A Robust Compressible APIC/FLIP Particle Grid Method with Conservative Resampling and Adaptive APIC/PIC Blending

TL;DR

This paper introduces a robust particle grid method for simulating compressible flows with shocks and vortices, addressing long-standing issues like particle depletion and quadrature degradation through conservative resampling and adaptive blending.

Contribution

It proposes novel sampling-aware controls, including conservative split resampling and a soft-switch for APIC blending, to improve stability and accuracy in challenging flow simulations.

Findings

Removes spike head voids in RTI simulations

Preserves vortex dynamics and roll-up

Matches reference Euler growth metrics

Abstract

Modeling inviscid compressible flows with shocks and vortex dominated dynamics remains challenging for particle grid methods due to moving discontinuities, cell crossing noise, and quadrature degradation under strong deformation. Building on a FLIP/APIC framework with vorticity aware tensor artificial viscosity, we identify a long time RTI failure mode: particle depletion at spike heads degrades quadrature and particle grid coupling, producing nonphysical, void-like dents. Standard mitigations (CPDI lite and subcell-jittered seeding) reduce but do not eliminate this artifact. We therefore add two sampling-aware controls: (i) conservative split resampling that replenishes depleted cells while exactly conserving mass, momentum, and internal energy; and (ii) a soft-switch that attenuates only the APIC affine term when local support is insufficient. Tests on the Sod shock tube and single/multi mode RTI show that the method removes spike head voids in long-time RTI while preserving vortex roll up, and matches reference Euler growth metrics