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The modelling of multiphase flow in a pipe presents a significant challengefor high-resolution computational fluid dynamics (CFD) models due to the highaspect ratio (length over diameter) of the domain. In subsea applications, thepipe length can be several hundreds of kilometres versus a pipe diameter ofjust a few inches. In this paper, we present a new AI-based non-intrusivereduced-order model within a domain decomposition framework (AI-DDNIROM) whichis capable of making predictions for domains significantly larger than thedomain used in training. This is achieved by using domain decomposition;dimensionality reduction; training a neural network to make predictions for asingle subdomain; and by using an iteration-by-subdomain technique to convergethe solution over the whole domain. To find the low-dimensional space, weexplore several types of autoencoder networks, known for their ability tocompress information accurately and compactly. The performance of theautoencoders is assessed on two advection-dominated problems: flow past acylinder and slug flow in a pipe. To make predictions in time, we exploit anadversarial network which aims to learn the distribution of the training data,in addition to learning the mapping between particular inputs and outputs. Thistype of network has shown the potential to produce realistic outputs. The wholeframework is applied to multiphase slug flow in a horizontal pipe for which anAI-DDNIROM is trained on high-fidelity CFD simulations of a pipe of length 10 mwith an aspect ratio of 13:1, and tested by simulating the flow for a pipe oflength 98 m with an aspect ratio of almost 130:1. Statistics of the flowsobtained from the CFD simulations are compared to those of the AI-DDNIROMpredictions to demonstrate the success of our approach.

An AI-based Domain-Decomposition Non-Intrusive Reduced-Order Model for Extended Domains applied to Multiphase Flow in Pipes