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Know your clusters to predict disasters -- how hazard exacerbating particle-gas feedback evolves inside pyroclastic density currents

Ermanno Brosch1, Gert Lube 1, Eric Breard2, Eckart Meiburg3

  • Affiliations: 1 Volcanic Risk Solutions, Massey University, Palmerston North, New Zealand. 2 School of Geosciences, The University of Edinburgh, Edinburgh, UK. 3 Department of Mechanical Engineering, University of California at Santa Barbara, Santa Barbara, CA, USA.

  • Presentation type: Poster

  • Presentation time: Friday 16:30 - 18:00, Room Poster Hall

  • Poster Board Number: 187

  • Programme No: 3.9.19

  • Theme 3 > Session 9

Abstract

Accurate predictions of the hazard intensity of PDCs necessitate models for the spatiotemporally evolving abundance of particles inside flows. The development of such models is challenging because the motion of particles inside PDCs is modified through long hypothesized but poorly understood feedback mechanisms between particles and gas. Particle-gas feedback occurs in PDCs because even the finest ash particles suspended in turbulence have significant inertia relative to the gas and cannot follow the rapid velocity fluctuations of the fluid. This condition leads to particle clustering, a reduction in particle-gas drag, and strongly enhanced sedimentation. The increased sedimentation should reduce flow-driving particle concentration, speed, and damage-causing dynamic pressure. Instead, recent measurements inside experimental PDCs demonstrated that the preferential clustering of particles at eddy margins causes one order of magnitude larger destructive power than predicted by conventional hazard models. Here we report measurements of the abundance and geometrical characteristics of particle clusters inside large-scale analogue PDCs of hot, polydisperse pyroclastic material and gas. The number density of clusters correlates non-linearly with the concentration of particles whose characteristic response time to fluid motion is similar to the characteristic fluid timescale. The characteristic length scales of clusters are characterized by turbulent spectra. Downstream, the mean length of clusters systematically decreases, while their thicknesses increase. Using energy spectra of flow-internal measurements of flow density, velocity and temperature, and tracking the particle-size distribution inside the evolving flows, we derive a model linking the geometric characteristics inside PDCs to the characteristic length- and timescales of coherent turbulence structures.