New insights into experimental stratified flows obtained through a physics-informed neural network

We develop a physics-informed neural network (PINN) to significantly augment state-of-the-art experimental data of stratified flows. A fully connected deep neural network is trained using experimental data in a salt-stratified inclined duct (SID) experiment. SID sustains a buoyancy-driven exchange flow for long time periods, much like an infinite gravity current. The data consist of time-resolved, three-component velocity fields and density fields measured simultaneously in three dimensions at Reynolds number= O(10^3) and at Prandtl or Schmidt number = 700 [1]. The PINN enforces incompressibility, the governing equations for momentum and buoyancy, and the boundary conditions at the duct walls. These 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 of Holmboe waves; (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 asymmetric Holmboe wave dynamics. These results mark a significant step forward in furthering the reach of fluid mechanics experiments, especially in the context of stratified turbulence, where accurately computing three-dimensional gradients and resolving small scales remain enduring challenges.

References
[1] L. Zhu, X. Jiang, A. Lefauve, R. R. Kerswell, and P. F. Linden. New insights into experimental
stratified flows obtained through physics-informed neural networks. J. Fluid Mech., 981:R1, 2024.