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

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...

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Bibliographic Details
Published inarXiv.org
Main Authors Botsas, Themistoklis, Pan, Indranil, Mason, Lachlan R, Matar, Omar K
Format Paper
LanguageEnglish
Published Ithaca Cornell University Library, arXiv.org 15.11.2021
Subjects
Algorithms
Computational fluid dynamics
Encoding-Decoding
Gaussian process
Initial conditions
Interpolation
Multiphase flow
Reduced order models
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Summary: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.
ISSN:2331-8422