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.

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.

This study focuses on the creation of a digital twin of a space exploration rover to perform damage detection. The digital twin incorporates various subsystems of real rovers to accurately simulate the rover’s behaviour. Damage detection is performed by introducing damages into the digital twin and comparing signals obtained in healthy and damaged conditions. By using the multiphysics model created by integrating different subsystems, the effect of damages can be observed in other subsystems of the rover. The study aims to demonstrate the potentiality of a digital twin for damage detection, reducing the risk of mission failure and data loss.

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.

Carbon nanotubes (CNTs) have been exploited in structural health monitoring applications as a novel inspection technique for fiber-reinforced plastic materials, thanks to their excellent electrical properties and piezoresistive behavior. However, investigations on the CNTs sensor network behavior during compression after impact tests and correlations between electric resistance variations and damage mechanisms are lacking. Therefore, in this study, the electromechanical behaviour of CNT percolation networks in glass fiber-reinforced plastics materials has been investigated during impact, indentation, and compression after impact tests aiming at relating electrical resistance variations with damage mechanisms, also proving the CNTs network sensitivity in presence of repeated damages. The electrical response of the CNTs networks was compared with conventional nondestructive measurements to improve the understanding of the electrical response. Good agreement between the electrical and mechanical response is demonstrated during impact tests, highlighting a shift in the electrical resistance correlated with permanent and irreversible damage. Sharp changes in electrical resistance are related to sudden variations in impact and indentation loads, which are correlated with damage mechanisms. Compliance between electrical and mechanical signals is also observed in compression after impact tests, resulting in a general decrease of the electrical resistance with increasing loading due to compressive and buckling strains. Finally, compression after impact and repeated impact tests proved the ability of the CNT-based sensor network to sense even after being damaged. These results push towards the future applicability of the CNTs-based monitoring approach for composite structures, exploiting the CNTs self-sensing capability for damage identification.