Zobrazeno 1 - 10
of 58
pro vyhledávání: '"Lim, HeeChang"'
This study presents a deep learning model-based reinforcement learning (DL-MBRL) approach for active control of two-dimensional (2D) wake flow past a square cylinder using antiphase jets. The DL-MBRL framework alternates between interacting with a de
Externí odkaz:
http://arxiv.org/abs/2408.14232
Autor:
Yousif, Mustafa Z, Paraskovia, Kolesova, Yang, Yifang, Zhang, Meng, Yu, Linqi, Rabault, Jean, Vinuesa, Ricardo, Lim, HeeChang
The present study proposes an active flow control (AFC) approach based on deep reinforcement learning (DRL) to optimize the performance of multiple plasma actuators on a square cylinder. The investigation aims to modify the control inputs of the plas
Externí odkaz:
http://arxiv.org/abs/2309.09197
This study proposes a novel deep-learning-based method for generating reduced representations of turbulent flows that ensures efficient storage and transfer while maintaining high accuracy during decompression. A Swin-Transformer network combined wit
Externí odkaz:
http://arxiv.org/abs/2309.09192
A multi-agent deep reinforcement learning (DRL)-based model is presented in this study to reconstruct flow fields from noisy data. A combination of the reinforcement learning with pixel-wise rewards (PixelRL), physical constraints represented by the
Externí odkaz:
http://arxiv.org/abs/2302.09559
Turbulence is a complex phenomenon that has a chaotic nature with multiple spatio-temporal scales, making predictions of turbulent flows a challenging topic. Nowadays, an abundance of high-fidelity databases can be generated by experimental measureme
Externí odkaz:
http://arxiv.org/abs/2208.05754
This study proposes a newly-developed deep-learning-based method to generate turbulent inflow conditions for spatially-developing turbulent boundary layer (TBL) simulations. A combination of a transformer and a multiscale-enhanced super-resolution ge
Externí odkaz:
http://arxiv.org/abs/2206.01618
In this study, a deep learning-based approach is applied with the aim of reconstructing high-resolution turbulent flow fields using minimal flow fields data. A multi-scale enhanced super-resolution generative adversarial network with a physics-based
Externí odkaz:
http://arxiv.org/abs/2109.04250
Publikováno v:
In International Journal of Thermofluids August 2024 23
The turbulent flow past a wall-mounted square cylinder with an aspect ratio of four was investigated with the aid of Spalart-Allmaras improved delayed detached-eddy simulation (S-A IDDES) and proper orthogonal decomposition (POD). The Reynolds number
Externí odkaz:
http://arxiv.org/abs/2012.11263
Autor:
Ahmed, Rayhan, Lim, Heechang
Publikováno v:
J. Fluid Mech. 823 (2017) pp.787-818
This paper describes a study of the generation of a plughole vortex and its consequences in a drainpipe during drainage of water from a stationary rectangular tank. The critical and minimum depths of water above the inlet of the drainpipe, where a su
Externí odkaz:
http://arxiv.org/abs/2012.11125