Imperial College London
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ProfessorChristopherPain

Faculty of Engineering, Department of Earth Science & Engineering

Professorial Research Fellow
 
 
 
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Contact

 

+44 (0)20 7594 9322c.pain

 
 
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Location

 

4.96Royal School of MinesSouth Kensington Campus

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Summary

 

Publications

Citation

BibTex format

@article{Heaney:2022:10.1063/5.0088070,
author = {Heaney, CE and Wolffs, Z and Tómasson, JA and Kahouadji, L and Salinas, P and Nicolle, A and Navon, IM and Matar, OK and Srinil, N and Pain, CC},
doi = {10.1063/5.0088070},
journal = {Physics of Fluids},
pages = {1--22},
title = {An AI-based non-intrusive reduced-order model for extended domains applied to multiphase flow in pipes},
url = {http://dx.doi.org/10.1063/5.0088070},
volume = {34},
year = {2022}
}
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RIS format (EndNote, RefMan)

TY  - JOUR
AB  - 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
AU  - Heaney,CE
AU  - Wolffs,Z
AU  - Tómasson,JA
AU  - Kahouadji,L
AU  - Salinas,P
AU  - Nicolle,A
AU  - Navon,IM
AU  - Matar,OK
AU  - Srinil,N
AU  - Pain,CC
DO  - 10.1063/5.0088070
EP  - 22
PY  - 2022///
SN  - 1070-6631
SP  - 1
TI  - An AI-based non-intrusive reduced-order model for extended domains applied to multiphase flow in pipes
T2  - Physics of Fluids
UR  - http://dx.doi.org/10.1063/5.0088070
UR  - https://aip.scitation.org/doi/10.1063/5.0088070
UR  - http://hdl.handle.net/10044/1/96759
VL  - 34
ER  -
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