用神经算子快速预测多相流界面演化，助力工业过程实时控制
Predicting The Evolution of Interfaces with Fourier Neural Operators
- 基于傅里叶神经算子建模多相流界面演化，替代传统耗时的CFD计算
- 在体积分数模拟数据上训练，对液汽界面演变预测精度极高
- 可实现实时响应，适合需要快速反馈的工业流程控制场景
近年来，神经算子已成为预测偏微分方程（如纳维-斯托克斯方程）演化的强大工具。一些复杂问题需依赖复杂算法处理计算域中的强不连续性，例如涉及大密度梯度或相变的液-汽多相流动。这类问题因计算流体力学（CFD）模型预测速度不足，难以实现对快速工业过程的精细控制。本文证明，基于神经算子的预测时间尺度与多相应用的时间尺度相当，表明其可用于需要快速响应的过程控制。神经算子可利用实验数据、仿真数据或二者结合进行训练。本研究在体积分数（VOF）模拟数据上训练神经算子，结果表明其在预测液-汽界面演化方面表现出极高精度，而界面演化是多相过程控制器中最关键的任务之一。
原文摘要 · Abstract (English)
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.
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