3 citations · 4 across the 5 of their papers we have counts for
8 papers · 1 filter
Efficient Active Flow Control Strategy for Confined Square Cylinder Wake Using Deep Learning-Based Surrogate Model and Reinforcement Learning
Meng Zhang, Mustafa Z. Yousif, Minze Xu +3
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 antiphas…
Self-Supervised Learning for Effective Denoising of Flow Fields
Linqi Yu, Mustafa Z. Yousif, Dan Zhou +3
In this study, we proposed an efficient approach based on a deep learning (DL) denoising autoencoder (DAE) model for denoising noisy flow fields. The DAE operates on a self-learnin…
Flow Reconstruction Using Spatially Restricted Domains Based on Enhanced Super-Resolution Generative Adversarial Networks
Mustafa Z. Yousif, Dan Zhou, Linqi Yu +4
This study aims to reconstruct the complete flow field from spatially restricted domain data by utilizing an Enhanced Super-Resolution Generative Adversarial Network (ESRGAN) model…
Optimizing Flow Control with Deep Reinforcement Learning: Plasma Actuator Placement around a Square Cylinder
Mustafa Z Yousif, Kolesova Paraskovia, Yifang Yang +5
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 cyl…
A Swin-Transformer-based Model for Efficient Compression of Turbulent Flow Data
Meng Zhang, Mustafa Z Yousif, Linqi Yu +1
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…
Active flow control over a finite wall-mounted square cylinder by using multiple plasma actuators
Mustafa Z. Yousif, Yifan Yang, Haifeng Zhou +3
The present study aims to investigate the effectiveness of plasma actuators in controlling the flow around a finite wall-mounted square cylinder (FWMSC) with a longitudinal aspect…