Clustering the Flow: A Data-Driven Framework for Pattern Discovery in Fluid Dynamics

TL;DR

This paper introduces a novel, data-driven clustering approach using VQPCA to identify sensitive regions in fluid flows, demonstrated on cylinder wake and synthetic jets, offering low-cost analysis and potential for flow control.

Contribution

First application of VQPCA to fluid dynamics for identifying flow patterns and sensitive zones, providing a low-cost, data-driven alternative to adjoint methods.

Findings

Successfully identified structural sensitivity zones in cylinder wake.

Validated robustness across different flow conditions.

Provided insights for flow control strategies in synthetic jets.

Abstract

Clustering techniques offer a powerful framework for analyzing complex flow dynamics and reducing computational costs in large-scale simulations. In this work, we propose a novel clustering-based approach using Vector Quantization Principal Component Analysis (VQPCA) to identify structural sensitivity zones, namely the regions where the fluid flow is more receptive to changes. To the authors knowledge, this is the first application of VQPCA to a fluid dynamics problem for the identification of flow patterns and dynamically relevant regions. As a fully data-driven technique, it does not rely on adjoint methods; therefore, this approach has the advantage of having low computational cost, since it depends exclusively on data from the direct problem. The VQPCA technique demonstrates its ability to extract dominant flow features by clustering the flow field into regions characterized by their intrinsic dynamics. To assess the validity of this method, it is used to investigate the wake behind a circular cylinder, revealing similarities to previously established structural sensitivity regions. The robustness of the approach is further assessed through validation and calibration in different operating conditions in this flow scenario. As an extension of the analysis, we address the complex dynamics of two planar synthetic jets, where the clustering insights can lead to develop flow control strategies. These results highlight the potential of clustering-based methods as practical and effective tools to analyze and optimize fluid flows.