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The paper begins with a brief historical overview of pressure adaptive materials and structures. By examining avian
anatomy, it is seen that pressure-adaptive structures have been used successfully in the Natural world to hold structural
positions for extended periods of time and yet allow for dynamic shape changes from one flight state to the next. More
modern pneumatic actuators, including FAA certified autopilot servoactuators are frequently used by aircraft around the
world. Pneumatic artificial muscles (PAM) show good promise as aircraft actuators, but follow the traditional model of
load concentration and distribution commonly found in aircraft. A new system is proposed which leaves distributed
loads distributed and manipulates structures through a distributed actuator. By using Pressure Adaptive Honeycomb
(PAH), it is shown that large structural deformations in excess of 50% strains can be achieved while maintaining full
structural integrity and enabling secondary flight control mechanisms like flaps. The successful implementation of
pressure-adaptive honeycomb in the trailing edge of a wing section sparked the motivation for subsequent research into
the optimal topology of the pressure adaptive honeycomb within the trailing edge of a morphing flap. As an input for the
optimization two known shapes are required: a desired shape in cruise configuration and a desired shape in landing
configuration. In addition, the boundary conditions and load cases (including aerodynamic loads and internal pressure
loads) should be specified for each condition. Finally, a set of six design variables is specified relating to the honeycomb
and upper skin topology of the morphing flap. A finite-element model of the pressure-adaptive honeycomb structure is
developed specifically tailored to generate fast but reliable results for a given combination of external loading, input
variables, and boundary conditions. Based on two bench tests it is shown that this model correlates well to experimental
results. The optimization process finds the skin and honeycomb topology that minimizes the error between the acquired
shape and the desired shape in each configuration.
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