Morphing Airfoils with Four Morphing Parameters

An episodic unsupervised learning simulation using the Q-Learning method is developed to learn the optimal shape and shape change policy for a problem with four state variables. Optimality is addressed by reward functions based on airfoil properties such as lift coecient, drag coecient, and moment coecient about the leading edge representing optimal shapes for specified flight conditions. The reinforcement learning as it is applied to morphing is integrated with a computational model of an airfoil. The methodology is demonstrated with numerical examples of a NACA type airfoil that autonomously morphs in four degrees-of-freedom, thickness, camber, location of maximum camber, and airfoil angle-of-attack, to a shape that corresponds to specified goal requirements. Although nonunique shapes can satisfy the aerodynamic requirements, the results presented in this paper show that this methodology is capable of learning the range of acceptable shapes for a given set of requirements. Also shown is that the agent can use its knowledge to change from one shape to another to satisfy a series of requirements with a probability of success between 92% 96%. This ability is analogous to an aircraft transitioning from one flight phase to another.

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