@article {3687,
	title = {Sand Pile Modeling of Multiseeded HTS Bulk Superconductors: Current Densities Identification by Genetic Algorithms},
	journal = {IEEE Transactions on Applied Superconductivity},
	volume = {23},
	year = {2013},
	note = {<p>DOI:10.1109/TASC.2012.2234187 </p>
},
	month = {June},
	pages = {8000804 - 8000804},
	abstract = {<p>The sand pile model, in conjunction with Bean model, is often applied to describe single grain bulk superconductors. However, in several applications such as electric motors, multiseeded bulks are needed, due to the need to increase sample dimensions. In this paper, an extension of the sand pile model is presented in order to manage this type of materials. Multiseeded HTS bulk superconductors, produced, e.g., by the top-seeded melt growth process, are characterized by intra- and intergrain currents, and these are reflected in the model. However, identifying these currents from flux density measurements is not straightforward, when considering more than one grain. In fact, the number of currents increases with the number of grains, and these have to be identified from the measured field surface. A method to identify these currents based on genetic algorithms is validated with artificial data and then used in real measurements.</p>
},
	keywords = {Bean model;critical current density (superconductivity);flux pinning;genetic algorithms;high-temperature superconductors;sandpile models;Bean model;artificial data;current density identification;electric motors;flux density measurements;genetic algorithms},
	url = {http://dx.doi.org/10.1109/TASC.2012.2234187 },
	author = {J M Pina and P Pereira and Valadas, D. and Ceballos, J.M. and A Alvarez}
}
