Submarine landslides:

A grain-scale perspective


Krishna Kumar, kks32@cam.ac.uk
University of Cambridge




King's College, Cambridge
8 March 2017.

Cambridge-Berkeley Computational Geomechanics

  • Lattice-Boltzmann + Discrete Element Method
  • Finite Element Method - Thermo-Hydro Mechanical Coupling
  • Material Point Method
  • Lattice Element Method
View the CB-Geo website for more information and software tools

Material Point Method

Lattice Element Method

Why soil behaviour is complex?

Supporting King's


Soil flow

Sand grains

Sand castle

Global landslide hazard

Fatalities due to landslides, 2007 - 2013 (Source: Nasa, 2015).

Debris flows and dry avalanches

Debris flow at Colorado (USGS,1997)

"Granular materials are ubiquitous in nature and are the second-most manipulated material in industry (the first one is water)"

- Patrick Richard (2005) in Nature

Aerial landslides

Afghanistan landslide - 2014 (Source: Boston Globe, 2014).

Oso landslide (2014)

River Cam bank collapse

The River Cam attempting to create a diversion


Meanwhile college staff were quick to put up an emergency barrier, to prevent any absent-minded professors accidentally taking an unexpected dip - Cambridge News

Submarine landslides

Mechanism of submarine landslides

Modelling Test at 1g Condition

  • Material type influences the mode of the flow.
  • Target: Clay‐rich flow (Less diffusive, Hydroplaning).

Effect of water entrainment on run-out

Possible boundary conditions of submarine run‐out

  • Presence of ambient water (larger drag force & less gravity).
  • Water entrainment.
  • Pore pressure does not dissipate.

Multiscale modelling in geomechanics

Discrete Element Method

Solve the Newton's equation of motion and the angular momentum (including rotational resistance)

$$F_n =m \times a $$

LBM - DEM simulation of granular collapse in fluid




aspect ratio 'a' of 6

Lattice Boltzmann - MRT

Real Fluid vs LBM Idealisation
LBM D2Q9 Model

Streaming
Collision

LBM-DEM fluid-solid coupling


Bounceback rule

LBM laminar & turbulent flows


Karman Vortex Street

Collapse in fluid

Collapse in fluid ('a'=0.8)

Granular collapse in fluid: Effect of aspect ratio



aspect ratio 'a' of 0.4

aspect ratio 'a' of 4

Collapse in fluid: Runout evolution

a = 0.4
a = 4

Critical time $\tau_c=\sqrt{H/g}$ (Staron and Hinch, 2005)
where, H = Height of the granular pile.

LBM - DEM simulation of granular collapse in fluid




aspect ratio 'a' of 8

Runout: dry vs. fluid

Collapse in fluid: Effect of permeability


Reduction ‘r’=0.7R

Reduction ‘r’=0.9R

Collapse in fluid: Effect of permeability

Effect of permeability: effective stress

Runout: effect of permeability

aspect ratio 0.8

Effect of permeability: runout

Effect of permeability: kinetic energy

Effect of permeability: runout

Collapse on an inclined plane




aspect ratio 'a' of 6 on a slope of 5*

CPU v GPU

GPU programming

LBM - DEM a = 0.8 & 10,000 partilces



  • LBM Nodes = 50 Million : DEM grains = 10000 discs
  • Real-time = 2 seconds
  • Run-time = 4 hours
  • Speedup = 25x on a Tesla K20

2D to 3D

LBM multi-component multi-phase


Thank you!


Krishna Kumar, kks32@cam.ac.uk

www.cb-geo.com