Date of Award
Spring 5-2025
Document Type
Thesis (Master's)
Department or Program
Computer Science
First Advisor
Bo Zhu
Abstract
Accurate interface tracking is essential in simulating multiphase flows, material deformation, and other physical phenomena involving evolving geometries. In this thesis, we present an adaptive dual-particle representation for high-fidelity interface tracking. By combining two types of particles—feature particles and sample particles—we construct an implicit representation that captures fine geometric details. This representation is evolved explicitly using a velocity flow field, enabling accurate and adaptive tracking of dynamic interfaces. We further propose a particle level set method on the flow map, leveraging the state-of-the-art accuracy of particle-based advection. By integrating this approach with the particle level set framework, we achieve fourth-order accuracy in interface representation and advection. Our method tracks both gradients and Hessians of the level set, allowing it to resolve sub-cell features beyond the reach of traditional techniques. Extensive numerical experiments and comparisons demonstrate that our method consistently outperforms existing interface tracking approaches in terms of accuracy, volume preservation, and feature fidelity.
Recommended Citation
Zhang, Taiyuan, "PARTICLE-BASED HIGH-FIDELITY INTERFACE TRACKING ALGORITHMS" (2025). Dartmouth College Master’s Theses. 232.
https://digitalcommons.dartmouth.edu/masters_theses/232
