Forecasting the evolution of three-dimensional turbulent recirculating flows from sparse sensor data

TL;DR

This paper introduces a scalable, data-driven forecasting algorithm for three-dimensional turbulent flows using sparse sensor data, combining Koopman theory, time-delay embedding, and linear estimation to predict flow evolution over large time windows.

Contribution

The paper presents a novel, scalable method that accurately forecasts turbulent flow evolution from sparse data, integrating Koopman theory with dimensionality reduction and system closure techniques.

Findings

Successfully applied to turbulent flow over a cube with over 10^8 degrees of freedom.

Accurately forecasts flow structures over time windows much larger than the flow's Lyapunov time.

Maintains high accuracy with increasing forecast window size.

Abstract

A data-driven algorithm is proposed that employs sparse data from velocity and/or scalar sensors to forecast the future evolution of three dimensional turbulent flows. The algorithm combines time-delayed embedding together with Koopman theory and linear optimal estimation theory. It consists of 3 steps; dimensionality reduction (currently POD), construction of a linear dynamical system for current and future POD coefficients and system closure using sparse sensor measurements. In essence, the algorithm establishes a mapping from current sparse data to the future state of the dominant structures of the flow over a specified time window. The method is scalable (i.e.\ applicable to very large systems), physically interpretable, and provides sequential forecasting on a sliding time window of prespecified length. It is applied to the turbulent recirculating flow over a surface-mounted cube (with more than $10^8$ degrees of freedom) and is able to forecast accurately the future evolution of the most dominant structures over a time window at least two orders of magnitude larger that the (estimated) Lyapunov time scale of the flow. Most importantly, increasing the size of the forecasting window only slightly reduces the accuracy of the estimated future states. Extensions of the method to include convolutional neural networks for more efficient dimensionality reduction and moving sensors are also discussed.