Hilbert Proper Orthogonal Decomposition: a tool for educing advective wavepackets from flow field data
Marco Raiola, Jochen Kriegseis

TL;DR
This paper introduces the Hilbert proper orthogonal decomposition (HPOD), a novel method for extracting advective wavepackets from flow data, applicable to both temporal and spatial analyses, especially in under-resolved datasets.
Contribution
The work presents a new complex-valued extension of POD using the Hilbert transform, including a space-only version, validated on diverse flow datasets, enhancing wavepacket analysis in advective flows.
Findings
HPOD effectively extracts wavepackets with amplitude and frequency modulation.
The space-only HPOD is mathematically equivalent to the conventional version.
Both methods perform well on complex turbulent flow datasets.
Abstract
Travelling wavepackets are key coherent features contributing to the dynamics of several advective flows. This work introduces the Hilbert proper orthogonal decomposition (HPOD) to distil these features from flow field data, leveraging their mathematical representation as modulated travelling waves. The HPOD is a complex-valued extension of the proper orthogonal decomposition, where the Hilbert transform of the dataset is used to compute its analytic signal. Two versions of the technique are explored and compared: the conventional HPOD, computing the analytic signal in time; a novel space-only HPOD, computing it along the advection direction. The HPOD is shown to extract wavepackets with amplitude and frequency modulation in time and space. Its broadband nature offers an alternative to spectrally-pure decompositions when instantaneous, local wave characteristics are important. The…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
