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Accurate prediction of the particle image velocimetry flow field and rotor thrust using deep learning

Published online by Cambridge University Press:  23 March 2022

Sehyeok Oh Open the ORCID record for Sehyeok Oh [Opens in a new window] ,
Seungcheol Lee Open the ORCID record for Seungcheol Lee [Opens in a new window] ,
Myeonggyun Son Open the ORCID record for Myeonggyun Son [Opens in a new window] ,
Jooha Kim Open the ORCID record for Jooha Kim [Opens in a new window]  and
Hyungson Ki Open the ORCID record for Hyungson Ki [Opens in a new window]
Sehyeok Oh
Affiliation:
Department of Mechanical Engineering, Ulsan National Institute of Science and Technology (UNIST), 50 UNIST-gil, Ulsan 44919, South Korea Department of Materials AI & Big-Data, Korea Institute of Materials Science (KIMS), 797 Changwon-daero, Changwon 51508, South Korea
Seungcheol Lee
Affiliation:
Department of Mechanical Engineering, Ulsan National Institute of Science and Technology (UNIST), 50 UNIST-gil, Ulsan 44919, South Korea
Myeonggyun Son
Affiliation:
Department of Mechanical Engineering, Ulsan National Institute of Science and Technology (UNIST), 50 UNIST-gil, Ulsan 44919, South Korea
Jooha Kim*
Affiliation:
Department of Mechanical Engineering, Ulsan National Institute of Science and Technology (UNIST), 50 UNIST-gil, Ulsan 44919, South Korea
Hyungson Ki*
Affiliation:
Department of Mechanical Engineering, Ulsan National Institute of Science and Technology (UNIST), 50 UNIST-gil, Ulsan 44919, South Korea
*
†Email addresses for correspondence: hski@unist.ac.kr, kimjooha@unist.ac.kr
†Email addresses for correspondence: hski@unist.ac.kr, kimjooha@unist.ac.kr
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Abstract

With particle image velocimetry (PIV), cross-correlation and optical flow methods have been mainly adopted to obtain the velocity field from particle images. In this study, a novel artificial intelligence (AI) architecture is proposed to predict an accurate flow field and drone rotor thrust from high-resolution particle images. As the ground truth, the flow fields past a high-speed drone rotor obtained from a fast Fourier transform-based cross-correlation algorithm were used along with the thrusts measured by a load cell. Two deep-learning models were developed, and for instantaneous flow-field prediction, a generative adversarial network (GAN) was employed for the first time. It is a spectral-norm-based residual conditional GAN translator that provides a stable adversarial training and high-quality flow generation. Its prediction accuracy is 97.21 % (coefficient of determination, or R2). Subsequently, a deep convolutional neural network was trained to predict the instantaneous rotor thrust from the flow field, and the model is the first AI architecture to predict the thrust. Based on an input of the generated flow field, the network had an R2 accuracy of 94.57 %. To understand the prediction pathways, the internal part of the model was investigated using a class activation map. The results showed that the model recognized the area receiving kinetic energy from the rotor and successfully made a prediction. The proposed architecture is accurate and nearly 600 times faster than the cross-correlation PIV method for real-world complex turbulent flows. In this study, the rotor thrust was calculated directly from the flow field using deep learning for the first time.

JFM classification

Mathematical Foundations: Machine learning
Type
JFM Papers
Information
Journal of Fluid Mechanics , Volume 939 , 25 May 2022 , A2
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Copyright
© The Author(s), 2022. Published by Cambridge University Press

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