Predicting The Evolution of Interfaces with Fourier Neural Operators

TL;DR

This paper demonstrates that Fourier Neural Operators can accurately predict the evolution of liquid-vapour interfaces in multi-phase flows, enabling fast control of complex industrial processes involving sharp discontinuities.

Contribution

It introduces the application of Fourier Neural Operators to multi-phase flow interface prediction, showing high accuracy and real-time capabilities for complex CFD problems.

Findings

Neural operators achieve high accuracy in interface evolution prediction.

Predictions are comparable in time scale to industrial multi-phase applications.

Neural operators can be trained on simulation data for effective control.

Abstract

Recent progress in AI has established neural operators as powerful tools that can predict the evolution of partial differential equations, such as the Navier-Stokes equations. Some complex problems rely on sophisticated algorithms to deal with strong discontinuities in the computational domain. For example, liquid-vapour multiphase flows are a challenging problem in many configurations, particularly those involving large density gradients or phase change. The complexity mentioned above has not allowed for fine control of fast industrial processes or applications because computational fluid dynamics (CFD) models do not have a quick enough forecasting ability. This work demonstrates that the time scale of neural operators-based predictions is comparable to the time scale of multi-phase applications, thus proving they can be used to control processes that require fast response. Neural Operators can be trained using experimental data, simulations or a combination. In the following, neural operators were trained in volume of fluid simulations, and the resulting predictions showed very high accuracy, particularly in predicting the evolution of the liquid-vapour interface, one of the most critical tasks in a multi-phase process controller.