Local structural excitations in model glasses

Structural excitations of model Lennard-Jones glass systems are investigated using the Activation-Relaxation-Technique (ART), which explores the potential energy landscape of a local minimum energy configuration by converging to a nearby saddle-point configuration. Performing ART results in a distribution of barrier energies that is single-peaked for well relaxed samples. The present work characterises such atomic scale excitations in terms of their local structure and environment. It is found that, at zero applied stress, many of the identified events consist of chain-like excitations that can either be extended or ring-like in their geometry. The location and activation energy of these saddle-point structures are found to correlate with the type of atom involved, and with spatial regions that have low shear moduli and are close to the excess free volume within the configuration. Such correlations are however weak and more generally the identified local structural excitations are seen to exist throughout the model glass sample. The work concludes with a discussion within the framework of $\alpha$ and $\beta$ relaxation processes that are known to occur in the under-cooled liquid regime.Comment: 34 Pages, 13 Figure

Deep learning-based optimization of piezoelectric vibration energy harvesters

Advances in energy harvesting technologies present prospective concepts to capture and store energy from the environment and use it to power sensors used in Structural Health Monitoring (SHM) systems. Among many others, ambient vibrations are a ubiquitous source of energy that has the potential to charge low-powered sensors attached to aircraft structures. This study aims at designing a vibrational-based energy harvesting system consisting of Macro-Fiber Composite (MFC) patches bonded to a cantilever beam with optimal design parameters. As a base model, an electromechanically coupled Finite Element (FE) model is first developed to predict the open-source voltage when subjected to input excitation, which is validated using previous experimental data. Subsequently, the harvested power is found by simulating an electrical circuit consisting of a full-bridge rectifier and an external capacitor, using Electronic Design Automation (EDA) simulation. A Deep learning-based optimization is proposed to calculate the optimal mechanical and electrical parameters, resulting in the maximum number of resonant peaks within a specified frequency range, and also to maximize the power generated from higher-order resonant peaks. Using the developed FE model, a large number of data is generated to train a Deep Neural Network (DNN), which has the capability to find the optimal design parameters for the specified objective. This approach aims at replacing conventional optimization techniques and to obtain an optimal design of broadband vibrational-based energy harvester in a more computationally efficient manner