Abstracts - Dipartimento di Elettronica Applicata
Abstracts - Dipartimento di Elettronica Applicata
Abstracts - Dipartimento di Elettronica Applicata
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Meta 2010 & FEM 2010 – Rome, 13-15 December 2010<br />
Optimization of Multistage Depressed Collectors by<br />
using FEM and METEO<br />
Salvatore Coco (1) , Antonino Laudani (1) , Giuseppe Pulcini (2) , Francesco Riganti<br />
Fulginei (2) , Alessandro Salvini (2)<br />
(1) University of Catania, DIEES Catania, Italy – e-mail: alaudani@<strong>di</strong>ees.unict.it<br />
(2) Roma Tre University, Department of Applied Electronics - Roma, Italy<br />
Specialized 3-D simulators are required by TWT multistage depressed collector<br />
(MDC) designers to help them to analyze and test new and arbitrarily shaped<br />
geometries for high efficiency TWTs. To perform such a task a Finite Element (FE)<br />
approach can be pursued, since it allows a very flexible meshing and gives the<br />
possibility of using irregular meshes to fit properly the MDC’s geometry; in such a<br />
way complex geometries can be accurately simulated [1]. Unfortunately, the use of<br />
optimization techniques in the design process of these devices is rarely used [2],<br />
because of the high number of parameters and the high computational cost of<br />
efficiency evaluation (the fitness function). In this paper the authors present the<br />
application of a novel metaheuristics technique called METEO (Metric-Topologicalevolutionary-Optimization)<br />
[3], to optimize the performance of multistage collectors,<br />
simulated by means of Finite element collector and electron gun simulator<br />
COLLGUN [4]. METEO [3] is a hybrid algorithm composed by three <strong>di</strong>fferent<br />
heuristics: FSO (Flock of Starlings Optimization) [5], PSO (Particle Swarm<br />
Optimization), and BCA (Bacterial Chemotaxis Algorithm); it performs the<br />
optimization using both the topological as the metric rules. In fact the mentioned<br />
heuristics own a <strong>di</strong>fferent performance taken alone, in particular FSO presents a high<br />
degree of exploration, and a good capability to escape from local minima, whilst PSO,<br />
and BCA own a good proprieties of convergence. Besides, they offer a natural parallel<br />
implementation that allows spee<strong>di</strong>ng up the whole process of optimization. This<br />
characteristic can be useful exploited in order to obtain the desired target with an<br />
acceptable computational cost.<br />
References<br />
[1] S. Coco, F. Emma, A. Laudani, S. Pulvirenti, M. Sergi, “COCA: A Novel 3-D FE Simulator for<br />
the Design of TWTs Multistage Collectors”, IEEE trans. on electron devices, 48 , 24-31, 2001.<br />
[2] T. K. Ghosh, R.G. Carter, “Optimization of Multistage Depressed Collectors”, IEEE trans. on<br />
electron devices, 54 , 2031-2039, 2007.<br />
[3] G. Pulcini, F. Riganti Fulginei, A. Salvini, “Metric-Topological-Evolutionary Optimization”,<br />
OIPE2010 Procee<strong>di</strong>ngs, Sofia - Bulgaria, Sept. 14-18 2010, pp. 3-4.<br />
[4] S. Coco, S. Corsaro, A. Laudani, G. Pollicino, R. Dionisio, and R. Martorana, “COLLGUN: A 3D<br />
FE simulator for the design of TWT's electron guns and multistage collectors”, Scientific<br />
Computing in Electrical Engineering, Mathematics in Industry, Springer-Verlag, 9, 175-180, 2006<br />
[5] F. R. Fulginei, A. Salvini, “Model identification by the Flock-of-Starlings Optimization”, Int.<br />
Journal of applied Electromagnetics and Mechanics, vol. 30, n. 3-4, pp. 321-331, 2009.<br />
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