The prediction of external flow field and hydrodynamic force with limited data using deep neural network
Author:
Publisher
Springer Science and Business Media LLC
Subject
Mechanical Engineering,Mechanics of Materials,Condensed Matter Physics,Modeling and Simulation
Link
https://link.springer.com/content/pdf/10.1007/s42241-023-0042-y.pdf
Reference35 articles.
1. Zhang X., Wu J., Coutier-Delgosha O. et al. Recent progress in augmenting turbulence models with physics-informed machine learning [J]. Journal of Hydrodynamics, 2019, 31: 1153–1158.
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3. Rabault J., Ren F., Zhang W. et al. Deep reinforcement learning in fluid mechanics: A promising method for both active flow control and shape optimization [J]. Journal of Hydrodynamics, 2020, 32(2): 234–246.
4. Sonoda T., Liu Z., Itoh T. et al. Reinforcement learning of control strategies for reducing skin friction drag in a fully developed turbulent channel flow [J]. Journal of Fluid Mechanics, 2023, 960: A30.
5. Fukami K., Nabae Y., Kawai K. et al. Synthetic turbulent inflow generator using machine learning [J]. Physical Review Fluids, 2019, 4(6): 064603.
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