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Outline

Drucker-prager elastoplasticity for sand animation

2016, ACM Transactions on Graphics

https://doi.org/10.1145/2897824.2925906

Abstract

We simulate sand dynamics using an elastoplastic, continuum assumption. We demonstrate that the Drucker-Prager plastic flow model combined with a Hencky-strain-based hyperelasticity accurately recreates a wide range of visual sand phenomena with moderate computational expense. We use the Material Point Method (MPM) to discretize the governing equations for its natural treatment of contact, topological change and history dependent constitutive relations. The Drucker-Prager model naturally represents the frictional relation between shear and normal stresses through a yield stress criterion. We develop a stress projection algorithm used for enforcing this condition with a non-associative flow rule that works naturally with both implicit and explicit time integration. We demonstrate the efficacy of our approach on examples undergoing large deformation, collisions and topological changes necessary for producing modern visual effects.

FAQs

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AI

What parameters significantly affect the visual simulation of sand behavior?add

The study shows different friction angles directly impact sand pile stability, with optimal Young's modulus being 3.537 × 10^7 for realistic visuals.

How does the Material Point Method compare to traditional numerical methods?add

MPM offers enhanced stability and less numerical dissipation than FLIP methods, preventing artifacts like unstable ringing during simulations.

What is the role of the Drucker-Prager model in this research?add

The Drucker-Prager model effectively captures the frictional behaviors of sand as a continuum, facilitating efficient simulation of complex interactions.

How are particle grid transfers handled in this elastoplasticity model?add

The paper employs cubic B-splines for APIC transfers, enhancing stability and addressing the challenges of handling topology changes.

What are the computational challenges faced in simulating granular materials?add

Simulating large numbers of grains requires balancing accuracy and efficiency, often leading to extensive computational demands in traditional models.

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