An AI-based Domain-Decomposition Non-Intrusive Reduced-Order Model for
Extended Domains applied to Multiphase Flow in Pipes
- URL: http://arxiv.org/abs/2202.06170v1
- Date: Sun, 13 Feb 2022 00:32:17 GMT
- Title: An AI-based Domain-Decomposition Non-Intrusive Reduced-Order Model for
Extended Domains applied to Multiphase Flow in Pipes
- Authors: Claire E. Heaney, Zef Wolffs, J\'on Atli T\'omasson, Lyes Kahouadji,
Pablo Salinas, Andr\'e Nicolle, Omar K. Matar, Ionel M. Navon, Narakorn
Srinil, Christopher C. Pain
- Abstract summary: We present a new AI-based non-intrusive reduced-order model within a domain decomposition framework.
It is capable of making predictions for domains significantly larger than the domain used in training.
The framework is applied to multiphase slug flow in a horizontal pipe for which an AI-DDNIROM is trained on high-fidelity CFD simulations.
- Score: 0.0
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: The modelling 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 kilometres versus a pipe diameter of
just a few inches. 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 using domain decomposition;
dimensionality reduction; training a neural network to make predictions for a
single subdomain; and by using an iteration-by-subdomain technique to converge
the solution over the whole domain. To find the low-dimensional space, we
explore several types of autoencoder networks, known for their ability to
compress information accurately and compactly. The performance of the
autoencoders is assessed on 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 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. Statistics of the flows
obtained from the CFD simulations are compared to those of the AI-DDNIROM
predictions to demonstrate the success of our approach.
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