Claudio Sbarufatti

Structural fatigue is one of the primary factors affecting the structural integrity and operational safety of aircraft during their service life. As flight hours accumulate and mission profiles become increasingly complex, accurately assessing and predicting fatigue damage becomes critical for ensuring airworthiness and optimizing maintenance planning. The Airframe Digital Twin has emerged as a promising paradigm for addressing this challenge, enabling structural fatigue diagnosis and prognosis through the development of multiphysics, multiscale, and probabilistic virtual representations of as-built systems. This paper presents a comprehensive and integrated framework for constructing the digital twin of an unmanned aerial vehicle, incorporating in-service load tracking, multilevel structural analysis, and probabilistic diagnosis and prognosis. The flight test of the unmanned aerial vehicle is utilized to validate the proposed framework. Results demonstrate that the developed digital twin can effectively predict fatigue crack growth in real time using only flight parameters as input. Furthermore, with inspection data available, the digital twin can be updated to provide a more accurate prediction of future damage evolution. These insights offer valuable guidance to optimize aircraft fleet maintenance strategies, thereby enhancing safety and cost-effectiveness.

The inverse Finite Element Method (iFEM) is a well-established approach for reconstructing displacement fields from strain measurements, offering notable advantages in structural health monitoring (SHM) of complex systems. However, its application to real-world structures such as stiffened panels is often limited by geometric complexity and sparse sensing capabilities. In such cases, direct iFEM implementation becomes challenging due to the inability to instrument all structural regions, especially around reinforcing elements. This work introduces a novel iFEM-based framework tailored for stiffened panel structures, combining a simplified strain-to-displacement reconstruction with a dehomogenization step. The procedure first applies iFEM to a reduced geometry (e.g., flat plate without stiffeners) using available strain data. Then, a post-processing step transfers the reconstructed displacement field onto the full, detailed geometry of the stiffened structure. This two-step approach enables accurate shape sensing even in the absence of strain sensors on complex features, significantly reducing modeling effort and sensor deployment requirements. The framework is experimentally validated and shows strong potential for SHM applications in marine and aerospace structures.

Ensuring the structural integrity of rotors is crucial in Structural Health Monitoring (SHM). Structural damage occurring in rotating shafts can alter both the mechanical properties and the load distribution, resulting in changes to the system’s dynamic behaviour. This work presents a numerical investigation into the implementation of the Inverse Finite Element Method (iFEM) for rotor condition monitoring. Within the framework of Euler–Bernoulli beam theory, the iFEM approach enables the reconstruction of the full three-dimensional displacement field through a 0th-order inverse finite element beam formulation. Dynamic Finite Element (FE) analyses are employed to validate the performance of the proposed approach under varying boundary and loading conditions. To emulate realistic sensing conditions and determine the minimum detectable shaft unbalance, Gaussian noise is added to the strain data, replicating the measurement variability of Fiber Bragg Grating (FBG) sensors. Furthermore, a systematic approach is developed to identify the optimal trade-off between iFEM reconstruction accuracy and sensor network density for a given rotor configuration. The analyses confirm the robustness of the iFEM methodology in accurately reproducing the displacement field, highlighting its potential for online shaft monitoring. In addition, iFEM demonstrates high sensitivity to variations in structural properties. The results obtained establish a strong foundation for the advancement of iFEM-based SHM frameworks, extending the method’s applicability to new industrial applications.

Accurate knowledge of in-service loads is fundamental to damage prognosis for structures. Ideally, such information would be obtained from permanently installed strain sensors distributed throughout the structure, yet in real-world scenarios, this is not feasible. As an alternative, data-driven models can be developed to predict the quantities of interest from routinely recorded operational data (e.g., flight telemetry), which are often informative of the underlying structural response. However, the ability of these models to make reliable predictions is connected to the acquisition and exploitation of labelled data. At the same time, data sparsity remains a common challenge, arising from transmission issues, sensor malfunctions, or other operational constraints. Along these lines, this work employs Gaussian Process (GP) regression for monitoring an airframe structure under data sparsity. Specifically, a virtual load monitoring framework is developed that reconstructs strain histories at hot spots from standard flight data. To address data sparsity, the study extends conventional GPs into multi-task GPs, enabling information sharing between correlated strain sensors. GPs were chosen because they adopt a flexible, non-parametric approach to modeling the data, avoiding the need to assume a predefined parametric form. Also, they provide probabilistic predictions with credible uncertainty estimates, while sparse variational approximations ensure computational efficiency in large-scale problems. However, in their standard form, they can only handle single outputs. Multi-task extensions reformulate the problem as vector-valued regression, exploiting cross-task covariance to transfer statistical strength from data-rich domains to data-poor ones. The framework is demonstrated using experimental data obtained from the flight of a UAV structure. To evaluate the robustness of the employed models in imbalanced dataset settings, some parts of the time series were hidden from training. Results show that the multi-task GP yields better predictive accuracy and more consistent uncertainty estimates in the masked (white) areas compared to single-task GPs. This can be seen in Figure 1. The analyzed scenario highlights the value of the multi-task learning approach. This lies in its ability to use data from specific operational states (or missions) to enhance predictive performance in other, unobserved states.

Digital Twins (DTs) have attracted significant interest across industries over the past decade, and they play a vital role in modern Structural Health Monitoring (SHM). A key component in DT development is the digital model of the monitored asset. In complex systems, these models can impose a considerable computational workload, necessitating the use of model surrogates to enable real-time simulations. While many effective methodologies for surrogate modelling have been developed, they still only serve as approximations of the original system. Recent advancements in Quantum Computing present the opportunity to create algorithms that can run full-order models significantly faster than traditional computers. The Quantum Finite Element Method (QFEM) is an innovative framework for solving Finite Element Method (FEM) problems using quantum algorithms, aiming to leverage the speed of quantum computers to handle large-scale models without the need for surrogates. This study introduces a QFEM-based application for beam model updating, implemented within a Monte Carlo Markov Chain Metropolis-Hastings (MCMC-MH) algorithm for Bayesian inference. Specifically, this model estimates beam degradation by inferring changes in stiffness, which decrease as the structure wears. The methodology focuses on developing a QFEM model capable of rapidly simulating the structure while varying its parameters. The model's accuracy is evaluated by comparing it with a traditional FEM formulation. Furthermore, using QFEM in conjunction with MCMC-MH allows for the identification of the posterior distribution of the structural parameters. This demonstrates that QFEM's accuracy is sufficient to enable reliable model updating, providing a foundation for future Digital Twin applications. Ultimately, the successful identification of structural parameters confirms that integrating QFEM into Bayesian frameworks is a promising approach for the realisation of next-generation Digital Twins, enabling increased computational speed while maintaining physical accuracy.

Structural Health Monitoring (SHM) of aircraft components is of critical importance due to their operation under severe fatigue loads and strong noise interference. Guided-wave-based SHM offers advantages of long-range propagation and high sensitivity to small defects, however, the damage-induced responses are typically weak and easily masked by noise. To address this challenge, a meta-lens based local signal enhancement strategy is proposed to achieve reliable and sensitive damage detection in noisy environments. A dual-lens and dual-sensor configuration is designed for a two-dimensional monitoring region, enabling both damage quantification and localization using meta-lens enhanced signals. The signal characteristics are systematically compared under noise-free and three noise-level conditions, which are SNR=5, 10, 15 dB, and these features are further used to train a Gaussian Process Regression (GPR) model for probabilistic damage identification. The influence of practical manufacturing and assembly tolerances on the meta-lens performance is also investigated. Both numerical and experimental results confirm that the meta-lens significantly improves the robustness of feature extraction and the sensitivity of damage-related responses. The redistributed signal features facilitate more effective learning within the GPR model, achieving localization errors below 1 mm and quantification errors below 0.5 mm, with a remarkable reduction in estimation uncertainty. Moreover, the performance remains stable within machining and assembly deviations of ±0.02 mm and ±0.2 mm, respectively. These results provide a comprehensive reference for the design, signal processing, and noise robustness performance of meta-structure enhanced SHM systems, demonstrating the strong potential of meta-lens based physical signal amplification for practical damage identification.

This paper develops a numerical methodology to evaluate the influence of seabed properties on the acceleration response of resiliently mounted equipment subjected to underwater explosions (UNDEX). The approach is based on a coupled acoustic–structural analysis (CASA), implemented in ABAQUS CAE, to simulate the transient response of a foundation–resilient–mass system installed on a patrol vessel. The study considers multiple seabed configurations, including rigid, rock, sand, muddy, and muddy-over-sand conditions, with the objective of quantifying how reflected shock waves modify the dynamic loading environment. A far-field non-contact UNDEX scenario is modelled, accounting for both incident and seabed-reflected pressure waves. The response of the mounted equipment is evaluated in terms of transmitted accelerations at different installation locations along the hull. The results demonstrate that seabed characteristics play a dominant role in shaping the vibration response. Rigid and rock seabeds induce significant amplification of acceleration levels, reaching values up to twice those of the free-field condition, whereas softer seabeds lead to a marked attenuation effect. The proposed framework enables a systematic assessment of seabed-induced effects on onboard equipment and provides quantitative support for the design and placement of resilient mounting systems in naval applications.

Rotating machinery is essential in industrial applications, where early fault detection is critical to prevent catastrophic failures. Shafts are mainly vulnerable to imbalances and cracks; these last ones pose a severe risk as they can lead to sudden failure if not identified during their early stages. Cracks induce progressive stiffness reduction, altering the system’s mechanical properties and affecting the forces transmitted to the supports. This study analyses the effects of cracks on a rotating shaft using experimental data. Vibration signals from accelerometers mounted on the supports are processed to identify changes in the shaft’s response. The methodology focuses on distinguishing crack-induced alterations for different imbalance scenarios by analysing key signal features. A statistical detection algorithm and the extracted feature analysis are exploited for crack identification before a critical failure occurs. The results highlight the distinct impact of cracks on the shaft’s dynamic behaviour and demonstrate effective strategies for early detection. While different features highlight the presence of the crack differently, all successfully contribute to detecting the damage. This study provides an analysis of a novel experimental case study for crack detection, enhancing both safety and economic sustainability of rotating machinery.

Fatigue prognostic studies in composites often model the delamination evolution over time in one or two dimensions, for example, by depicting one-dimensional (1D) length or two-dimensional (2D) shape contour. Performing prognostics on a three-dimensional (3D) representation of delamination-related damage has not been considered yet, though it conveys vital information, such as delamination volume and depth, that can be essential for structural performance evaluation. In this context, this work proposes a prognostic framework of a three-dimensional representation of delamination-related fatigue damage for composite laminates. Firstly, to facilitate downstream prognostic modeling, the 3D delamination-related damage image is processed through pixel intensity-based discretization, where pixel intensity values are transformed into depth-related damage severity levels and replaced with integer class labels, resulting in a feature map made of an integer matrix. Then, a U-net–transformer model is trained with prior information-aided loss functions and then used to perform long-horizon prognostics of future feature maps. The framework is demonstrated through composite fatigue experiments under ultrasonic C-scan monitoring. The codes and data are publicly available at https://github.com/JingYuan93/3D-delamination-prognostics.