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This paper investigates the interrelated effects of thermomechanical treatments and chemical composition on the phase transformation capabilities of thin sheets made from Cu-Al-Mn shape memory alloys. The transformation capacity and transition temperatures were determined using DSC and DMA testing, while composition measurements were performed using SEM/EDX analysis. The results demonstrate that applying hot-rolling treatments to alloys of reduced thickness leads to manganese oxidation and modifications in chemical composition, adversely impacting the phase transformation performance. This effect can be mitigated by the use of cold rolling. Additionally, the presence of phosphorus impurities can create inclusions that bind manganese, preventing it from remaining in the solid solution and further affecting phase transformation capabilities.

The application of intelligent systems for structural health monitoring is investigated. A change in the nominal configuration can be related to a structural defect that has to be monitored before it reaches a critical condition. Evidently, the ability to automatically detect changes in a structure is a very attractive feature. When there is no prior knowledge on the system, deep learning models could effectively detect a change and enhance the capability of determining the damage location. However, the acquisition of data related to damaged structures is not always practical. In this paper, two deep learning approaches, a physics-informed autoencoder and a simple data-driven autoencoder, are applied to a test rig consisting of a small four-storey building model. Modifications to the system are simulated by changing the stiffness of the springs. Both the machine learning algorithms outperform the traditional approach based on an experimental modal analysis. Moreover, the increased potential of the physics-informed neural networks to detect and locate damage is confirmed.

In many structures, undesired noise and vibrations generated by external sources represent a huge problem in terms of structural damage and comfort. Active vibration absorbers can be used to dynamically suppress vibrations, by increasing the damping of the system. A wireless smart active damper has been developed to perform this task and some automated functionalities have been implemented to perform the identification of the structure on which it is mounted on. The sharing of information between wireless sensors represents one of the most interesting features of this kind of control system. In this work, a procedure to estimate the nondimensional damping and modal amplitude for each wireless sensor location and each vibration mode is studied. Then, the information obtained by each sensor in the identification phase are used to implement a coordinated control strategy, which is based on a modified version of the Efficient Modal Control (EMC). Such control strategy implements the low level Selective Negative Derivative Feedback control law and modulates the control gains of each actuator and controlled mode pair in order to get an effective vibration reduction. The tuning procedure represents the next step of the algorithm, in which the evaluation of the introduced damping and the maximum applicable gains are derived; finally, the proposed solution is validated with experimental results on a simply-supported beam.

Wireless power transfer (WPT) devices represent one of the most efficient and increasingly used technologies for the transfer of data and power in the near-field range. This work analyzes and describes a new type of device: a ferrite-cored, variable gap, high-frequency power and data transfer system. The classic theoretical models existing in the literature for near-field communication (NFC) and WPT devices have foreseen a lumped-parameters characterization based on the representation of an equivalent circuit model (ECM). The strict interdependence between the different physical domains has clearly increased the difficulty in predicting the behavior of the device, due to the unwanted continuous and chaotic variation of the parameters. The proposed paper aims to provide a general and reliable multi-physics model based on the co-simulation of a Spice®-based ECM analysis and the ESRF Radia®-based 3D finite volume methodology (3DFVM), placing particular emphasis on the intrinsic sensitivity with respect to variables that cannot be directly controlled, such as the variation of the air gap between the coupled coils interfaces. Furthermore, this work outlines a detailed and effective experimental methodology for the estimation of static and dynamic electro-magnetic parameters and the validation of the numerical models in both the time and frequency domain, through the analysis of a real coupled WPT device.

Interest in autonomous underwater vehicles is constantly increasing following the emerging needs of underwater exploration and military purposes. Thus, several new propulsion mechanisms are studied and developed. Fish swimming is a promising source of inspiration because they outperform conventional propellers in terms of energy efficiency and maneuvrability. Their advantages are not only due to the streamlined shape and their low-drag skin but also, above all, due to the particular fin motion, which makes thrust generation possible with small energy dissipation. This paper analyses the motion of batoid fishes that are considered highly efficient by biologists. Their motion is reproduced by different linkage mechanisms optimized to fit underwater robots. A bioinspired robot mimicking cownose ray locomotion is, then, designed and built. Numerical analysis of its dynamics allows us to measure the size of actuators and to estimate the robot behavior. Finally, the control algorithm that maintains the mechanism synchronization according to different strategies is described and some experimental results are presented.