Towards real-time reconstruction of velocity fluctuations in turbulent channel flow

TL;DR

This paper presents a framework for real-time reconstruction of turbulent velocity fluctuations from limited sensor data, utilizing linear estimators and efficient computational techniques to enable fast, accurate streaming reconstructions in turbulent channel flow.

Contribution

The paper introduces a novel framework combining flow statistics, resolvent modes, and spectral methods for efficient real-time turbulent flow reconstruction from limited measurements.

Findings

Reconstruction captures large-scale dynamics with few measurements.

Efficient streaming updates enable near real-time performance.

Framework shows potential for experimental real-time turbulence monitoring.

Abstract

We develop a framework for efficient streaming reconstructions of turbulent velocity fluctuations from limited sensor measurements with the goal of enabling real-time applications. The reconstruction process is simplified by computing linear estimators using flow statistics from an initial training period and evaluating their performance during a subsequent testing period with data obtained from direct numerical simulation. We address cases where (i) no, (ii) limited, and (iii) full-field training data are available using estimators based on (i) resolvent modes, (ii) resolvent-based estimation, and (iii) spectral proper orthogonal decomposition modes. During training, we introduce blockwise inversion to accurately and efficiently compute the resolvent operator in an interpretable manner. During testing, we enable efficient streaming reconstructions by using a temporal sliding discrete Fourier transform to recursively update Fourier coefficients using incoming measurements. We use this framework to reconstruct with minimal time delay the turbulent velocity fluctuations in a minimal channel at ${\rm Re}_\tau \approx 186$ from sparse planar measurements. We evaluate reconstruction accuracy in the context of the extent of data required and thereby identify potential use cases for each estimator. The reconstructions capture large portions of the dynamics from relatively few measurement planes when the linear estimators are computed with sufficient fidelity. We also evaluate the efficiency of our reconstructions and show that the present framework has the potential to help enable real-time reconstructions of turbulent velocity fluctuations in an analogous experimental setting.