Clustering the flow: a data-driven framework for pattern discovery in fluid dynamics

doi:10.1017/flo.2026.10066

Clustering the flow: a data-driven framework for pattern discovery in fluid dynamics

2026-09-30

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DOI：10.1017/flo.2026.10066

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Clustering techniques provide a powerful framework for analysing complex flow dynamics while reducing computational costs in large-scale simulations. In this work, we propose a clustering-based approach using vector quantisation principal component analysis (VQPCA) to identify structural sensitivity zones, i.e. regions where the flow is most receptive to perturbations. To the authors’ knowledge, this is the first application of VQPCA to a fluid dynamics problem for identifying flow patterns and dynamically relevant regions. As a fully data-driven technique, it does not rely on adjoint methods and depends exclusively on direct simulation data. This method extracts dominant flow features by partitioning the flow field into regions characterised by their intrinsic dynamics. Its validity is assessed by analysing the wake behind a circular cylinder, where the identified regions show strong similarity with previously established structural sensitivity zones. Robustness is further evaluated under different operating conditions for this configuration. The approach is then applied to the interaction of two planar synthetic jets to investigate more complex flow dynamics and illustrate how the identified regions can guide flow control strategies. These results highlight the potential of clustering-based methods as practical tools for analysing complex flows and identifying regions where small perturbations can strongly influence global dynamics.

Keywords:

instability control

low-dimensional models

nonlinear instability

phenomena in the physical world

wakes

Clustering the flow: a data-driven framework for pattern discovery in fluid dynamics

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