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Biology provides many examples of how body adaption can be used to achieve a change in functionality. The feather star, an underwater crinoid that uses feather arms to locomote and feed, is one such system; it releases its arms to distract prey and vary its maneuverability to help escape predators. Using this crinoid as inspiration, we develop a robotic system that can alter its interaction with the environment by changing its morphology. We propose a robot that can actuate layers of flexible feathers and detach them at will. We first optimize the geometric and control parameters for a flexible feather using a hydrodynamic simulation followed by physical experiments. Second, we provide a theoretical framework for understanding how body change affects controllability. Third, we present a novel design of a soft swimming robot ( Figure 1 ) with the ability of changing its morphology. Using this optimized feather and theoretical framework, we demonstrate, on a robotic setup, how the detachment of feathers can be used to change the motion path while maintaining the same low-level controller.

Within the Collaborative Research Centre 880, future technologies are to be developed for the application on a cruise-efficient transport aircraft. Part of this concept is to mount the aircraft engines on the rear part of the wing. This installation position might hold the potential to accommodate aircraft engines with growing engine diameters. Moreover, a synergy between wing and nacelle might lead to a short nacelle design which becomes crucial for increasing bypass ratios. To enhance the coupling between airframe and nacelle, the engine will be partly embedded into the wing. A preliminary design of an embedded ultra-high bypass ratio engine was investigated at high speed conditions. The axial position of the engine was varied at constant lift showing that aircraft performance improves by shifting the nacelle axially downstream. In addition, studies on intake parameter variations for a generic embedded configuration are discussed.

Although often regarded a childhood toy, the design of paper airplanes is subtly complex. The design space and mapping from geometry to distance flown is highly nonlinear and probabilistic where a single airplane design exhibits a multitude of trajectory forms and flight distances. This makes optimization and understanding of their behavior challenging for humans. By understanding the behavior of paper airplanes and predicting flight behavior, there is a potential to improve the design of aerial vehicles that operate at low Reynolds numbers. By developing a robotic system that can fabricate, test, analyze, and model the flight behavior in an unsupervised fashion, a wide design space can be reliably characterized. We find there are discrete behavioral groups that result in different trajectories: nose dive, glide, and recovery glide. Informed by this characterization we propose a method of using Gaussian mixture models to extract the clusters of the design space that map to these different behaviors. This allows us to solve both the forward and reverse design problem for paper airplanes, and also to perform efficient optimization of the geometry for a given target flight distance.