Iterative Volume-of-Fluid interface positioning in general polyhedrons with Consecutive Cubic Spline interpolation

TL;DR

This paper introduces a fast, efficient, and easy-to-implement iterative algorithm for accurately positioning fluid interfaces in complex geometries within the Volume-of-Fluid method, improving upon existing approaches.

Contribution

The paper presents a novel Consecutive Cubic Spline (CCS) iterative algorithm that enhances interface positioning accuracy and efficiency without relying on complex geometrical parametrizations.

Findings

Requires only two iterations on average for high precision

Handles challenging volume fractions near 10^{-9}

Comparable efficiency to the fastest existing methods

Abstract

A straightforward and computationally efficient Consecutive Cubic Spline (CCS) iterative algorithm is proposed for positioning the planar interface of the unstructured geometrical Volume-of-Fluid method in arbitrarily-shaped cells. The CCS algorithm is a two-point root-finding algorithm specifically designed for the VOF interface positioning problem, where the volume fraction function has diminishing derivatives at the ends of the search interval. As a two-point iterative algorithm, CCS re-uses function values and derivatives from previous iterations and does not rely on interval bracketing. The CCS algorithm only requires only two iterations on average to position the interface with a tolerance of $10^{-12}$, even with numerically very challenging volume fraction values, e.g. near $10^{-9}$ or $1-10^{-9}$. The proposed CCS algorithm is very straightforward to implement because its input is already calculated by every geometrical VOF method. It builds upon and significantly improves the predictive Newton method and is independent of the cell's geometrical model and related intersection algorithm. Geometrical parametrizations of truncated volumes used by other contemporary methods are completely avoided. The computational efficiency is comparable in terms of the number of iterations to the fastest methods reported so far. References are provided in the results section to the open-source implementation of the CCS algorithm and the performance measurement data.