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Journal of Flow Visualization and Image Processing
SJR: 0.161 SNIP: 0.312 CiteScore™: 0.1

ISSN Imprimir: 1065-3090
ISSN En Línea: 1940-4336

Journal of Flow Visualization and Image Processing

DOI: 10.1615/JFlowVisImageProc.v12.i4.30
pages 363-376

VISUALIZATION OF THE RAYLEIGH−TAYLOR INSTABILITY

Wayne Kraft
Department of Mechanical Engineering, Texas A&M University, College Station, Texas 77843, USA
Malcolm J. Andrews
Department of Mechanical Engineering, Texas A&M University, College Station, Texas 77843
Praveen Ramaprabhu
Center for Non-Linear Studies, Los Alamos National Laboratory, Los Alamos, New Mexico 87545, USA
Dale Snider
Arena-flow, LLC, 10701 Montgomery Blvd. NE Suite C, Albuquerque, New Mexico 87111, USA

SINOPSIS

Buoyancy-driven turbulence resulting from the Rayleigh−Taylor instability occurs in an unstably stratified flow when a hearty fluid rests above a light fluid. Small perturbations at the interface between two fluids grow, developing into a turbulent mixing layer. The major difficulty in studying this phenomenon is creating the initial interface between the two fluids in the presence of an unstable density gradient. As a result, many creative approaches have been used to study buoyancy-driven turbulence and the Rayleigh−Taylor instability. The current experiment utilizes a water channel facility in the same manner as the study of shear mixing layers. Over the past several years, we have used various visualization techniques to observe the development and behavior of the Rayleigh−Taylor instability in our water channel. These techniques included fluid marking by means of Nigrosene dye, PIV-S, and finally PLIF. Each technique has unique benefits for viewing the buoyancy-driven mixing layer. Used together these techniques help provide an understanding of the nature and complexity of buoyancy-driven turbulence. This paper presents a collection of images from our water channel facility to serve as a visual record of our work over the past ten years to study the Rayleigh-Taylor instability in our statistically steady experiment.


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