Stochastic Smoothed Particle Hydrodynamics for Stochastic Mechanics Problems

TL;DR

This paper introduces Stochastic SPH, a mesh-free method incorporating Polynomial Chaos and Karhunen-Loève expansions to efficiently quantify uncertainties in complex mechanics problems, validated against benchmarks.

Contribution

The paper presents a novel stochastic extension of SPH using polynomial chaos and ghost-particle techniques, enabling efficient uncertainty quantification in mesh-free mechanics simulations.

Findings

S-SPH accurately captures mean and variance of stochastic systems.

Achieves up to three orders of magnitude reduction in computational cost.

Validated on benchmark problems with excellent agreement to Monte Carlo results.

Abstract

Smoothed Particle Hydrodynamics (SPH_ is a mesh-free Lagrangian method renowned for modeling large deformations and free-surface flows, yet classical formulations remain confined to deterministic systems. We introduce Stochastic SPH (S-SPH), which employs orthogonal Polynomial Chaos expansions to represent uncertainties in system parameters, forcing functions, and boundary or initial conditions, while spatial variation is captured via the SPH kernel. Random fields are discretized through Karhunen-Lo\`eve expansions, and a Galerkin projection in the polynomial basis transforms the underlying SPDE into a coupled system of ordinary differential equations governing the time evolution of expansion coefficients. To enforce Dirichlet and Neumann conditions in a mesh-free context, ghost-particle techniques augmented by a gradient-correction matrix are employed, and a predictor-corrector integration scheme ensures numerical stability. We validate S-SPH on benchmark problems, including one-dimensional advection with stochastic advection speed, inviscid Burgers' equations with random initial amplitudes, and two-dimensional Burgers' flows with uncertain Fourier-mode initial fields and viscosity, demonstrating excellent agreement with Monte Carlo simulation statistics of mean and variance. Remarkably, S-SPH achieves up to three orders of magnitude reduction in computational cost relative to direct sampling approaches. The proposed framework thus provides an efficient, accurate, and fully mesh-free methodology for uncertainty quantification in complex mechanics applications.