Correlating Entrainment Mechanisms and Turbulence in a Buoyant Plume to Large Scale Visual Structures

Motivated by dynamically rising volcanic ash plumes, we present an experimental study of positively buoyant plumes, in which we aim to measure turbulence-driven entrainment in relation to large-scale external plume features. Though it is difficult to make in situ measurements of a volcanic eruption in nature, video recordings in the field show incredible large-scale structures of plume evolution. Prior researchers have investigated discrepancies between results from modeling volcanic ash plumes as quasi-steady flows and actual plume behavior. To better understand impacts of velocity gradients on the plume structure and its transport, researchers have turned to methods that allow complete visualization of plume dynamics. Methods include the use of dyes (Chojnicki et al. 2015, Rocco & Woods 2015, Kitamura & Sumita 2011), particle image velocimetry (PIV - Clarke at al. 2009, Chojnicki et al. 2014), and laser induced fluorescence (LIF - Crimaldi 2008, Funatani et al. 2004), among others.

We have built an experimental facility that simulates the rise of ash clouds through the use of a buoyant water plume. Using time-lapsed photography, we examine the growth of the plume, with emphasis on the visible billows as they evolve in time and space. Specifically, we develop an algorithm to track features at the edge of the plume and perform statistical analysis of these geometric features as they depend upon plume density and outlet conditions. We also perform analysis of entrainment mechanisms via turbulence at the interface of the plume and ambient LIF and PIV measurements through the lateral center of the plume. The PIV and LIF measurements are then correlated to large-scale optical measurements of external features.

Ultimately, this work aims to increase the amount of information regarding physical transport mechanisms that may be derived from remote sensing measurements. This will allow us to connect field videos to detailed entrainment models and improve predictions of the fate and transport of the ash, as well as strengthen laboratory data collection methods that can be applied across a variety of applications in fluid mechanics.