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.