Fourier Neural Operators for Two-Phase, 2D Mold-Filling Problems Related to Metal Casting

TL;DR

This paper introduces a neural operator model that efficiently predicts 2D mold filling dynamics in metal casting, significantly reducing computation time while maintaining high accuracy, thus enabling rapid design and optimization.

Contribution

The paper presents a novel Fourier neural operator architecture combining graph and spectral methods for accurate, fast simulation of mold filling in metal casting, outperforming traditional CFD methods.

Findings

Achieves about 5% mean relative L2 error across fields.

Inference speed is 100 to 1000 times faster than CFD.

Model generalizes well across different geometries and conditions.

Abstract

We formulate mold filling in metal casting as a 2D neural operator learning problem that maps geometry and boundary data on an unstructured mesh to time resolved flow quantities, replacing expensive transient CFD. In the proposed method, a graph based encoder aggregates local neighborhood information on the input mesh and encodes geometry and boundary data, a Fourier spectral core operates on a regular latent grid to capture global interactions across the domain, and a graph based decoder projects the latent fields to a target mesh. The model is trained to jointly predict velocity components, pressure, and liquid volume fraction over a fixed rollout horizon and generalizes across different ingate locations and process settings. On held out geometries and inlet conditions, it reproduces large scale advection and the fluid-air interface evolution with localized errors near steep gradients. The mean relative L2 error is about 5% across all fields, and inference is two to three orders of magnitude faster than conventional CFD, enabling design in the loop exploration. Ablation studies show monotonic accuracy degradation under stronger spatial subsampling of input vertices and a smoother decline under temporal subsampling. Halving the training set yields only a small increase in error. These results establish neural operators as accurate and data efficient surrogates for 2D mold filling and enable rapid optimization of gating systems in casting workflows.