用图神经网络从实验数据学多相流演化，提升孔隙尺度模拟精度。
Learning Pore-scale Multi-phase Flow from Experimental Data with Graph Neural Network
- 基于图网络直接学习微米级断层扫描数据中的流体动态
- 模型可自回归预测多相流随时间演变，保持高效计算
- 适合需高精度模拟的碳封存、氢能存储等能源研究
理解多相流体在多孔介质中的运移对气候变化缓解技术（如二氧化碳地质封存、氢气储存和燃料电池）至关重要。然而，现有数值模型往往难以准确捕捉实验中观察到的复杂孔隙尺度物理机制。本文提出一种基于图神经网络的方法，直接利用微米级断层扫描（micro-CT）实验数据学习孔隙尺度多相流。我们设计了长-短边网格图网络（LSE-MGN），能够预测每个时间步下孔隙空间中各节点的状态。推理时，给定初始状态，模型可自回归地预测多相流过程的时序演化。该方法成功从高分辨率实验数据中捕捉物理规律，同时保持计算效率，为复杂多相流动力学的高精度、高效建模提供了新方向。
原文摘要 · Abstract (English)
Understanding the process of multiphase fluid flow through porous media is crucial for many climate change mitigation technologies, including CO$_2$ geological storage, hydrogen storage, and fuel cells. However, current numerical models are often incapable of accurately capturing the complex pore-scale physics observed in experiments. In this study, we address this challenge using a graph neural network-based approach and directly learn pore-scale fluid flow using micro-CT experimental data. We propose a Long-Short-Edge MeshGraphNet (LSE-MGN) that predicts the state of each node in the pore space at each time step. During inference, given an initial state, the model can autoregressively predict the evolution of the multiphase flow process over time. This approach successfully captures the physics from the high-resolution experimental data while maintaining computational efficiency, providing a promising direction for accurate and efficient pore-scale modeling of complex multiphase fluid flow dynamics.
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