箱势阱中量子气的湍流特性.

报告人
Dr.Jinyi Zhang
单位
剑桥大学
时间
2018-12-12 (周三) 10:00
地点
上海研究院4号楼331会议室
摘要

Title:Turbulent features of quantum gas in a box trap

Abstract:
Compared to classical fluids, superfluids present fascinating peculiarities such as irrotational and frictionless flow, which raises fundamental questions about the character of turbulent cascades. Numerous experiments on quantum-fluid turbulence have been performed with liquid helium, exploring both vortex and wave turbulence. However, cold atom systems only have a few experiments on vortex turbulence, wave turbulence has never been studied. Recently, ultracold atomic gases have emerged as a novel platform, box trap for studies of turbulence, which offers new experimental possibilities.

In this talk, I first give a introduction for wave turbulence and our previous study on it, followed by three new works. First, synthetic dissipation and cascade fluxes. The adjustable trap depth provides a high-momentum cutoff k_D, which realises a synthetic dissipation scale. This gives us direct access to the particle flux across a momentum shell of radius k_D, and the tunability of k_D allows for a clear demonstration of the zeroth law of turbulence.
Second, decay of axial mode in a box trap. We study the lowest-lying excitation in a box trap. Using modulation spectroscopy, we measure the amplitude and phase response of this mode in the frequency domain, and observe that its width is strongly affected by the drive strength, while the resonant frequency remains almost unchanged. We develop a generic theoretical model that explains this fundamental nonlinear damping as a process whereby two elementary phonons couple to a higher-energy mode which subsequently decays in a continuum.
Third, relaxation after turbulence. In this ongoing project, we study the phase ordering kinetic after turbulence. We measure two point g1 function to characterise the correlation length, and find the universal power-law scaling of the growth of correlation length, obtain the exponent is 0.8. This behaviour may be understood by inverse particle cascade.