Imperial College London
Alternate

ProfessorChristosMarkides

Faculty of Engineering, Department of Chemical Engineering

Professor of Clean Energy Technologies
 
 
 
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Contact

 

+44 (0)20 7594 1601c.markides Website

 
 
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Location

 

404ACE ExtensionSouth Kensington Campus

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Summary

 

Publications

Citation

BibTex format

@article{Sun:2022:10.1016/j.enganabound.2022.04.001,
author = {Sun, F and Xie, G and Song, J and Markides, CN},
doi = {10.1016/j.enganabound.2022.04.001},
journal = {Engineering Analysis with Boundary Elements},
pages = {282--299},
title = {Proper orthogonal decomposition and physical field reconstruction with artificial neural networks (ANN) for supercritical flow problems},
url = {http://dx.doi.org/10.1016/j.enganabound.2022.04.001},
volume = {140},
year = {2022}
}
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RIS format (EndNote, RefMan)

TY  - JOUR
AB  - The development of mathematical models, and the associated numerical simulations, are challenging in higher-dimensional systems featuring flows of supercritical fluids in various applications. In this paper, a data-driven methodology is presented to achieve system order reduction, and to identify important physical information within the principal flow features. Firstly, a new hybrid neural network based on radial basis function (RBF) and multi-layer perceptron (MLP) methods, namely RBF-MLP, is tested to achieve a highly nonlinear approximation. When provided with 1000 nonlinear test samples, this model provides an excellent prediction accuracy with a maximum regression coefficient (R) of 0.99 and a minimum root mean square error (RMSE) below 1%. Furthermore, the model is also proven to be flexible enough to capture accurately the turbulent fluctuation characteristics, even at significant nonlinear buoyancy conditions. Secondly, the high-dimensional buoyancy data is collected and integrated into a matrix database. Subsequently, a proper orthogonal decomposition (POD) approach is employed to reduce the high-dimensional database, and to obtain a set of low-dimensional POD basis-spanned space, which defines a reduced-order system with low-dimensional basis vectors. The results reveal that the first five order modes contain dominant flow features, accounting for 93% of the total mode energy, which can be selected to reconstruct the physical flow field. Thirdly, a new data-driven POD-ANN model is established to construct the nonlinear mapping between the full-field buoyancy data and decomposed basis vectors. It is also demonstrated that the POD-ANN model reconstructs the principal flow features accurately and reliably. This POD-ANN model can be used to provide new insights for reduced-order modelling and for reconstructing physical fields of higher-dimensional nonlinear flow cases.
AU  - Sun,F
AU  - Xie,G
AU  - Song,J
AU  - Markides,CN
DO  - 10.1016/j.enganabound.2022.04.001
EP  - 299
PY  - 2022///
SN  - 0955-7997
SP  - 282
TI  - Proper orthogonal decomposition and physical field reconstruction with artificial neural networks (ANN) for supercritical flow problems
T2  - Engineering Analysis with Boundary Elements
UR  - http://dx.doi.org/10.1016/j.enganabound.2022.04.001
UR  - https://www.sciencedirect.com/science/article/pii/S0955799722001035?via%3Dihub
UR  - http://hdl.handle.net/10044/1/96562
VL  - 140
ER  -
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