Geometric Reinitialization for Capillary Flows: a Comparative Study with State-of-the-Art Conservative Level-Set Methods

TL;DR

This paper introduces a new geometric reinitialization method for conservative level-set simulations of capillary flows, demonstrating improved robustness and comparable accuracy to PDE-based methods across complex 3D cases.

Contribution

It proposes a novel geometric reinitialization technique for CLS models and provides a comprehensive comparison with existing approaches in 3D capillary flow simulations.

Findings

The geometric method achieves high-quality, spatially-converged results.

It is more robust and easier to parameterize than PDE-based reinitialization.

Projection-based reinitialization fails in complex 3D interfacial dynamics.

Abstract

Simulations of immiscible flows involving surface tension (ST) require a robust high-fidelity framework. State-of-the-art multi-phase models, such as the Conservative Level-Set (CLS) approach, rely on Eulerian representations of the fluids and interface and require reinitialization methods to ensure volume conservation and accurate ST force modeling. This work focuses on the complete description of a CLS solver and proposes a novel geometric reinitialization method, based on the level-set literature. It includes a quantitative and objective comparison of this new geometric method to two reinitialization approaches: the PDE-based reinitialization proposed in the original CLS method and a simple projection-based approach. This comparison tackles three 3D application cases: the rise of a bubble, the capillary migration of a droplet, and the Rayleigh-Plateau instability development in a capillary jet. The PDE-based and geometric methods lead to high-quality, spatially-converged results in good agreement with benchmark and analytic solutions, while the projection-based reinitialization fails to capture complex 3D interfacial dynamics. The results also highlight the robustness of the novel geometric method which offers a two-parameter framework in comparison to the PDE-based method that necessitates a case-dependent selection of four parameters.