\chapter{Conclusion and Future Work} \label{Chap: Conclusion}

This report has ventured into the nascent and challenging intersection of active flow control and hydrodynamic perception management. Moving beyond the traditional objectives of performance optimization, such as drag reduction or vibration suppression, this work has established a comprehensive framework for achieving active hydrodynamic cloaking and illusion in a non-linear, vortical flow regime. By leveraging the power of model-free Deep Reinforcement Learning, we have demonstrated that it is possible to command a simple actuator system—the fluidic pinball—to perform sophisticated wake engineering tasks that were previously considered intractable.

The primary contribution of this thesis is the successful demonstration of both high-fidelity hydrodynamic cloaking and, for the first time to our knowledge, targeted hydrodynamic illusion in a complex, unsteady flow environment. We began by validating our DRL framework in a baseline steady flow, where the agent learned to completely suppress vortex shedding and nullify the net hydrodynamic forces on the pinball, effectively rendering it invisible to the mean flow. The true capability of the framework was then proven in more demanding scenarios. We demonstrated robust cloaking in the presence of complex background flows, including periodic Kármán vortex streets and aperiodic, transient vortices. In these cases, the DRL agent discovered non-trivial, multi-frequency control policies to allow these structures to pass through the control region with their fundamental characteristics preserved.

Building upon this success, we introduced and validated the novel concept of hydrodynamic illusion. The DRL agent successfully learned to manipulate the pinball's wake to convincingly mimic the distinct hydrodynamic signature of a different bluff body. This was achieved with remarkable precision, matching not only the overall wake structure but also the target's characteristic Strouhal number. Our investigation into mimicking targets of varying sizes revealed a fascinating control strategy, where the agent shifted to higher-frequency actuation to generate larger-scale flow structures, opening new questions about the underlying physics of control. Finally, we have taken the crucial first steps toward bridging the gap between simulation and reality by designing and performing preliminary tests on a physical experimental platform, identifying key engineering challenges and proposing viable solutions, such as reducing the observation space required for effective control.

While the results presented herein mark a significant step forward, they also illuminate a vast and exciting landscape for future research. The work completed thus far serves as a strong foundation upon which several promising research avenues can be built. These future directions aim to enhance the performance and applicability of the current framework, deepen our physical understanding of the learned control strategies, and accelerate the discovery process.

\begin{figure}[htbp!]
     \centering
     \includegraphics[width=\textwidth, trim=0 0 0 3cm, clip]{Graphs/gantt_chart.pdf}
     \caption{Gantt chart visualizes the timeline for the PhD research.}
     \label{fig:gantt_chart}
\end{figure}

One of the most immediate priorities is to enhance the control performance and robustness, particularly at higher Reynolds numbers. Our results indicated a clear degradation in both cloaking and illusion performance as the Reynolds number increased beyond 200. Future work could explore more advanced DRL algorithms, such as introduce transformer layer, which may be better suited to capturing the longer time dependencies and more chaotic dynamics of higher-Re flows. Furthermore, a curriculum learning approach, where the agent is progressively trained on tasks of increasing difficulty (e.g., gradually increasing the Reynolds number), could significantly improve the agent's ability to generalize. Investigating more actuation methods, such as change the position of the pinball, could also provide the additional control authority needed to manipulate more energetic flows.

A second critical direction is to improve the interpretability of the learned control policies and extract physical insights. The DRL agent, while effective, currently operates as a "black box." A significant future contribution would be to distill the complex, neural network-based policy into a simpler, physically interpretable "white-box" model. Techniques such as symbolic regression or sparse identification of nonlinear dynamics (SINDy) could be employed to find a concise mathematical expression that approximates the agent's control law. This would not only demystify the agent's decision-making process but could also lead to the discovery of new, fundamental principles of flow control. Understanding why the agent chooses a specific multi-frequency actuation or why it shifts to higher harmonics for certain illusion tasks would be of immense value to the broader fluid mechanics community.

Finally, the integration of Reduced-Order Models (ROMs) into the training pipeline presents a powerful opportunity to dramatically accelerate future research. The primary bottleneck in this work was the computational expense of high-fidelity CFD simulations. By developing an accurate and computationally inexpensive ROM of the fluidic pinball system, for example using a POD-Galerkin approach, we could create a surrogate environment for DRL training. An agent could undergo thousands or even millions of learning episodes on the ROM in a fraction of the time required by the full simulation. This "ROM-in-the-loop" training would enable more extensive exploration of the parameter space, faster development of control policies for new tasks, and more robust hyperparameter tuning. The policy learned on the ROM could then be efficiently fine-tuned on the high-fidelity model or transferred directly to the physical experiment, creating a highly efficient sim-to-real workflow.

In conclusion, this report has pushed the frontier of active flow control into the realm of perception management. By demonstrating the feasibility of hydrodynamic cloaking and illusion through deep reinforcement learning, we have opened the door to a new class of intelligent fluid systems. The future work outlined here promises not only to refine these capabilities but also to deepen our fundamental understanding of flow control, ultimately paving the way for transformative applications in marine engineering, underwater robotics, and environmental science.