VortexFitting: A post-processing fluid mechanics tool for vortex identification

Abstract

VortexFitting is a fluid mechanics post-processing tool developed in Python. It aims to detect the presence of vortices in a flow and evaluate their properties. Data obtained from both numerical simulations and experimental flow imaging techniques can be used as inputs. The software supports a number of input file formats such as NetCDF, HD5, TecPlot, and raw text files. The first stage of the vortex search procedure, which is identification of vortex candidates, is accomplished using a set of detection methods: swirling strength, Q criterion, and Δ criterion. The candidate vortices are then fitted to a Lamb–Oseen vortex model using a non-linear least-squares method, and the correlation between the model and the original velocity field is evaluated. If the correlation is deemed high enough, based on a user defined threshold, the vortex is accepted, and properties such as vortex radius and circulation, and vortex center are obtained. Each vortex can be tracked in a transient flow, and its trajectory is reconstructed with its decay characteristics. Two applications are presented in this paper: (i) an experimental columnar vortex moving through a free-surface water channel, and (ii) a numerical simulation of a bubbling fluidized bed. We demonstrate that VortexFitting can successfully identify the presence of vortices and characterize their features in both applications.

Keywords

• Vortex
• Detection methods
• Fluid dynamics
• Python