Statistical Treatment, Fourier and Modal Decomposition

TL;DR

This paper provides a tutorial on processing image velocimetry data, covering statistical, Fourier, and modal analysis techniques, including advanced topics like multiscale decompositions and nonlinear reduction.

Contribution

It offers a practical, tutorial-style overview of data processing methods in fluid dynamics, with accessible code and coverage of both basic and advanced modal analysis techniques.

Findings

Introduces a comprehensive tutorial on data processing in particle image velocimetry.

Includes practical code implementations for various analysis methods.

Covers advanced topics like multiscale modal decompositions and nonlinear dimensionality reduction.

Abstract

These are lecture notes for the lecture "Statistical Treatment, Fourier and Modal Decompositions", given at the VKI Lecture series "Fundamentals and Recent Advances in Particle Image Velocimetry and Lagrangian Particle Tracking". The course was held at the von Karman Institute for fluid dynamics from 15 November to 18 November 2021. This lecture provides a guided tour through the processing of data acquired via image velocimetry. Far from being an exhaustive account of the field, which would require an entire course on its own, the scope is to provide a hands-on tutorial. This begins with basic statistical treatment, briefly reviews frequency and modal analysis, and conclude with more advanced research topics such as multiscale modal decompositions and nonlinear dimensionality reduction. The material covered should hopefully propel newcomers into the subject while remaining of interest to experienced practitioners. All the codes related to this lecture are made available on a github repository.