Validation and Application of Sand Pile Modeling of Multiseeded HTS Bulk Superconductors

Abstract

Sand pile and Bean models have already been applied to describe single grain HTS bulks. An extension to that approach was used to model multiseed bulks, needed for several practical applications as electric motors or flywheels with superconducting bearings. The use of genetic algorithms was then proposed to determine intra-and intergrain current densities, and application to two and three seeds samples using trapped flux experimental measurements was exemplified. However, this model assumed some simplifications, as equal properties in grain boundaries between neighboring grains. In this paper an extension to this methodology is proposed and evaluated by analyzing measurements performed in plans at different distances from surfaces of samples with three seeds. Discussion of its influence on a practical application is also explored.

A reconstructed three-dimensional HTS bulk electromagnetic model considering J c spatial inhomogeneity and its implementation in a bulks’ combination system

“…The growth processes of the two seed domains are assumed to be independent without the mutual influence of the material fabrication. In fact, there is a weakly coupled boundary between them [55,56]. The size effect of the rectangular two-seeded bulk is focused on in this section.…”

Roadmap on artificial intelligence and big data techniques for superconductivity

“…This unknown can be determined from experiments by a simple mean squared error minimisation between the experimental trapped field surface and the one generated by the sandpile model.. Real-scale, high-power/field applications require increasingly larger bulks, where multiple grains improve the trapping flux ability due to the effects of inter and intragrain currents [32]. The sand-pile model was already applied to bulks with two and three grains (figure 5), where distinct current densities were inferred through genetic algorithms (GAs) [33], a metaheuristic inspired by natural evolution, widely used in diverse optimisation problems [34]. Yet, its application in this context requires a priori knowledge that is often unavailable or may be unfeasible in more complex bulks and new manufacturing concepts.…”

Evolution and Modern Approaches of the Power Transformer Cost Optimization Methods

“…The achievements of the last decades have unexpected twists in the direction of the transformer development. For example, there are comprehensive researches are in progress on the application of superconducting materials in power transformers [61][62][63][64][65][66][67][68][69].…”