Image processing of axi-symmetric interferograms for flow field visualisation

Program Code: 
1661
Contact: 

A/Prof Harald Kleine (a.kleine@adfa.edu.au)

Description of Work: 

Background:

One of the ongoing projects in this group is the investigation of axisymmetric compressible flows that are established when a shock wave exits an open shock tube. This rather simple scenario generates a large number of secondary waves and flow features, and the details of this process are not fully clarified. We have conducted a number of studies to investigate this flow pattern, and one of the test campaigns has yielded a large number of time-resolved interferometric visualisation results such as the ones shown below. One important task that to-date has not been tackled is to evaluate the obtained results to extract information on the density distribution in this flow. This information is crucial for a comparison with other experimental data and the results of numerical simulation.

Objectives:

High-speed interferograms of an axi-symmetrical flow field are used to visualise its expansion. Without unwrapping the fringe patterns, the analysis remains mainly qualitative. However, a technique called the Abel transform, similar to tomography, can be used to unwrap the fringe patterns and obtain pressure gradients. The project September 2014 29 described here has the aim of developing a computational tool that can reconstruct an axisymmetric density distribution from interferograms of high-speed flows. These interferograms are provided as time-resolved records, which can then be used to determine how the density field develops in space and time.

Description of work:

It is expected that this project will deliver a fast and efficient algorithm that allows one to determine the density distribution in a transient, axisymmetric flow field recorded in sequences of line-of-sight interferometric visualisations. The principal techniques for image acquisition are well established and a large amount of expertise and highclass equipment for this purpose is available in the School. There already exists a large amount of image data that is suitable for the proposed analysis. Image registration algorithms that have been developed in the School for atmospheric imaging can be adapted and expanded to this application.