Physics-Informed (and -informative) Reinforcement Learning and Bio-Inspired Design of a Smart Morphing Flapping Wing for Dual Aerial/Aquatic Propulsion and Maneuvering

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Dual aerial/aquatic (DA2) vehicles that allow fast aerial travel interspersed with underwater exploration are envisioned as the best candidate for many oceanic missions, such as water quality sampling, search and rescue, and ocean territory infiltration. Developing such a system can significantly advance Canadian global competitiveness in the future ocean or inland water exploration and exploitation. One of the key obstacles is to design a propulsion system that can work optimally in both air and water. While it is challenging to use traditional propulsor to achieve this mission, nature has provided its own solution of a morphing flapping wing/foil, seen in seabirds. To create and control a morphing flapping actuator for viable DA2 vehicle designs, technologically, apart from 1) an understanding of the vortical flow around flapping foils, it also requires 2) a learning strategy to quickly solve problems with a large number of variables in an uncertain environment, 3) a design and fabrication toolkit for multi-functional and robust smart morphing structures. Therefore, we propose two key tasks to address the aforementioned requirements. The first task is to develop a physics-informed (and -informative) reinforcement learning (Phi2RL) framework for the flapping foil capable of using sparsely distributed pressure sensors to sense the near-body wake and swiftly performing trajectory planning in a turbulent/gusty environment. The Phi2RL includes 1) a system dynamics model that contains both the physics-embedded-as-structure reduced-order model as well as the learning flexibility of the data-assisted component to compensate the unmodeled dynamics, and 2) practical reinforcement learning and transfer learning algorithms to explore and exploit the optimal force (lift and thrust) profile generation in real-time. The second task is to use discrete cellular metamaterial and carbon-black-polydimethylsiloxane (CB-PDMS) to design a smart morphing flapping actuator with a skin of soft pressure sensor arrays that is capable of adaptively alternating wing shapes, areas, and flapping kinematics. The proposed research will provide tremendous insights into a viable propulsion solution for a DA2 vehicle in the future. Additionally, the Phi2RL will be a powerful artificial intelligence (AI)-enhanced fluid experiment solution that can be generalized to address a variety of fluid problems at a broader scope and greater scale, such as drag reduction of streamline and bluff bodies. Furthermore, HQPs, including 2 Ph.D., 3 MSc, and 1 undergraduate, will work collaboratively on this multi-disciplinary project. They will learn knowledge on unsteady aerodynamics/hydrodynamics, reduced-order modeling, experimental testing, AI algorithms, sparse sensing, and digital fabrications and will acquire strong communication and teamwork skills, which transfers them to be successful scientists and engineers contributing to the Canadian academia and industry.