The real-time reconstruction of the displacement field of a structure from a network of in situ strain sensors is commonly referred to as “shape sensing”. The inverse finite element method (iFEM) stands out as a highly effective and promising approach to perform this task. In the current investigation, this technique is employed to monitor different plate structures experiencing flexural and torsional deformation fields. In order to reduce the number of installed sensors and obtain more accurate results, the iFEM is applied in synergy with smoothing element analysis (SEA), which allows the pre-extrapolation of the strain field over the entire structure from a limited number of measurement points. For the SEA extrapolation to be effective for a multitude of load cases, it is necessary to position the strain sensors appropriately. In this study, an innovative sensor placement strategy that relies on a multi-objective genetic algorithm (NSGA-II) is proposed. This approach aims to minimize the root mean square error of the pre-extrapolated strain field across a set of mode shapes for the examined plate structures. The optimized strain reconstruction is subsequently utilized as input for the iFEM technique. Comparisons are drawn between the displacement field reconstructions obtained using the proposed methodology and the conventional iFEM. In order to validate such methodology, two different numerical case studies, one involving a rectangular cantilevered plate and the other encompassing a square plate clamped at the edges, are investigated. For the considered case studies, the results obtained by the proposed approach reveal a significant improvement in the monitoring capabilities over the basic iFEM algorithm with the same number of sensors.

This work presents a novel Graph Neural Network (GNN) based framework for structural damage detection and localization in composite aerospace structures. The sensor network is modeled as a graph whose nodes correspond to the strain measurement points placed on the system, while the edges capture spatial and structural relationships among sensors. Strain mode shapes, extracted via Automated Operational Modal Analysis (AOMA), are used as input features for the GNN. Two architectures are developed: one for binary damage detection and another for damage localization, the latter outputting a spatial probability distribution of damage over the structure. Both networks are trained and validated on synthetic datasets generated from high-fidelity finite element transient simulations performed on a composite wing equipped with 40 strain sensors. The obtained results show strong effectiveness in both detection and localization tasks, thus highlighting the potential of leveraging GNNs for topology-aware Structural Health Monitoring applications. In particular, the proposed framework achieves an AUC of 0.97 for damage detection and a mean localization error of approximately 3% of the wingspan on the synthetic dataset. The performance of the GNN is also compared with a fully connected and a convolutional neural network, demonstrating significant improvements in the localization accuracy.

Miniaturization, multi-functionality, and low power consumption are key features in the development of electronic devices today. These characteristics open up the possibility for the development of small, self-powered systems. Piezoelectric materials have emerged as a promising solution due to their ability to convert mechanical energy into electrical energy from the surrounding environment, such as wind. In this research work, we investigate an aeroelastic piezoelectric energy harvester based on the interaction between vortex-induced vibrations and galloping. The device, a bimorph cantilevered beam with a square-section bluff body attached to its tip, produces mechanical oscillations that are extracted by various electrical circuits, including a nonlinear switching interface. A numerical model, implemented in a Simulink environment, is developed to solve the coupled electro-aeroelastic problem for each electrical interface. The results are compared to experimental data collected from wind tunnel tests conducted at various wind velocities and load resistances, and a satisfactory correlation is observed. The validated model is finally used to carry out a parametric analysis aimed at optimizing the performance of the harvester in a low wind speed range. This investigation could contribute to a better understanding of an aeroelastic harvesting system and potentially assist in the optimization of design parameters and electrical interfaces for specific applications.

Purpose: In this study, a methodology for damage detection and localization in aeronautical structures based on Automatic Operational Modal Analysis and a strain-based damage index is presented. Methods: The proposed approach enables the automatic extraction of strain mode shapes under operational conditions, relying solely on output measurements. Modal parameters are estimated using Stochastic Subspace Identification, with pole selection performed via Density-Based Spatial Clustering. The methodology is initially validated numerically on two different case studies: a stiffened cantilevered plate and a composite glider. In both cases, damage is simulated by locally reducing the stiffness of specific regions, and strain signals are collected from virtual sensors under spatially and temporally random excitation. The proposed approach is then applied to an experimental test on a manufactured composite glider model, instrumented with Fiber Bragg Grating sensors bonded to the wing surface. Results and Conclusion: The numerical results demonstrate that the proposed methodology effectively detects and localizes damage across varying intensities, even under noisy conditions. Experimental results confirm the effectiveness of the methodology in real-world conditions, highlighting its potential for in-flight Structural Health Monitoring applications.