Neural Operator Based Coarse-Grid Navier-Stokes / Interface Tracking Model Development

Direct numerical simulations (DNS) combined with an interface tracking method enable the investigation of interface dynamics in multiphase flows. However, these simulations are typically limited to small, millimeter-sized domains and/or short simulation times. To overcome these limitations, a novel data-driven approach has been proposed. This approach facilitates coarse-grid (CG) Navier-Stokes modeling using the level-set method, allowing for extended domain sizes and/or longer simulation periods. Numerical simulations are performed using the finite element code PHASTA, which has been validated for various two-phase flows and geometries, such as bubbly flow through a spacer grid with mixing vanes, flow regime transition in a pipe, and two-phase flow near the pickoff ring of a steam separator. The Fourier neural operator is trained on DNS datasets generated by PHASTA. The trained model is then incorporated into CG PHASTA simulations as a torch script to provide solutions for the level-set function at each time step. Several tests have been conducted, and the observed speed-up has been documented. Future work aims to extend this workflow to predict temperature variations.