An AI-based non-intrusive reduced-order model for extended domains applied to multiphase flow in pipes

• Published in: Physics of fluids (1994) 2022-05, Vol.34 (5)
• Main Authors: Heaney, Claire E., Wolffs, Zef, Tómasson, Jón Atli, Kahouadji, Lyes, Salinas, Pablo, Nicolle, André, Navon, Ionel M., Matar, Omar K., Srinil, Narakorn, Pain, Christopher C.
• Format: Article
• Language: English
• Subjects: Accuracy; Computational fluid dynamics; Decomposition; Diameters; Domain decomposition methods; Flow simulation; Fluid dynamics; Fractions; High aspect ratio; Inspection; Iterative methods; Mathematical models; Multiphase flow; Neural networks; Physics; Pipes; Proper Orthogonal Decomposition; Reduced order models; Slug flow; Training
• ISSN: 1070-6631; 1089-7666
• DOI: 10.1063/5.0088070

The modeling of multiphase flow in a pipe presents a significant challenge for high-resolution computational fluid dynamics (CFD) models due to the high aspect ratio (length over diameter) of the domain. In subsea applications, the pipe length can be several hundreds of meters vs a pipe diameter of just a few inches. Approximating CFD models in a low-dimensional space, reduced-order models have been shown to produce accurate results with a speed-up of orders of magnitude. In this paper, we present a new AI-based non-intrusive reduced-order model within a domain decomposition framework (AI-DDNIROM), which is capable of making predictions for domains significantly larger than the domain used in training. This is achieved by (i) using a domain decomposition approach; (ii) using dimensionality reduction to obtain a low-dimensional space in which to approximate the CFD model; (iii) training a neural network to make predictions for a single subdomain; and (iv) using an iteration-by-subdomain technique to converge the solution over the whole domain. To find the low-dimensional space, we compare Proper Orthogonal Decomposition with several types of autoencoder networks, known for their ability to compress information accurately and compactly. The comparison is assessed with two advection-dominated problems: flow past a cylinder and slug flow in a pipe. To make predictions in time, we exploit an adversarial network, which aims to learn the distribution of the training data, in addition to learning the mapping between particular inputs and outputs. This type of network has shown the potential to produce visually realistic outputs. The whole framework is applied to multiphase slug flow in a horizontal pipe for which an AI-DDNIROM is trained on high-fidelity CFD simulations of a pipe of length 10 m with an aspect ratio of 13:1 and tested by simulating the flow for a pipe of length 98 m with an aspect ratio of almost 130:1. Inspection of the predicted liquid volume fractions shows a good match with the high fidelity model as shown in the results. Statistics of the flows obtained from the CFD simulations are compared to those of the AI-DDNIROM predictions to demonstrate the accuracy of our approach.