Dynamics of a Data-Driven Low-Dimensional Model of Turbulent Minimal Pipe Flow

TL;DR

This paper develops low-dimensional, data-driven models of turbulent pipe flow using neural networks, capturing essential dynamics with fewer than 20 degrees of freedom and discovering new exact coherent states.

Contribution

The study introduces a novel neural network framework combining autoencoders and neural ODEs to model turbulent flow dynamics with unprecedented low dimensionality and discovers new invariant solutions.

Findings

Models accurately track short-term trajectories for one Lyapunov time.

Reduced models capture Reynolds stresses and energy balance.

Discovered 17 new exact coherent states, including long-period orbits.

Abstract

The simulation of turbulent flow requires many degrees of freedom to resolve all the relevant times and length scales. However, due to the dissipative nature of the Navier-Stokes equations, the long-term dynamics are expected to lie on a finite-dimensional invariant manifold with fewer degrees of freedom. In this study, we build low-dimensional data-driven models of pressure-driven flow through a circular pipe. We impose the `shift-and-reflect' symmetry to study the system in a minimal computational cell (e.g., smallest domain size that sustains turbulence) at a Reynolds number of 2500. We build these models by using autoencoders to parametrize the manifold coordinates and neural ODEs to describe their time evolution. Direct numerical simulations (DNS) typically require on the order of O(105) degrees of freedom, while our data-driven framework enables the construction of models with fewer than 20 degrees of freedom. Remarkably, these reduced order models effectively capture crucial features of the flow, including the streak breakdown. In short-time tracking, these models accurately track the true trajectory for one Lyapunov time, while at long-times, they successfully capture key aspects of the dynamics such as Reynolds stresses and energy balance. Additionally, we report a library of exact coherent states (ECS) found in the DNS with the aid of these low-dimensional models. This approach leads to the discovery of seventeen previously unknown solutions within the turbulent pipe flow system, notably featuring relative periodic orbits characterized by the longest reported periods for such flow conditions.