Imperial College London
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DrPabloSalinas

Faculty of Engineering, Department of Earth Science & Engineering

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pablo.salinas

 
 
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Location

 

Royal School of MinesSouth Kensington Campus

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Summary

 

Publications

Citation

BibTex format

@article{Heaney:2022:10.3389/fphy.2022.910381,
author = {Heaney, C and Liu, X and Go, H and Wolffs, Z and Salinas, P and Navon, IM and Pain, CC},
doi = {10.3389/fphy.2022.910381},
journal = {Frontiers in Physics},
pages = {1--16},
title = {Extending the capabilities of data-driven reduced-order models to make predictions for unseen scenarios: applied to flow around buildings},
url = {http://dx.doi.org/10.3389/fphy.2022.910381},
volume = {10},
year = {2022}
}
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RIS format (EndNote, RefMan)

TY  - JOUR
AB  - We present a data-driven or non-intrusive reduced-order model (NIROM) which is capable of making predictions for a significantly larger domain than the one used to generate the snapshots or training data. This development relies on the combination of a novel way of sampling the training data (which frees the NIROM from its dependency on the original problem domain) and a domain decomposition approach (which partitions unseen geometries in a manner consistent with the sub-sampling approach). The method extends current capabilities of reduced-order models to generalise, i.e., to make predictions for unseen scenarios. The method is applied to a 2D test case which simulates the chaotic time-dependent flow of air past buildings at a moderate Reynolds number using a computational fluid dynamics (CFD) code. The procedure for 3D problems is similar, however, a 2D test case is considered sufficient here, as a proof-of-concept. The reduced-order model consists of a sampling technique to obtain the snapshots; a convolutional autoencoder for dimensionality reduction; an adversarial network for prediction; all set within a domain decomposition framework. The autoencoder is chosen for dimensionality reduction as it has been demonstrated in the literature that these networks cancompress information more efficiently than traditional (linear) approaches based on singular value decomposition. In order to keep the predictions realistic, properties of adversarial networks are exploited. To demonstrate its ability to generalise, once trained, the method is applied to a larger domain which has a different arrangement of buildings. Statistical properties of the flows from the reduced order model are compared with those from the CFD model in order to establish how realistic the predictions are.
AU  - Heaney,C
AU  - Liu,X
AU  - Go,H
AU  - Wolffs,Z
AU  - Salinas,P
AU  - Navon,IM
AU  - Pain,CC
DO  - 10.3389/fphy.2022.910381
EP  - 16
PY  - 2022///
SN  - 2296-424X
SP  - 1
TI  - Extending the capabilities of data-driven reduced-order models to make predictions for unseen scenarios: applied to flow around buildings
T2  - Frontiers in Physics
UR  - http://dx.doi.org/10.3389/fphy.2022.910381
UR  - https://www.frontiersin.org/articles/10.3389/fphy.2022.910381/full
UR  - http://hdl.handle.net/10044/1/97567
VL  - 10
ER  -
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