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Pinning time statistics for vortex lines in disordered environments.

Ulrich Dobramysl1, Michel Pleimling2, Uwe C Täuber2

  • 1Mathematical Institute, University of Oxford, Oxford OX2 6GG, United Kingdom.

Physical Review. E, Statistical, Nonlinear, and Soft Matter Physics
|January 24, 2015
PubMed
Summary
This summary is machine-generated.

We investigated magnetic vortex line pinning in superconductors using simulations. Both discrete and continuous disorder models show power-law pinning times, but differ in scaling and short-time dynamics.

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Area of Science:

  • Condensed Matter Physics
  • Superconductivity
  • Materials Science

Background:

  • Disordered type-II superconductors exhibit complex magnetic flux (vortex) line dynamics.
  • Understanding vortex pinning is crucial for superconductor applications.
  • Previous studies suggest extreme-event statistics may describe pinning behavior.

Purpose of the Study:

  • To investigate the pinning dynamics of magnetic flux lines in disordered type-II superconductors.
  • To compare the effects of different disorder models on vortex pinning time distributions.
  • To analyze the applicability of extreme-event statistics to vortex pinning.

Main Methods:

  • Numerical simulations using a directed elastic line model.
  • Extraction of pinning time distributions for vortex line segments.
  • Comparison of discrete (attractive/repulsive wells) and continuous (Gaussian landscapes) disorder models.

Main Results:

  • Both discrete and continuous disorder models yield power-law pinning time distributions.
  • Significant differences observed in effective scaling exponents between the two disorder models.
  • Distinct short-time behavior identified for discrete versus continuous disorder implementations.

Conclusions:

  • Extreme-event statistics successfully predict power-law behavior in vortex pinning for both disorder models.
  • The choice of disorder model significantly impacts the quantitative description of pinning dynamics.
  • Further research is needed to fully elucidate the short-time dynamics and scaling in these systems.