Multiphase flow applications of non-intrusive reduced-order models with Gaussian process emulation

TL;DR

This paper demonstrates the effectiveness of non-intrusive reduced-order models with Gaussian process emulation in predicting multiphase flow characteristics, extending the methodology with Deep Gaussian Processes and LSTM for improved interpolation.

Contribution

It introduces an enhanced ROM framework using Deep Gaussian Processes and compares it with LSTM-based interpolation for multiphase flow applications.

Findings

Deep Gaussian Processes outperform standard GPs in interpolation accuracy.

LSTM networks provide competitive results for flow prediction.

The methodology enables reliable extrapolation in initial conditions space.

Abstract

Reduced-order models (ROMs) are computationally inexpensive simplifications of high-fidelity complex ones. Such models can be found in computational fluid dynamics where they can be used to predict the characteristics of multiphase flows. In previous work, we presented a ROM analysis framework that coupled compression techniques, such as autoencoders (AE), with Gaussian process (GP) regression in the latent space. This pairing has significant advantages over the standard encoding-decoding routine, such as the ability to interpolate or extrapolate in the initial conditions' space, which can provide predictions even when simulation data are not available. In this work, we focus on this major advantage and show its effectiveness by performing the pipeline on three multiphase flow applications. We also extend the methodology by using Deep Gaussian Processes (DGP) as the interpolation algorithm and compare the performance of our two variations, as well as another variation from the literature that uses Long short-term memory (LSTM) networks, for the interpolation.