Physics-informed neural network to augment experimental data: an application to stratified flows

TL;DR

This paper introduces a physics-informed neural network (PINN) that enhances experimental data quality and resolution for stratified flows, enabling noise reduction, distortion correction, vortex detection, and improved turbulence analysis.

Contribution

The study presents a novel PINN framework that enforces physical laws to augment experimental data, improving resolution and revealing hidden flow features in stratified turbulence.

Findings

Noise elimination from measurements

Correction of measurement distortions

Identification of key three-dimensional vortices

Abstract

We develop a physics-informed neural network (PINN) to significantly augment state-of-the-art experimental data and apply it to stratified flows. The PINN is a fully-connected deep neural network fed with time-resolved, three-component velocity fields and density fields measured simultaneously in three dimensions at $Re = O(10^3)$ in a stratified inclined duct experiment. The PINN enforces incompressibility, the governing equations for momentum and buoyancy, and the boundary conditions by automatic differentiation. The physics-constrained, augmented data are output at an increased spatio-temporal resolution and demonstrate five key results: (i) the elimination of measurement noise; (ii) the correction of distortion caused by the scanning measurement technique; (iii) the identification of weak but dynamically important three-dimensional vortices; (iv) the revision of turbulent energy budgets and mixing efficiency; and (v) the prediction of the latent pressure field and its role in the observed Holmboe wave dynamics. These results mark a significant step forward in furthering the reach of experiments, especially in the context of turbulence, where accurately computing three-dimensional gradients and resolving small scales remain enduring challenges.