# Robust Principal Component Analysis for Modal Decomposition of Corrupt Fluid Flows

## TL;DR

This paper demonstrates that robust principal component analysis (RPCA) effectively cleans corrupted fluid flow data, preserving coherent structures and improving modal decomposition accuracy across simulations and experiments.

## Contribution

It introduces the application of RPCA for robust modal analysis in fluid flows, handling corrupted data to enhance flow pattern identification.

## Key findings

- RPCA accurately recovers low-rank flow structures.
- RPCA effectively identifies and fills missing or incorrect data points.
- RPCA improves the robustness of dynamic mode decomposition in noisy conditions.

## Abstract

Modal analysis techniques are used to identify patterns and develop reduced-order models in a variety of fluid applications. However, experimentally acquired flow fields may be corrupted with incorrect and missing entries, which may degrade modal decomposition. Here we use robust principal component analysis (RPCA) to improve the quality of flow field data by leveraging global coherent structures to identify and replace spurious data points. RPCA is a robust variant of principal component analysis (PCA), also known as proper orthogonal decomposition (POD) in fluids, that decomposes a data matrix into the sum of a low-rank matrix containing coherent structures and a sparse matrix of outliers and corrupt entries. We apply RPCA filtering to a range of fluid simulations and experiments of varying complexities and assess the accuracy of low-rank structure recovery. First, we analyze direct numerical simulations of flow past a circular cylinder at Reynolds number 100 with artificial outliers, alongside similar PIV measurements at Reynolds number 413. Next, we apply RPCA filtering to a turbulent channel flow simulation from the Johns Hopkins Turbulence database, demonstrating that dominant coherent structures are preserved in the low-rank matrix. Finally, we investigate PIV measurements behind a two-bladed cross-flow turbine that exhibits both broadband and coherent phenomena. In all cases, we find that RPCA filtering extracts dominant coherent structures and identifies and fills in incorrect or missing measurements. The performance is particularly striking when flow fields are analyzed using dynamic mode decomposition, which is sensitive to noise and outliers.