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This work presents a comprehensive study of the burst of an LPG tank in Gravedona, Italy. The possible causes of the burst were investigated through analytical methods and numerical simulations. That is, an analytical lumped system analysis was conducted to accurately predict the thermodynamic properties of the LPG–air mixture within the tank during filling operations. Additionally, computational fluid dynamics (CFD) simulations were carried out to (i) better capture local effects and (ii) determine if the mixture reached explosive conditions during these operations. The likelihood that possible mechanical defects led to the burst of the tank during filling operations was also evaluated through numerical simulations. The proposed methods were validated against experimental observations, confirming their accuracy and reliability. Furthermore, a specifically developed analytical model was used to describe the tank’s dynamic response after the burst. The results provided a comprehensive understanding of the cause of the burst thanks to the combination of analytical models and numerical simulations. The derived insights not only pinpointed the factors leading to the incident, but also provided valuable perspectives for predicting and preventing similar occurrences.

An increase in aircraft availability and readiness is one of the most desired characteristics of aircraft fleets. Unforeseen failures cause additional expenses and are particularly critical when thinking about combat jets and Unmanned Aerial Vehicles (UAVs). For instance, these systems are used under extreme conditions, and there can be situations where standard maintenance procedures are impractical or unfeasible. Thus, it is important to develop a Health and Usage Monitoring System (HUMS) that relies on diagnostic and prognostic algorithms to minimise maintenance downtime, improve safety and availability, and reduce maintenance costs. In particular, within the realm of aircraft structures, landing gear emerges as one of the most intricate systems, comprising several elements, such as actuators, shock absorbers, and structural components. Therefore, this work aims to develop a preliminary digital twin of a nose landing gear and implement diagnostic algorithms within the framework of the Health and Usage Monitoring System (HUMS). In this context, a digital twin can be used to build a database of signals acquired under healthy and faulty conditions on which damage detection algorithms can be implemented and tested. In particular, two algorithms have been implemented: the first is based on the Root-Mean-Square Error (RMSE), while the second relies on the Mahalanobis distance (MD). The algorithms were tested for three nose landing gear subsystems, namely, the steering system, the retraction/extraction system, and the oleo-pneumatic shock absorber. A comparison is made between the two algorithms using the ROC curve and accuracy, assuming equal weight for missed detections and false alarms. The algorithm that uses the Mahalanobis distance demonstrated superior performance, with a lower false alarm rate and higher accuracy compared to the other algorithm.

Structural health monitoring (SHM) has become paramount for developing cheaper and more reliable maintenance policies. The advantages coming from adopting such process have turned out to be particularly evident when dealing with plated structures. In this context, state-of-the-art methods are based on exciting and acquiring ultrasonic-guided waves through a permanently installed sensor network. A baseline is registered when the structure is healthy, and newly acquired signals are compared to it to detect, localize, and quantify damage. To this purpose, the performance of traditional methods has been overcome by data-driven approaches, which allow processing a larger amount of data without losing diagnostic information. However, to date, no diagnostic method can deal with varying environmental and operational conditions (EOCs). This work aims to present a proof-of-concept that state-of-the-art machine learning methods can be used for reducing the impact of EOCs on the performance of damage diagnosis methods. Generative artificial intelligence was leveraged to mitigate the impact of temperature variations on ultrasonic guided wave-based SHM. Specifically, variational autoencoders and singular value decomposition were combined to learn the influence of temperature on guided waves. After training, the generative part of the algorithm was used to reconstruct signals at new unseen temperatures. Moreover, a refined version of the algorithm called forced variational autoencoder was introduced to further improve the reconstruction capabilities. The accuracy of the proposed framework was demonstrated against real measurements on a composite plate.

In recent times, the demand for resilient space rovers has surged, which has been driven by the amplified exploration of celestial bodies such as the Moon and Mars. Recognising the limitations of direct human intervention in such environments, these rovers have gained a great deal of importance. Our proposal introduces a digital twin for space exploration rovers that seamlessly integrates intricate geometric, kinematic, and dynamic models, along with sensor and control systems. It faithfully emulates genuine real-world scenarios, providing an authentic testing ground for rover prototypes and the development of damage detection algorithms. Its flexibility in replicating diverse terrains, environmental conditions, and operational scenarios significantly expedites rover development. The digital twin serves as a valuable tool in the perfecting of damage detection systems, allowing engineers to efficiently craft diagnostic algorithms. This innovative approach not only conserves valuable resources but also ensures the robustness of space mission systems, thus enhancing the overall success and safety of planetary exploration endeavours.

Condition monitoring of rotating shafts is essential for ensuring the reliability and optimal performance of machinery in diverse industries. In this context, as industrial systems become increasingly complex, the need for efficient data processing techniques is paramount. Deep learning has emerged as a dominant approach due to its capacity to capture intricate data patterns and relationships. However, a prevalent challenge lies in the black-box nature of many deep learning algorithms, which often operate without adhering to the underlying physical characteristics intrinsic to the studied phenomena. To address this limitation and enhance the fusion of data-driven methodologies with the fundamental physics of the system under study, this paper leverages physics-informed neural networks (PINNs). Specifically, a simple but realistic numerical case study of an extended Jeffcott rotor model, encompassing damping effects and anisotropic supports for a more comprehensive modelling, is considered. PINNs are used for the estimation of five parameters that characterize the health state of the system. These parameters encompass the radial and angular position of the static unbalance due to the disk installed on the shaft, the stiffness along the principal axes of elasticity, and the non-rotating damping coefficient. The estimation is conducted solely by exploiting the displacement signals from the centre of the disk and, to showcase the efficacy and precision provided by this novel methodology, various scenarios involving different constant rotational speeds are examined. Additionally, the impact of noisy input data is also taken into account within the analysis and the performance is compared to that of traditional optimization algorithms used for parameters estimation.

Blast loading represents a critical dynamic condition for engineering structures. While the response of metal materials to such a condition has been studied in detail, the behavior of composites has not been properly addressed yet. In this context, this work leverages numerical methods to assess the damage that occurs in a carbon-fiber-reinforced polymer plate subjected to close-range blast loading. Numerical analyses were carried out using two methods, i.e., the pure Lagrangian and hybrid coupled Eulerian–Lagrangian approaches. The simulations were validated against observations from a benchmark experimental test taken from the literature. The results showed that (i) the hybrid approach seems to be the most promising solution in terms of efficiency and accuracy; (ii) the Lagrangian approach can accurately reproduce the experimental observations, even though it comes with strong limitations; and (iii) the numerically predicted damage adheres to the experimentally observed damage, although the simulation outcome is influenced by the modeling technique used to describe the behavior of the composite material. We consider the approaches presented in this paper promising for investigation of blast-loaded composite structures, and further improvements can be achieved by (i) refining the description of the material behavior, e.g., by including the strain rate sensitivity; and (ii) better modeling the boundary conditions.

Concentrated Solar Power (CSP) plants use mirrors to reflect and concentrate sunlight onto a receiver, to heat a fluid and store thermal energy, at high temperature and energy density, to produce dispatchable heat and/or electricity. The secondary mirror is a critical component in the optical system of certain Solar Power Tower plants (SPT), as it redirects the concentrated sunlight from the primary mirror onto the receiver, which can be arranged at ground level. In this study, we propose a design optimisation for the secondary mirror of a CSP plant. The design optimisation method consists of two steps. The first step involves the use of the finite element simulation software Abaqus 2022 to analyse the structural performance of the secondary mirror under thermal loads and wind. The second step consists of the use of simulation results to identify the combination of design parameters and best performances, with respect to both design constraints and structural safety. This is carried out by developing an algorithm that selects those configurations which satisfy the constraints by using safety coefficients. The proposed optimisation method is applied to the design of a potential configuration of a secondary mirror for the beam-down of the CSP Magaldi STEM® technology, although the methodology can be extended to other components of CSP plants, such as primary mirrors and receivers, to further enhance the structural performance of these systems.

The inverse finite element method (iFEM) is a model-based technique to compute the displacement (and then the strain) field of a structure from strain measurements and a geometrical discretization of the same. Different literature works exploit the error between the numerically reconstructed strains and the experimental measurements to perform damage identification in a structural health monitoring framework. However, only damage detection and localization are performed, without attempting a proper damage size estimation. The latter could be based on machine learning techniques; however, an a priori definition of the damage conditions would be required. To overcome these limitations, the present work proposes a new approach in which the damage is systematically introduced in the iFEM model to minimize its discrepancy with respect to the physical structure. This is performed with a maximum likelihood estimation framework, where the most accurate damage scenario is selected among a series of different models. The proposed approach was experimentally verified on an aluminum plate subjected to fatigue crack propagation, which enables the creation of a digital twin of the structure itself. The strain field fed to the iFEM routine was experimentally measured with an optical backscatter reflectometry fiber and the methodology was validated with independent observations of lasers and the digital image correlation.

The inverse Finite Element Method (iFEM) has recently gained much popularity within the Structural Health Monitoring (SHM) field since, given sparse strain measurements, it reconstructs the displacement field of any beam or shell structure independently of the external loading conditions and of the material properties. However, in principle, the iFEM requires a triaxial strain measurement for each inverse finite element, which is seldom feasible in practical applications due to both costs and cabling-related limitations. To alleviate this problem several techniques to pre-extrapolate the measured strains have been developed, so that interpolated or extrapolated strain values are inputted to elements without physical sensors: the benefit is that the required number of sensors can be reduced. Nevertheless, whenever the monitored components comprise regions of different thicknesses, each region of constant thickness must be extrapolated separately, due to thickness-induced discontinuities in the strain field. This is the case in many practical applications, especially those concerning fiber-reinforced composite laminates. This paper proposes to extrapolate the measured strain field in a thickness-normalized space, where the thickness-induced trends are removed; this novel method can significantly decrease the number of required sensors, effectively reducing the costs of iFEM-based SHM systems. The method is validated in a simple but informative numerical case study, highlighting the potentialities and benefits of the proposed approach for more complex application scenarios.

The mechanical behaviour of a quasi-brittle material, i.e. Pietra Serena sandstone, was investigated both numerically and experimentally in order to build a reliable numerical modelling system applicable to more complex cases. The Karagozian and Case concrete (KCC) model was exploited as the material constitutive law and a new method to utilise this model for efficient and accurate simulation of quasi-brittle materials is discussed. The capability of this model is evaluated by comparing the results of the numerical simulations with the corresponding experimental results, and the method itself is critically assessed. Keywords: Finite element method (FEM), Triaxial compression test, Brazilian disc test, Quasi-brittle behaviour, Karagozian and Case concrete (KCC) model

Predicting the perforation limit of composite laminates is an important design aspect and is a complex task due to the multi-mode failure mechanism and complex material constitutive behaviour required. This requires high-fidelity numerical models for a better understanding of the physics of the perforation event. This work presents a numerical study on the perforation behaviour of a satin-weave S2-glass/epoxy composite subjected to low-velocity impact. A novel strain-rate-dependent finite-discrete element model (FDEM) is presented and validated by comparison with experimental data for impacts at several energies higher and lower than their perforation limit. The strain rate sensitivity was included in the model by developing a novel user-defined material model, which had a rate-dependent bilinear traction separation cohesive behaviour, implemented using a VUSDFLD subroutine in Abaqus/Explicit. The capability of the model in predicting the perforation limit of the composite was investigated by developing rate-sensitive and insensitive models. The results showed that taking the strain rate into account leads to more accurate predictions of the perforation limit and damage morphology of the laminate subjected to impacts at different energies. The experimental penetration threshold of 89 J was estimated as 79 J by the strain-rate-sensitive models, which was more accurate compared to 52 J predicted by the strain-rate-insensitive model. Additionally, the coupling between interlaminar and intralaminar failure modes in the models led to a more accurate prediction of the delamination area when considering the rate sensitivity.

This study aims to numerically and experimentally investigate the response of a medium strength rock material under unconfined compression loading up to failure. The unconfined compressive strength (UCS) is one of the most important parameters in characterising rock material behaviour. Hence the UCS is crucial in understanding the failure mechanism of fractured rocks. An effective approach to determine the UCS and to investigate the behaviours of rock materials under unconfined compression is essential in the majority of research fields of rock mechanics. The experimental configuration for the unconfined compression test, suggested by the protocols of the ASTM standard, has some limitations which affect the accuracy in determination of the real UCS. Among several alternative configurations proposed, the Mogi's configuration seems to be the most appropriate one. Therefore, the ASTM and Mogi's configurations were used to perform the tests on a medium strength rock material, i.e. Pietra Serena sandstone. The results using two configurations were discussed in terms of the differences. The tests were also replicated in LS-DYNA using a finite element method (FEM) coupled smooth particle hydrodynamics (SPH) technique. This technique is employed in this study due to its capabilities to cope with large deformation issues related to the rocks. An advanced material model, called the Karagozian and Case Concrete (KCC) model, is implemented in the numerical simulations. The KCC model consists of three independent fixed failure surfaces and it can consider the damage accumulation based on the current state of stress among these failure surfaces. An equation-of-state (EOS) is used in conjunction with KCC material model for decoupling the volumetric and deviatoric responses. The numerical and experimental results were finally compared with the focus on the stress–strain diagram and the failure patterns. The comparison shows that the numerical results are in good agreement with the experimental results. Keywords: Pietra Serena sandstone, Unconfined compression, Mogi's configuration, Karagozian and Case Concrete (KCC) model, Finite element method (FEM), Smooth particle hydrodynamics (SPH)

Developing a reliable numerical modelling technique is considered as challenge for fracture assessment of the geological materials, which are much subjected to hydrostatic pressure. For this purpose, the mechanical behaviour and the fracture pattern of a middle strength rock material, called Pietra Serena sandstone, is investigated both numerically and experimentally under a Four-Point Bending (also called Flexural) testing program. For the numerical approach, an innovative method, namely FEMcoupled to-SPH, is exploited due to its capabilities in dealing with rock mechanics related issues. Two different material models, which are the Karagozian and Case Concrete (KCC) and the Extended (Linear) DruckerPrager, are exerted to assess their capabilities. The Flexural strength and the crack initiation area are studied based on the state of the stress in various parts of the specimen in both models, and finally the results obtained from the numerical models are compared with the data obtained from the experimental tests in order to assess the capability of the modelling approach.

The inverse Finite Element Method (iFEM) is receiving more attention for shape sensing due to its independence from the material properties and the external load. However, a proper definition of the model geometry with its boundary conditions is required, together with the acquisition of the structure’s strain field with optimized sensor networks. The iFEM model definition is not trivial in the case of complex structures, in particular, if sensors are not applied on the whole structure allowing just a partial definition of the input strain field. To overcome this issue, this research proposes a simplified iFEM model in which the geometrical complexity is reduced and boundary conditions are tuned with the superimposition of the effects to behave as the real structure. The procedure is assessed for a complex aeronautical structure, where the reference displacement field is first computed in a numerical framework with input strains coming from a direct finite element analysis, confirming the effectiveness of the iFEM based on a simplified geometry. Finally, the model is fed with experimentally acquired strain measurements and the performance of the method is assessed in presence of a high level of uncertainty.

In the case of protection of transportation systems, the optimization of the shield is of practical interest to reduce the weight of such components and thus increase the payload or reduce the fuel consumption. As far as metal shields are concerned, some investigations based on numerical simulations showed that a multi-layered configuration made of layers of different metals could be a promising solution to reduce the weight of the shield. However, only a few experimental studies on this subject are available. The aim of this study is therefore to discuss whether or not a monolithic shield can be substituted by a double-layered configuration manufactured from two different metals and if such a configuration can guarantee the same perforation resistance at a lower weight. In order to answer this question, the performance of a ballistic shield constituted of a layer of high-strength steel and a layer of an aluminum alloy impacted by an armor piercing projectile was investigated in experimental tests. Furthermore, an axisymmetric finite element model was developed. The effect of the strain rate hardening parameter C and the thermal softening parameter m of the Johnson–Cook constitutive model was investigated. The numerical model was used to understand the perforation process and the energy dissipation mechanism inside the target. It was found that if the high-strength steel plate is used as a front layer, the specific ballistic energy increases by 54% with respect to the monolithic high-strength steel plate. On the other hand, the specific ballistic energy decreases if the aluminum plate is used as the front layer.

Nowadays the design of large-scale structures can be effectively improved by the adoption of numerical models. Even if experimental tests still play a fundamental role, a methodological approach that combines experimental testing and modelling technique can significantly improve the understanding of the matter. This, in fact, would result in a more reliable optimization process, drastically reducing efforts and uncertainties towards the implementation of the final product. The present work deals with the development of a finite element model for the analysis of a full-scale prototype of an innovative parabolic trough collector. The collector is analysed under several load conditions in order to evaluate its structural behaviour. Each load configuration is also numerically reproduced. Moreover, it is demonstrated that the model is capable of reproducing both the global (stiffness) and local (strain state) behaviour of the structure. Specifically, the comparison between experimental data and numerical results show a good agreement for the global parameter torsional stiffness. Local strain values are also well reproduced in high-stressed zone. Thus, the model can be used as a reliable “virtual tool” for designers to evaluate the suitability of layout modifications, thereby replacing and reducing the amount of commonly needed experimental tests and, consequently, reducing time and costs. Finally, an example of the potentiality of the finite element model adopted for a computer-aided engineering approach is shown to determine the most promising solution for increasing the torsional stiffness of the trough, while simultaneously limiting the required experimental tests.

In concentrated solar power technology, the precise shape of the reflective surfaces is crucial for efficiency. Considering the geometry and size of a parabolic trough collector, measuring the actual shape is not trivial and some techniques can only be adopted during the assembly operations, evaluating only the manufacturing and alignment processes. The method proposed and tested in this work exploits a laser scanner-based three-dimensional digitization technique that can be used without any marker or other tools, and is attached to the structure. This technique is particularly suitable for assessing the behavior and the optical efficiency of the collectors under load and for validating a finite element model of the structure. The method defines the shape of the parabolic surface by collecting a 3D point cloud of the parabolic surface using a laser scanner. The measured form can then be compared with the ideal shape obtained from a finite element analysis of the structure subject to the gravity field. The comparison can also be performed when the collector is loaded by known forces or torques, with the finite element model reproducing the actual loading scenario. The object of the case study of this work was a 12 m wide full-scale prototype trough collector manufactured at the Politecnico di Milano. The uncertainty of the 3D measurements, acquiring twelve images in different positions, was verified to be less than 3.6 mm.

Maximum loads acting on aircraft structures generally arise when the aircraft is undergoing some form of acceleration, such as during landing. Landing, especially when considering rotorcrafts, is thus crucial in determining the operational load spectrum, and accurate predictions on the actual health/load level of the rotorcraft structure cannot be achieved unless a database comprising the structural response in various landing conditions is available. An effective means to create a structural response database relies on the modeling and simulation of the items and phenomena of concern. The structural response to rotorcraft landing is an underrated topic in the open scientific literature, and tools for the landing event simulation are lacking. In the present work, a coupled sequential simulation strategy is proposed and experimentally verified. This approach divides the complex landing problem into two separate domains, namely a dynamic domain, which is ruled by a multibody model, and a structural domain, which relies on a finite element model (FEM). The dynamic analysis is performed first, calculating a set of intermediate parameters that are provided as input to the subsequent structural analysis. Two approaches are compared, using displacements and forces at specific airframe locations, respectively, as the link between the dynamic and structural domains.

In the present work an experimental program aimed at assessing the mechanical behavior of an innovative parabolic solar trough is presented. More specifically, a lightweight and low-cost collector making large use of adhesive joints, which can be easily assembled on-site, still performing at a high efficiency, was designed. Static and fatigue tests were performed on a full-scale prototype of the collector in the pre-production stage. The tests included differential torsion, concentrated and distributed bending, and distributed load (wind effect). During the tests, a network of strain gauges was placed in the most critical locations to measure the strain field, while laser sensors and cable transducers were placed in strategic positions to measure the displacements. The results demonstrate the strengths of the innovative parabolic trough collector and support the assessment of its structural integrity.

Low-velocity impacts represent a major concern for aeronautical structures, sometimes producing barely detectable damage that could severely hamper the aircraft safety, even with regards to metallic structures. For this reason, the development of an automated impact monitoring system is desired. From a passive monitoring perspective, any impact generates a strain wave that can be acquired using sensor networks; signal processing techniques allow for extracting features useful for impact identification, possibly in an automatic way. However, impact wave characteristics are related to the impactor stiffness; this presents a problem for the evaluation of an impact-related feature and for the development of an automatic approach to impact identification. This work discusses the problem of reducing the influence of the impactor stiffness on one of the features typically characterizing the impact event, i.e., the time of arrival (TOA). Two passive sensor networks composed of accelerometers and piezoelectric sensors are installed on two metallic specimens, consisting of an aluminum skin and a sandwich panel, with aluminum skins and NOMEXTM honeycomb core. The effect of different impactor stiffnesses is investigated by resorting to an impact hammer, equipped with different tips. Subsequently, a method for data processing is defined to obtain a feature insensitive to the impactor stiffness, and this method is applied to multiple impact signals for feature uncertainty evaluation.

This research focused on the assessment of the damaged area on aramid/epoxy composites subjected to high velocity impact. Digital X-ray radiography and active pulsed thermography techniques were used for performing post-mortem analysis of the impacted specimens (8 to 28 aramid layers). Two types of projectiles were used: 9 mm Luger FMJ and 0.357 Magnum FMJ. Two types of dedicated algorithms were developed to post-process the thermograms obtained and the results are compared with the damaged diameter measured directly on the X-ray radiographs. The output of the three methods are therefore compared and discussed.

A novel fiber optic sensing technology for high frequency dynamics detection is proposed in this paper, specifically tailored for structural health monitoring applications based on strain wave analysis, for both passive impact identification and active Lamb wave monitoring. The sensing solution relies on a fiber optic-based interferometric architecture associated to an innovative coherent detection scheme, which retrieves in a completely passive way the high-frequency phase information of the received optical signal. The sensing fiber can be arranged into different layouts, depending on the requirement of the specific application, in order to enhance the sensor sensitivity while still ensuring a limited gauge length if punctual measures are required. For active Lamb wave monitoring, this results in a sensing fiber arranged in multiple loops glued on an aluminum thin panel in order to increase the phase signal only in correspondence to the sensing points of interest. Instead, for passive impact identification, the required sensitivity is guaranteed by simply exploiting a longer gauge length glued to the structure. The fiber optic coherent (FOC) sensor is exploited to detect the strain waves emitted by a piezoelectric transducer placed on the aluminum panel or generated by an impulse hammer, respectively. The FOC sensor measurements have been compared with both a numerical model based on Finite Elements and traditional piezoelectric sensors, confirming a good agreement between experimental and simulated results for both active and passive impact monitoring scenarios.