A high-order fully Lagrangian particle level-set method for dynamic surfaces

TL;DR

This paper introduces a high-order Lagrangian particle level-set method that performs accurate surface redistancing directly on irregular particles using polynomial regression, improving robustness and accuracy without mesh reliance.

Contribution

It develops a novel high-order polynomial regression approach for particle-based level-set redistancing, eliminating the need for mesh-based interpolation and enhancing robustness.

Findings

Achieves high-order accuracy in 2D and 3D geometries.

Demonstrates robustness against particle distortion.

Shows convergence in benchmark tests and applies to droplet simulation.

Abstract

We present a fully Lagrangian particle level-set method based on high-order polynomial regression. This enables closest-point redistancing without requiring a regular Cartesian mesh, relaxing the need for particle-mesh interpolation. Instead, we perform level-set redistancing directly on irregularly distributed particles by polynomial regression in a Newton-Lagrange basis on a set of unisolvent nodes. We demonstrate that the resulting particle closest-point (PCP) redistancing achieves high-order accuracy for 2D and 3D geometries discretized on highly irregular particle distributions and has better robustness against particle distortion than regression in a monomial basis. Further, we show convergence in a classic level-set benchmark case involving ill-conditioned particle distributions, and we present an application to an oscillating droplet simulation in multi-phase flow.