The ability to detect and follow time-varying features in data obtained from numerical simulations enables characterisation and analysis of modelled physical phenomena. Feature-based flow visualisation shows only fragments of the results that are considered significant based on the application and the research problem; some examples of such patterns include vortices, shocks, eddies, critical points, etc. The saving in storage space with feature extraction can be significant and allows for data analysis and on-the-fly statistics at large scale. Additionally, as the scientific datasets often suffer from numerical noise, filtering techniques need to be employed before feature-based analysis can be performed.
Our work focuses on developing methodologies that allow important structures in numerical results to be extracted and studied with negligible impact on the overall simulation run-time.
The images on the right show the result of applying our proposed level-dependent WienerChop filter to data corrupted with coloured noise. We obtain a substantial computational saving compared to classical approaches.