Multiphase Flow

Multiphase flow, the study of the interaction between multiple fluids (liquids and/or gases), aims to understand and predict their complex behavior in various systems. Current research heavily utilizes machine learning, employing neural operator architectures like Multigrid Neural Operators (MgNO) and Fourier Neural Operators (FNO), along with physics-informed neural networks (PINNs) and graph convolutional networks (GCNs), to create efficient surrogate models for simulating multiphase flow in diverse contexts such as porous media and microchannels. These data-driven approaches offer significant speedups compared to traditional numerical methods, impacting fields like oil and gas extraction, geological carbon sequestration, and heat exchanger design by enabling faster and more cost-effective simulations and optimization.