# Searching turbulence for periodic orbits with dynamic mode decomposition

## TL;DR

This paper introduces a novel method using dynamic mode decomposition to efficiently identify unstable periodic orbits in turbulent flows, surpassing traditional recurrence-based techniques in accuracy and robustness.

## Contribution

The authors develop a DMD-based approach to generate initial guesses for UPOs directly from turbulent data, avoiding the need for near recurrences and improving orbit detection.

## Key findings

- Successfully extracts UPO periods from short data segments.
- Finds more converged periodic orbits than recurrent flow analysis.
- Identifies UPOs where traditional methods fail to converge.

## Abstract

We present a new method for generating robust guesses for unstable periodic orbits (UPOs) by post-processing turbulent data using dynamic mode decomposition (DMD). The approach relies on the identification of near-neutral, repeated harmonics in the DMD eigenvalue spectrum from which both an estimate for the period of a nearby UPO and a guess for the velocity field can be constructed. In this way, the signature of a UPO can be identified in a short time series without the need for a near recurrence to occur, which is a considerable drawback to recurrent flow analysis, the current state-of-the-art. We first demonstrate the method by applying it to a known (simple) UPO and find that the period can be reliably extracted even for time windows of length one quarter of the full period. We then turn to a long turbulent trajectory, sliding an observation window through the time series and performing many DMD computations. Our approach yields many more converged periodic orbits (including multiple new solutions) than a standard recurrent flow analysis of the same data. Furthermore, it also yields converged UPOs at points where the recurrent flow analysis flagged a near recurrence but the Newton solver did not converge, suggesting that the new approach can be used alongside the old to generate improved initial guesses. Finally, we discuss some heuristics on what constitutes a "good" time window for the DMD to identify a UPO.