In this work, we present a comprehensive framework for the generation and efficient management of particles in both fully three-dimensional (3D) and depth-averaged Material Point Method (DAMPM) simulations. Our approach leverages TIFF image data to construct initial conditions for large-scale geophysical flows, with a primary focus on landslide modeling. We describe the algorithms developed for particle initialization, distribution, and tracking, ensuring consistency and computational efficiency across different MPM formulations. The proposed methods enable accurate representation of complex topographies while maintaining numerical stability and adaptability to diverse material behaviors. Although the primary application is landslide simulation, the methodologies outlined are broadly applicable to other fields involving granular flows, fluid–structure interactions, and large-deformation processes. Performance evaluations demonstrate the efficiency and robustness of our approach, highlighting its potential for advancing high-fidelity simulations in geomechanics and beyond.
Efficient particle generation for depth-averaged and fully 3D MPM using TIFF image data
de Falco, Carlo;Formaggia, Luca
2026-01-01
Abstract
In this work, we present a comprehensive framework for the generation and efficient management of particles in both fully three-dimensional (3D) and depth-averaged Material Point Method (DAMPM) simulations. Our approach leverages TIFF image data to construct initial conditions for large-scale geophysical flows, with a primary focus on landslide modeling. We describe the algorithms developed for particle initialization, distribution, and tracking, ensuring consistency and computational efficiency across different MPM formulations. The proposed methods enable accurate representation of complex topographies while maintaining numerical stability and adaptability to diverse material behaviors. Although the primary application is landslide simulation, the methodologies outlined are broadly applicable to other fields involving granular flows, fluid–structure interactions, and large-deformation processes. Performance evaluations demonstrate the efficiency and robustness of our approach, highlighting its potential for advancing high-fidelity simulations in geomechanics and beyond.| File | Dimensione | Formato | |
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