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A methodology to predict the run-out distance of submarine landslides.

Authors :
Guo, Xingsen
Stoesser, Thorsten
Zheng, Defeng
Luo, Qianyu
Liu, Xiaolei
Nian, Tingkai
Source :
Computers & Geotechnics. Jan2023, Vol. 153, pN.PAG-N.PAG. 1p.
Publication Year :
2023

Abstract

In this paper, the method of computational fluid dynamics (CFD) is proposed to simulate a fluidized submarine landslide with shear thinning non-Newtonian fluids over a seabed under different contact conditions in the ambient water. The CFD method is first validated using data of physical channel and non-Boussinesq lock-exchange experiments with different rheological characteristics and is found to provide good accuracy. Various CFD-based experiments with different initial velocities of the submarine landslide and different seabed contact conditions are then performed systematically. During the movement of the submarine landslide in ambient water, stress state and causes of the submarine landslide mass are revealed, and the submarine landslide-seabed contact relation is clarified as a decisive influencing factor. Furthermore, based on the principle of energy conservation, a methodology to predict the run-out distance of the submarine landslide mass by using the initial geometry of the submarine landslide masses with different scales and the total initial kinetic energy (including the initial kinetic energy and the gravitational potential energy difference caused by the self-slumping) of the submarine landslide mass is presented, equations to quantify the process are established, and the proposed methodology and equations are validated by the numerical result, which provides a significant basis for the prediction of marine engineering geological and hydrodynamic hazards with special reference to submarine landslides. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
0266352X
Volume :
153
Database :
Academic Search Index
Journal :
Computers & Geotechnics
Publication Type :
Academic Journal
Accession number :
160584879
Full Text :
https://doi.org/10.1016/j.compgeo.2022.105073