Project

Submarine landslides involve the movement of saturated sediments down a slope, interacting with seawater and/or offshore infrastructure. During landslides, the bulk of the sediment material (usually considered as a porous medium), transits from solid-like to fluid-like, i.e., from stagnant to continuously flowing. In addition, the coupling between seawater and sediment is crucial in the landslide dynamics. Recent studies have shown that the material point method (MPM) can describe the movement of saturated sediment and the hydrodynamic coupling between soil skeleton and seawater, within a multiple-phase framework. Nevertheless, to accurately predict the dynamics of and dissipation within the sliding masses, the transition between solid and flowing states of sediments must be incorporated.

The doctoral candidate will implement constitutive models for saturated sediments in fluid- and solid-like states into an existing GPU-MPM code. The exchange of momentum, mass, and energy between these admissible states of saturated sediments will be achieved over overlapping subdomains where the transition can potentially take place. To further improve the computational efficiency, the numerical models will be coupled to machine learning surrogates to enable large-scale industrial applications. The project aims to provide more accurate, highly efficient, and physics-based predictions for submarine landslides in order to quantitively assess the risk of damages to offshore infrastructures (e.g., foundations anchors) and induced disasters (e.g., tsunamis).