A Novel Concentration Gradient Method (CGM) for Modeling Sediment Suspension in Earthquake-Induced Submarine Landslides: Enhancing Predictive Accuracy in a 3D CFD Framework

 

Predicting the transformation of earthquake-induced submarine landslides into dilute turbidity currents is critical for geohazard assessment, yet capturing the sediment suspension at the landslide interface remains a significant numerical challenge. This study presents a major methodological innovation by developing the Concentration Gradient Method (CGM) and integrating it into the SPLASH3D framework. The primary goal is to resolve the dynamic sediment remobilization triggered by seismic events with high fidelity, moving beyond the limitations of traditional rigid-interface models.

The numerical framework solves the three-dimensional incompressible Navier-Stokes equations combined with the Volume of Fluid (VOF) method for interface tracking. While the solver utilizes a standard Two-Step Projection Method, the originality of this research lies in the introduction of the CGM and the Discontinuous Bi-viscosity Model (DBM) during the predictor step. By implementing the newly developed CGM, we effectively bridge the landslide mass and the ambient fluid, transforming the traditionally rigid sediment-water boundary into a dynamic, concentration-dependent layer. This allows for the precise tracking of sediment particle suspension and settling mechanisms, which are governed by local concentration gradients.

Numerical results demonstrate that the diffusion coefficient (D) in the CGM is the governing parameter for the evolution of turbidity currents. We found that higher diffusion rates significantly increase the volume of suspended sediment and accelerate the flow front through enhanced momentum exchange at the interface. Furthermore, the model successfully captures complex turbulent structures and spiral-like diffusion patterns—physical features that are unresolvable in conventional single-phase or rigid-body models. This advanced simulation approach significantly improves the accuracy of modeling landslide propagation and deposition, providing robust numerical support for risk-informed design of offshore infrastructure and the quantitative reconstruction of historical seismic events.