77 research outputs found
Vortices and dynamics in trapped Bose-Einstein condensates
I review the basic physics of ultracold dilute trapped atomic gases, with
emphasis on Bose-Einstein condensation and quantized vortices. The hydrodynamic
form of the Gross-Pitaevskii equation (a nonlinear Schr{\"o}dinger equation)
illuminates the role of the density and the quantum-mechanical phase. One
unique feature of these experimental systems is the opportunity to study the
dynamics of vortices in real time, in contrast to typical experiments on
superfluid He. I discuss three specific examples (precession of single
vortices, motion of vortex dipoles, and Tkachenko oscillations of a vortex
array). Other unusual features include the study of quantum turbulence and the
behavior for rapid rotation, when the vortices form dense regular arrays.
Ultimately, the system is predicted to make a quantum phase transition to
various highly correlated many-body states (analogous to bosonic quantum Hall
states) that are not superfluid and do not have condensate wave functions. At
present, this transition remains elusive. Conceivably, laser-induced synthetic
vector potentials can serve to reach this intriguing phase transition.Comment: Accepted for publication in Journal of Low Temperature Physics,
conference proceedings: Symposia on Superfluids under Rotation (Lammi,
Finland, April 2010
Effect of sediment on the bioaccumulation of a complex mixture of polychlorinated dibenzo-p-dioxins (PCDDs) and polychlorinated dibenzofurans (PCDFs) by fish
Stabilization and pumping of giant vortices in dilute Bose-Einstein condensates
Recently, it was shown that giant vortices with arbitrarily large quantum
numbers can possibly be created in dilute Bose-Einstein condensates by
cyclically pumping vorticity into the condensate. However, multiply quantized
vortices are typically dynamically unstable in harmonically trapped nonrotated
condensates, which poses a serious challenge to the vortex pump procedure. In
this theoretical study, we investigate how the giant vortices can be stabilized
by the application of a Gaussian potential peak along the vortex core. We find
that achieving dynamical stability is feasible up to high quantum numbers. To
demonstrate the efficiency of the stabilization method, we simulate the
adiabatic creation of an unsplit 20-quantum vortex with the vortex pump.Comment: 8 pages, 6 figures; to be published in J. Low Temp. Phys., online
publication available at http://dx.doi.org/10.1007/s10909-010-0216-
EMP control and characterization on high-power laser systems
Giant electromagnetic pulses (EMP) generated during the interaction of high-power lasers with solid targets can seriously degrade electrical measurements and equipment. EMP emission is caused by the acceleration of hot electrons inside the target, which produce radiation across a wide band from DC to terahertz frequencies. Improved understanding and control of EMP is vital as we enter a new era of high repetition rate, high intensity lasers (e.g. the Extreme Light Infrastructure). We present recent data from the VULCAN laser facility that demonstrates how EMP can be readily and effectively reduced. Characterization of the EMP was achieved using B-dot and D-dot probes that took measurements for a range of different target and laser parameters. We demonstrate that target stalk geometry, material composition, geodesic path length and foil surface area can all play a significant role in the reduction of EMP. A combination of electromagnetic wave and 3D particle-in-cell simulations is used to inform our conclusions about the effects of stalk geometry on EMP, providing an opportunity for comparison with existing charge separation models
Measuring the Intangible Aspects of the Manufacturing Strategy – A Case Study from the Automotive Industry
Functional and genetic analysis of regulatory regions of coliphage H-19B: location of shiga-like toxin and lysis genes suggest a role for phage functions in toxin release
Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/74784/1/j.1365-2958.1998.00890.x.pd
Modelling commodity value at risk with Psi Sigma neural networks using open–high–low–close data
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