Chen, D, Gao, X, Xu, C et al. (4 more authors) (2020) FlowGAN: A Conditional Generative Adversarial Network for Flow Prediction in Various Conditions. In: 2020 IEEE 32nd International Conference on Tools with Artificial Intelligence. 32nd International Conference on Tools with Artificial Intelligence (ICTAI 2020), 09-11 Nov 2020, Baltimore, MD, USA. IEEE ISBN 978-1-7281-9228-4
Abstract
Many flow-related design optimization problems like aircraft and automobile aerodynamic design are solved via computational fluid dynamics (CFD) simulations. However, CFD simulations are known to be resource-demanding and time-consuming. Deep learning (DL) is emerging as a viable means to accelerate CFD simulations by directly predicting the outcomes of multiple simulation iterations. While promising, existing DL-based models have to be re-trained whenever the flow condition changes, which incurs significant training overhead for real-life scenarios with a wide range of flow conditions. This paper presents FLOWGAN, a novel conditional generative adversarial network for accurate prediction of flow fields in various conditions. FlowGAN is designed to directly obtain the generation of solutions to flow fields in various conditions based on observations rather than re-training. As FlowGAN does not rely on knowledge of the underlying governing equations, it can quickly adapt to various flow conditions and avoid the need for expensive re-training. We evaluate FlowGAN by applying it to scenarios of simulating both the whole flow field and selected regions of interest (RoI). Compared to the state-of-the-art DL based methods, FlowGAN significantly reduces the prediction errors by 2.27% while exhibiting a better generalization ability.
Metadata
Item Type: | Proceedings Paper |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. |
Keywords: | Flow fields prediction, Multi-source data processing, GAN, Predictive performance |
Dates: |
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Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Engineering & Physical Sciences (Leeds) > School of Computing (Leeds) |
Depositing User: | Symplectic Publications |
Date Deposited: | 18 Sep 2020 11:22 |
Last Modified: | 30 Apr 2021 20:42 |
Status: | Published |
Publisher: | IEEE |
Identification Number: | 10.1109/ICTAI50040.2020.00057 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:165644 |