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Frank Stefani

Researcher at Helmholtz-Zentrum Dresden-Rossendorf

Publications -  297
Citations -  5819

Frank Stefani is an academic researcher from Helmholtz-Zentrum Dresden-Rossendorf. The author has contributed to research in topics: Dynamo & Magnetic field. The author has an hindex of 40, co-authored 281 publications receiving 5246 citations.

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The azimuthal magnetorotational instability (AMRI)

TL;DR: In this article, it was shown that nonaxisymmetric perturbations are unstable if the rotation rate and the Alfven frequency of the field are of the same order almost independent of the magnetic Prandtl number Pm.
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Quasi-two-dimensional nonlinear evolution of helical magnetorotational instability in a magnetized Taylor-Couette flow

TL;DR: In this article, the authors investigated the nonlinear development and saturation properties of the helical magnetorotational instability (HMRI) in a magnetized Taylor-Couette flow at very low magnetic Prandtl number (correspondingly at low magnetic Reynolds number) relevant to liquid metals.
Patent

Verfahren und anordnung zur kontaktlosen bestimmung von räumlichen geschwindigkeitsverteilungen in elektrisch leitfähigen flüssigkeiten

TL;DR: In this article, a method and a system for determining the spatial speed distributions in electrically conductive fluids, which guarantee replicable results for all speed components and avoid all contact with the fluid, or with the walls encompassing the latter.
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Parametric instability in periodically perturbed dynamos

TL;DR: Giesecke et al. as mentioned in this paper examined kinematic dynamo action driven by an axisymmetric large-scale flow that is superimposed with an azimuthally propagating non-axisymmetric perturbation with a frequency ω.
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Long term time dependent frequency analysis of chaotic waves in the weakly magnetized spherical Couette system

TL;DR: In this paper, the authors investigated the long term behavior of chaotic flows by means of time dependent frequency analysis and showed that Laskar's frequency algorithm is sufficiently accurate to identify these strange attractors and thus is an efficient tool for classification of chaotic flow in high dimensional dynamical systems.