Evaluating Sampling Strategies for Visualizing Uncertain Multi-Phase Fluid Simulation Data

Abstract

Abstract. With Eulerian Method of Moment (MoM) solvers for the CFD simulation of multi-phase fluid flow, the positions of bubbles or droplets are not modeled explicitly, but through scalar fields of moments. These fields can be interpreted as probability density functions describing the distribution of bubble locations. To enable intuitive visualization that allows direct visual comparisons between simulation and physical experiment, explicit instances of the bubble distribution are required.In this work, we examine one sampling-based method for obtaining sets of bubble positions from density fields. Based on an example dataset, we study the influence of the main parameter, the kernel size, on the resulting bubble set. We identify a tradeoff between numerical accuracy and temporal continuity for the visualization.