Active grid turbulence anomalies through the lens of physics informed neural networks

TL;DR

This paper investigates anomalies in active grid turbulence using a combination of experimental measurements, machine learning, and simulations to understand their origins and effects on flow correlations.

Contribution

It introduces a physics-informed neural network approach to analyze turbulence anomalies and links experimental observations with numerical simulations for better understanding.

Findings

Anomalies near active grids cause persistent flow correlations.

Simulations incorporating anomalies reproduce experimental decay behaviors.

Machine learning helps identify the impact of anomalies on turbulence statistics.

Abstract

Active grids operated with random protocols are a standard way to generate large Reynolds number turbulence in wind and water tunnels. But anomalies in the decay and third-order scaling of active-grid turbulence have been reported. We combine Laser Doppler Velocimetry and hot-wire anemometry measurements in a wind tunnel, with machine learning techniques and numerical simulations, to gain further understanding on the reasons behind these anomalies. Numerical simulations that incorporate the statistical anomalies observed in the experimental velocity field near the active grid can reproduce the experimental anomalies observed later in the decay. The results indicate that anomalies in experiments near the active grid introduce correlations in the flow that can persist for long times.