitation due to flux pinning, higher current densities and higher excitation field. The second is an induction disk motor with Bi-2223 armature, where iron, ironless and hybrid approaches are compared. Finally, an innovative command strategy, consisting of an electronically variable pole pairs' number approach, is applied to a superconducting hysteresis disk motor. All these concepts are being investigated and simulation and experimental results are presented.

In this paper an optimization-based approach for the design of RF integrated inductors is addressed. For the characterisation of the inductor behaviour the double pi-model is used. The use of this model is twofold. On one hand it enables the generation of the inductor characterisation in a few seconds. On the other hand its integration into the optimization procedure is straightforward. For the evaluation of the model element values analytical expressions based on technology parameters as well as on the device geometric characteristics are used. The use of a technology-based methodology for the evaluation of the model parameters grants the adaptability of the model to any technology. The inductor analytical characterization is integrated into an optimization-based tool for the automatic design of RF integrated inductors. This tool uses a modified genetic algorithm (MGA) optimization procedure, which has proved its validation in previous work. Due to the design parameter constraints nature as well as the topology constraints, discrete variables optimization techniques are used. The accuracy of the results is checked against a non-commercial software.

This paper introduces an optimization methodology for the design of RF varactors. The characterization of the varactor behaviour is supported by a set of equations based on technological parameters, granting the accuracy of the results, as well as the adaptability of the model to any technology. The varactor design is achieved through the implementation of a Genetic Algorithms (GA) optimization methodology, which is able to deal with continuous and/or discrete variables, making possible to suit both technological and layout constraints. A set of working examples for UMC130 technology are addressed. The results presented, spotlight the potential of varactor analytical model, combined with a GA optimization procedure, when integrated in optimization design tools. The accuracy of the results is checked against HSPICE simulator.

In this paper, three metaheuristics are investigated to optimize RF Integrated Inductors; namely Differential Evolution (DE) from the field of evolutionary computation, Gravitational Search Algorithm (GSA) based on the law of gravity and mass interactions and Particle Swarm Optimization (PSO) inspired by swarm behaviors in nature. A particular interest is given to the optimization of RF Integrated Inductors. Performances in terms of optimum quality and computing time of the metaheuristics are checked via three test functions and one application that consist of optimizing performances of characterize integrated inductors based on the double$π$-model.