Fabrication and characterization of folded foils supporting streamwise traveling waves

Calisch, Sam; Fan, Dixia; Jodin, Gurvan; Triantafyllou, Michael

doi:10.1016/j.jfluidstructs.2019.01.004

Cited by 3 publications

(3 citation statements)

References 31 publications

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“…We also note that when applying reinforcement learning in the real-world experiment, it is important, as a first step, to identify the limitations of the available hardware. Taking our experiment as an example, the constraints of the communication speed and the motor response time limit the frequency of the state inquiry and action decision, which may be essential for other flow control problems, for example, using high-frequency traveling waves to control the flow around an airfoil (35). Additionally, the rotation speed of the control cylinders cannot exceed = 3.8 due to the structural pin-pin setup; when the motor rotates faster than = 3.8, the control cylinder starts to vibrate laterally as a flexible cylinder, hence changing the physics of the problem.…”

Section: Discussionmentioning

confidence: 99%

Reinforcement learning for bluff body active flow control in experiments and simulations

Fan

Liu

Wang

et al. 2020

Proc. Natl. Acad. Sci. U.S.A.

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182

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We have demonstrated the effectiveness of reinforcement learning (RL) in bluff body flow control problems both in experiments and simulations by automatically discovering active control strategies for drag reduction in turbulent flow. Specifically, we aimed to maximize the power gain efficiency by properly selecting the rotational speed of two small cylinders, located parallel to and downstream of the main cylinder. By properly defining rewards and designing noise reduction techniques, and after an automatic sequence of tens of towing experiments, the RL agent was shown to discover a control strategy that is comparable to the optimal strategy found through lengthy systematically planned control experiments. Subsequently, these results were verified by simulations that enabled us to gain insight into the physical mechanisms of the drag reduction process. While RL has been used effectively previously in idealized computer flow simulation studies, this study demonstrates its effectiveness in experimental fluid mechanics and verifies it by simulations, potentially paving the way for efficient exploration of additional active flow control strategies in other complex fluid mechanics applications.

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Section: Discussionmentioning

confidence: 99%

Reinforcement learning for bluff body active flow control in experiments and simulations

Fan

Liu

Wang

et al. 2020

Proc. Natl. Acad. Sci. U.S.A.

Self Cite

182

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“…A morphing outer mold line surface could be used to create travelling waves for boundary layer attachment control, which reduces form drag by keeping the laminarized flow attached to the moving surface past the typical separation point. 59 Wave drag can be reduced through the use of foils, such as those demonstrated at the bow for pitching wave drag reduction. 60 The ability to morph for retraction, deployment, and AoA control can increase the performance of these structures.…”

Section: Conclusion and Discussionmentioning

confidence: 99%

Modular Morphing Lattices for Large-Scale Underwater Continuum Robotic Structures

et al. 2023

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In this study, we present a method to construct meter-scale deformable structures for underwater robotic applications by discretely assembling mechanical metamaterials. We address the challenge of scaling up nature-like deformable structures while remaining structurally efficient by combining rigid and compliant facets to form custom unit cells that assemble into lattices. The unit cells generate controlled local anisotropies that architect the global deformation of the robotic structure. The resulting flexibility allows better unsteady flow control that enables highly efficient propulsion and optimized force profile manipulations. We demonstrate the utility of this approach in two models. The first is a morphing beam snake-like robot that can generate thrust at specific anguilliform swimming parameters. The second is a morphing surface hydrofoil that, when compared with a rigid wing at the same angles of attack (AoAs), can increase the lift coefficient up to 0.6. In addition, in lower AoAs, the ratio improves by 5 times, whereas in higher angles it improves by 1.25 times. The resulting hydrodynamic performance demonstrates the potential to achieve accessible, scalable, and simple to design and assemble morphing structures for more efficient and effective future ocean exploration and exploitation.

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“…The machine learning methodology in this paper is not limited to fluid mechanics and can be easily transferred to other areas, e.g., in experimental solid mechanics, where a large number of specimens are required to quantify the modulus of elasticity, the yield stress, and the onset of fracture. Hence, combined with advanced manufacturing technologies (58)(59)(60) that are capable of generating versatile prototypes in a short amount of time, we foresee great potential for automatic sequential experimentation to map material and structural properties (61) to obtain understanding that may lead to new advances, such as developing the next generation of morphing wings for aviation (62). Similarly, this methodology is readily applicable to nondestructive evaluation of materials, where uncertainty quantification and automation will accelerate considerably such testing.…”

Section: Discussionmentioning

confidence: 99%

A robotic Intelligent Towing Tank for learning complex fluid-structure dynamics

et al. 2019

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We describe the development of the Intelligent Towing Tank, an automated experimental facility guided by active learning to conduct a sequence of vortex-induced vibration (VIV) experiments, wherein the parameters of each next experiment are selected by minimizing suitable acquisition functions of quantified uncertainties. This constitutes a potential paradigm shift in conducting experimental research, where robots, computers, and humans collaborate to accelerate discovery and to search expeditiously and effectively large parametric spaces that are impracticable with the traditional approach of sequential hypothesis testing and subsequent train-and-error execution. We describe how our research parallels efforts in other fields, providing an orders-of-magnitude reduction in the number of experiments required to explore and map the complex hydrodynamic mechanisms governing the fluid-elastic instabilities and resulting nonlinear VIV responses. We show the effectiveness of the methodology of “explore-and-exploit” in parametric spaces of high dimensions, which are intractable with traditional approaches of systematic parametric variation in experimentation. We envision that this active learning approach to experimental research can be used across disciplines and potentially lead to physical insights and a new generation of models in multi-input/multi-output nonlinear systems.

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Fabrication and characterization of folded foils supporting streamwise traveling waves

Cited by 3 publications

References 31 publications

Reinforcement learning for bluff body active flow control in experiments and simulations

Reinforcement learning for bluff body active flow control in experiments and simulations

Modular Morphing Lattices for Large-Scale Underwater Continuum Robotic Structures

A robotic Intelligent Towing Tank for learning complex fluid-structure dynamics

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