Date of Award
Doctor of Philosophy
Adrian Del Maestro, Josh Pierce, Anthony Mezzacappa, Ryan Glasby
Turbulence is ubiquitous in life —from biology to astrophysics. The best direct numeric simulations (DNS) have only been benchmarked against low resolution, time-averaged experimental configurations—partly because of limitations in computing power. With time, computing power has greatly increased, so there is need for higher quality data of turbulent flow. In this dissertation, we explore a solution that enables quantitative visualization measurement of the velocity field in liquid helium, which has the potential of breaking new ground for high Reynolds number turbulence research and model testing.
Our technique involves creation of clouds of molecular tracers using 3He-neutron absorption reaction in liquid helium and the tracking of tracer clouds using laser-induce fluorescence. The extremely small kinematic viscosity of liquid helium enables the generation of such high-Re flows in compact facilities for laboratory research, which would not otherwise be possible with conventional fluids. We applied machine learning to identify clusters of excimers and tracked their centroids as the excimers flowed in an empty channel, and a channel with two bluff objects. The technique also broadens the application of neutron beams.
Wen, Xin, "IMAGING NORMAL FLUID FLOW IN HE II WITH NEUTRONS AND LASERS — A NEW APPLICATION OF NEUTRON BEAMS FOR STUDIES OF TURBULENCE. " PhD diss., University of Tennessee, 2022.