用4D速度测量数据，快速预测孔隙中多相流动态变化。
Learning Pore-scale Multiphase Flow from 4D Velocimetry
- 结合图网络与3D U-Net，从实时4D速度数据推断流体界面演化。
- 在毛细主导条件下（Ca≈10⁻⁶），模拟秒级物理时间内的流动突变和跳跃现象。
- 比传统数值模拟快数百倍，适合研究地下碳氢存储的注入条件与孔隙结构影响。
多相流在多孔介质中的行为支撑着地下二氧化碳封存与氢气储存等技术，但真实三维材料中的孔隙尺度动态仍难表征与预测。本文提出一种多模态学习框架，直接从时序四维（4D）微速度测量数据推断多相孔隙流。模型耦合图网络模拟拉格朗日示踪粒子运动与3D U-Net处理体素化界面演化，以成像得到的孔隙几何作为速度场和界面预测的边界约束，并在每一步时间迭代更新。在毛细主导条件（Ca≈10⁻⁶）下，通过实验序列自回归训练，该学习代理模型可捕捉持续数秒物理时间内的瞬态非局部流动扰动与突发界面重构（如哈因斯跳跃），推理仅需数秒，相较小时至数天尺度的直接数值模拟效率大幅提升。该框架提供快速、实验驱动的预测，开启‘数字实验’新路径，实现对多相流实验中孔隙尺度物理过程的复现，为研究注入条件与孔隙结构对地下碳氢存储的影响提供了高效工具。
原文摘要 · Abstract (English)
Multiphase flow in porous media underpins subsurface energy and environmental technologies, including geological CO$_2$ storage and underground hydrogen storage, yet pore-scale dynamics in realistic three-dimensional materials remain difficult to characterize and predict. Here we introduce a multimodal learning framework that infers multiphase pore-scale flow directly from time-resolved four-dimensional (4D) micro-velocimetry measurements. The model couples a graph network simulator for Lagrangian tracer-particle motion with a 3D U-Net for voxelized interface evolution. The imaged pore geometry serves as a boundary constraint to the flow velocity and the multiphase interface predictions, which are coupled and updated iteratively at each time step. Trained autoregressively on experimental sequences in capillary-dominated conditions ($Ca\approx10^{-6}$), the learned surrogate captures transient, nonlocal flow perturbations and abrupt interface rearrangements (Haines jumps) over rollouts spanning seconds of physical time, while reducing hour-to-day--scale direct numerical simulations to seconds of inference. By providing rapid, experimentally informed predictions, the framework opens a route to ''digital experiments'' to replicate pore-scale physics observed in multiphase flow experiments, offering an efficient tool for exploring injection conditions and pore-geometry effects relevant to subsurface carbon and hydrogen storage.
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