Filtering of electromagnetic signals is key for improved signal to noise ratios for a broad class of devices. However, maintaining filter performance in systems undergoing large changes in shape can be challenging, due to the interdependency between element geometry, orientation and lattice spacing. To address this challenge, an origami-based, reconfigurable spatial X-band filter with consistent frequency filtering is presented. Direct-write additive manufacturing is used to print metallic Archimedean spiral elements in a lattice on the substrate. Elements in the lattice couple to one another and this results in a frequency selective surface acting as a stop-band filter at a target frequency. The lattice is designed to maintain the filtered frequency through multiple fold angles. The combined design, modeling, fabrication, and experimental characterization results of this study provide a set of guidelines for future design of physically reconfigurable filters exhibiting sustained performance.

The integration of soft actuating materials within origami-based mechanisms is a novel method to amplify the actuated motion and tune the compliance of systems for low stiffness applications. Origami structures provide natural flexibility given the extreme geometric difference between thickness and length, and the energetically preferred bending deformation mode can naturally be used as a form of actuation. However, origami fold patterns that are designed for specific actuation motions and mechanical loading scenarios are needed to expand the library of fold-based actuation strategies. In this study, a recently developed optimization framework for maximizing the performance of compliant origami mechanisms is utilized to discover optimal actuating fold patterns. Variant patterns are discovered through exploring different symmetries in the input and output conditions of the optimization problem. Patterns designed for twist (rotational symmetry) yield significantly better performance, in terms of both geometric advantage and energy requirements, than patterns exhibiting vertical reflection symmetries. The mechanical energy requirements for each design are analyzed and compared for both the small and large applied displacement regimes. Utilizing the patterns discovered through optimization, the multistability of the actuating arms is demonstrated empirically with a paper prototype, where the stable configurations are accessed through local vertex pop-through instabilities. Lastly, the coupled mechanics of fold networks in these actuators yield useful macroscopic motions and can achieve stable shape change through accessing the local vertex instabilities. This survey of origami mechanisms, energy comparison, and multistability characterization provides a new set of designs for future integration with soft actuating materials.

A machine learning method to predict steady external fluid flows using elliptic input features is introduced. Using data from as few as one high-fidelity simulation, the proposed method produces models generalisable under changes to boundary geometry by using solutions to elliptic boundary value problems over the flow domain as the model input, instead of Cartesian coordinates of the domain. Training data is generated through pointwise evaluation of flow features at points selected through a quad-tree adaptive sampling method to concentrate training points in areas with large field gradients. Models are trained within a training window around the body, while predictions are smoothly extended to freestream conditions using a Partition-of-Unity extension. Predictive capabilities of the machine learning model are demonstrated in steady-state flow of incompressible fluid around a cylinder and a Joukowski airfoil. The predicted flow field is used to warm-start CFD simulations to achieve acceleration in solver convergence.