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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 deep learning surrogate model (DL-SM) and computational fluid dynamics (CFD) simulations to suppress wake vortex shedding, significantly reducing computational costs. The DL-SM, which combines a Transformer and a multiscale enhanced super-resolution generative adversarial network (MS-ESRGAN), effectively models complex flow dynamics, efficiently emulating the CFD environment. Trained on 2D direct numerical simulation (DNS) data, the Transformer and MS-ESRGAN demonstrated excellent agreement with DNS results, validating the DL-SM's accuracy. Error analysis suggests replacing the DL-SM with CFD every five interactions to maintain reliability. While DL-MBRL showed less robust convergence than model-free reinforcement learning (MFRL) during training, it reduced training time by 49.2%, from 41.87 hours to 20.62 hours. Both MFRL and DL-MBRL achieved a 98% reduction in shedding energy and a 95% reduction in the standard deviation of the lift coefficient (C_L). However, MFRL exhibited a nonzero mean lift coefficient due to insufficient exploration, whereas DL-MBRL improved exploration by leveraging the randomness of the DL-SM, resolving the nonzero mean C_L issue. This study demonstrates that DL-MBRL is not only comparably effective but also superior to MFRL in flow stabilization, with significantly reduced training time, highlighting the potential of combining deep reinforcement learning with DL-SM for enhanced active flow control., Comment: 19 pages, 12 figures

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 ratio of 4. The test is conducted in a small-scale closed return-type wind tunnel. The Reynolds number (${Re}_d$) of the experiments is 500 based on the width of the bluff body and the freestream velocity. The plasma actuators are installed on the top surface and the rear surface of the square cylinder. The induced flow velocities of the plasma actuators are modulated by adjusting the operating voltage and frequency of the high-voltage generator. In this work, particle image velocimetry (PIV) is used to obtain the velocity fields. Furthermore, force measurements are conducted to investigate the effect of using plasma actuators with different driving voltages on the drag force. Our results show that the plasma actuators can successfully suppress flow separation and reduce the size of the recirculation region and turbulent kinetic energy (TKE) in the wake. A correlation between the drag coefficient and the operating voltage of the power generator is also revealed and the mean drag coefficient is found to decrease with increasing imposing voltage. The plasma actuators can enhance the momentum exchange and the interactive behavior between the shear layer and the flow separation region, resulting in flow reattachment at the free end and shrinkage of the recirculation zone in the near-wake region of the bluff body. Overall, the present study demonstrates the practical effectiveness of using plasma actuators for active flow control around FWMSC., Comment: 19 pages, 10 figures

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 momentum equation and the pressure Poisson equation and the known boundary conditions is utilised to build a physics-guided deep reinforcement learning (PGDRL) model that can be trained without the target training data. In the PGDRL model, each agent corresponds to a point in the flow field and it learns an optimal strategy for choosing pre-defined actions. The proposed model is efficient considering the visualisation of the action map and the interpretation of the model performance. The performance of the model is tested by utilising synthetic direct numerical simulation (DNS)-based noisy data and experimental data obtained by particle image velocimetry (PIV). Qualitative and quantitative results show that the model can reconstruct the flow fields and reproduce the statistics and the spectral content with commendable accuracy. These results demonstrate that the combination of DRL-based models and the known physics of the flow fields can potentially help solve complex flow reconstruction problems, which can result in a remarkable reduction in the experimental and computational costs., Comment: 13 Pages, 11 figures, Draft for Journal of Fluid Mechanics

Deep packet inspection (DPI) has been extensively investigated in software-defined networking (SDN) as complicated attacks may intractably inject malicious payloads in the packets. Existing proprietary pattern-based or port-based third-party DPI tools can suffer from limitations in efficiently processing a large volume of data traffic. In this paper, a novel OpenFlow-enabled deep packet inspection (OFDPI) approach is proposed based on the SDN paradigm to provide adaptive and efficient packet inspection. First, OFDPI prescribes an early detection at the flow-level granularity by checking the IP addresses of each new flow via OpenFlow protocols. Then, OFDPI allows for deep packet inspection at the packet-level granularity: (i) for unencrypted packets, OFDPI extracts the features of accessible payloads, including tri-gram frequency based on Term Frequency and Inverted Document Frequency (TF-IDF) and linguistic features. These features are concatenated into a sparse matrix representation and are then applied to train a binary classifier with logistic regression rather than matching with specific pattern combinations. In order to balance the detection accuracy and performance bottleneck of the SDN controller, OFDPI introduces an adaptive packet sampling window based on the linear prediction; and (ii) for encrypted packets, OFDPI extracts notable features of packets and then trains a binary classifier with a decision tree, instead of decrypting the encrypted traffic to weaken user privacy. A prototype of OFDPI is implemented on the Ryu SDN controller and the Mininet platform. The performance and the overhead of the proposed sulotion are assessed using the real-world datasets through experiments. The numerical results indicate that OFDPI can provide a significant improvement in detection accuracy with acceptable overheads.