Numerical insights into rock–ice avalanche geophysical flow mobility through CFD–DEM simulation

Computational Mathematics; Fluid Flow and Transfer Processes; Modeling and Simulation; Numerical Analysis; Civil and Structural Engineering; Computational Mechanics

Abstract :

[en] Geophysical flows like rock–ice avalanches have high mobility and destructive potential, causing global loss of life and property. Water, often from melted ice, significantly impacts their mobility. Experimental investigations of debris friction in a rotating drum with melting ice show reduced friction due to water. However, experimental limitations hinder extensive testing. Employing a numerical model can overcome this, facilitating the study of various scenarios in understanding such calamitous geophysical flows. In the current work, we numerically replicate the rotating drum experiment using Eulerian–Lagrangian CFD–DEM coupling. We focus on the initial and final states, considering a 30% gravel and 70% ice mixture (B12-070 ). We don’t model the ice melting; rather, we inject equivalent water over time. Our simulation captures changes in the frictional behavior of the gravel bulk and flow height profile, closely aligning with experimental observations.

Research center :

LuXDEM - University of Luxembourg: Luxembourg XDEM Research Centre
ULHPC - University of Luxembourg: High Performance Computing

Disciplines :

Engineering, computing & technology: Multidisciplinary, general & others
Geological, petroleum & mining engineering
Computer science

Author, co-author :

ADHAV, Prasad ; University of Luxembourg > Faculty of Science, Technology and Medicine (FSTM) > Department of Engineering (DoE)

Feng, Zetao

Ni, Tao

PETERS, Bernhard ; University of Luxembourg > Faculty of Science, Technology and Medicine (FSTM) > Department of Engineering (DoE)

Fan, Xuanmei

External co-authors :

yes

Language :

English

Title :

Numerical insights into rock–ice avalanche geophysical flow mobility through CFD–DEM simulation

Publication date :

03 January 2024

Journal title :

Computational Particle Mechanics

ISSN :

2196-4378

eISSN :

2196-4386

Publisher :

Springer Science and Business Media LLC

Peer reviewed :

Peer reviewed

Focus Area :

Computational Sciences

Additional URL :

https://link.springer.com/content/pdf/10.1007/s40571-023-00699-3.pdf

Funders :

National Science Fund for Distinguished Young Scholars
Tencent
Natural Science Foundation of Sichuan Province
Science Fund for Distinguished Young Scholars of Sichuan Province
National Natural Science Foundation of China

Available on ORBilu :

since 04 January 2024

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