THE EFFECT OF STOKES NUMBER AND POLYDISPERSITY ON PREFERENTIAL CONCENTRATION AND AGGREGATION IN A PARTICLE-LADEN JET FLOW

Md Miraz Hossain

doi:10.7273/000007385

Ash transport and deposition from volcanic eruptions pose significant hazards to human health, infrastructure, and aircraft. Volcanic ash transport and deposition (VATD) models are essential tools to predict volcanic ash transportation and deposition. Particle size distribution is an important factor in modeling ash fall. Small particles can travel far and remain in the air for weeks. Larger particles fall faster and closer to the vent. Ash aggregation plays an important role in the overall size distribution of the volcanic ash cloud. Small particles can aggregate into larger ones and significantly influence ash settling behavior. Many factors, such as liquid bonding, electrostatic attraction, and preferential concentration due to turbulence, can influence the ash aggregation process. Despite the importance of ash aggregation, many VATD models do not incorporate this process accurately. To better represent aggregation dynamics in VATD models, this study investigates particle preferential concentration and aggregation through controlled laboratory experiments in relevant conditions. We investigate particle aggregation in a particle laden jet flow, which is analogous to the near exit region of a volcanic eruption. We eject compressed air and feed particles in controlled mass loadings into the jet stream to create particle laden flow. The study tests three particle types: hollow glass spheres, solid nickel spheres, and volcanic ash from the May 18, 1980 Mount St. Helens eruption. These particles vary in size distribution, shape, density, and electrostatic properties. The difference in types of particles allow us to investigate into how particle properties and polydispersity influence aggregation dynamics. Key flow variables are the Reynolds number (Re ~ 5000 to 10000) and Stokes number (St ~ 0.6 to 9.4 based on convective scale). The particles in the flow are illuminated by a laser, and a series of images are captured using a high-speed camera. Velocity profiles, turbulence characteristics, and preferential concentration of the particles in the jet flow are measured using Particle Image Velocimetry (PIV) and MATLAB image processing analysis, while the particles aggregates are collected in microscope slides and visualized with 100x zoom using a microscope. Particle overlapping with one another in the microscope slides can be falsely taken as aggregation when analyzing the images. This study proposes a method to normalize the experimental images with randomly distributed particles images in MATLAB to address the particle overlapping. We observe that in dry conditions, test runs with single type of particles lead to minimal aggregation. However, even in dry conditions, the test runs yield enhanced aggregation when there are hollow glass and nickel particles together in the jet. The tests with volcanic ash also show minimal aggregation in dry conditions. However, the size distribution used in the tests have a wide range (1μm to 1000μm) that can only produce qualitative results. Preferential concentration, or “clustering”, refers to locally dense concentrated particles in the flow. Turbulence can lead to preferential concentration in a jet flow. This study utilizes legacy PIV data to analyze preferential concentration in different Stokes number, Reynolds number, mass loading and relative humidity. We observe much more particle clustering in the flow when the Stokes number is close to one. We also observe that the clustering pattern is very similar when analyzing with normalized. The study also suggests some future directions to investigate the mechanisms for enhanced aggregation with two particle types.

THE EFFECT OF STOKES NUMBER AND POLYDISPERSITY ON PREFERENTIAL CONCENTRATION AND AGGREGATION IN A PARTICLE-LADEN JET FLOW

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