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This paper presents initial findings from aeroelastic studies conducted on a wing-propeller model, aimed at evaluating the impact of aerodynamic interactions on wing flutter mechanisms and overall aeroelastic performance. The flutter onset is assessed using a frequency-domain method. Mid-fidelity tools based on the time-domain approach are then exploited to account for the complex aerodynamic interaction between the propeller and the wing. Specifically, the open-source software DUST and MBDyn are leveraged for this purpose. The investigation covers both windmilling and thrusting conditions. During the trim process, adjustments to the collective pitch of the blades are made to ensure consistency across operational points. Time histories are then analyzed to pinpoint flutter onset, and corresponding frequencies and damping ratios are identified. The results reveal a marginal destabilizing effect of aerodynamic interaction on flutter speed, approximately 5%. Notably, the thrusting condition demonstrates a greater destabilizing influence compared to the windmilling case. These comprehensive findings enhance the understanding of the aerodynamic behavior of such systems and offer valuable insights for early design predictions and the development of streamlined models for future endeavors.

We proposed a novel class of honeycomb structures inspired by triply periodic minimal surfaces (TPMS) architecture with tuneable mechanical responses. The design procedure based on the level-set approximation was first introduced for two TPMS-based honeycombs, namely G-Honeycomb and P-Honeycomb. A numerical model was developed and validated by experimental results to evaluate the mechanical properties of G-Honeycomb and P-Honeycomb lattices. The results showed that P-Honeycomb exhibited higher elastic modulus and plateau stress than G-Honeycomb at various relative densities. Meanwhile, both TPMS-based honeycombs showed higher in-plane elastic modulus than conventional square honeycombs, although they had the same nodal connectivity. The idea of designing lattices with tuneable mechanical responses was achieved by introducing a density gradient or creating a G-P hybrid structure with the topology of G-Honeycomb and P-Honeycomb in different regions. The graded structures showed gradual stiffening and progressive collapse under compression, while the G-P hybrid structure can exhibit distinct properties at different compressive strains.

Advanced composites have a brittle nature making them highly susceptible to failure and propagation under impact loading conditions. Appropriate modeling techniques to accurately simulate these conditions are required. This study presents and examines a coupled thermo-mechanical modeling technique and its associated numerical simulations for analyzing carbon fiber-reinforced composite panels subjected to high-velocity impact. The essential numerical parameters necessary to accurately simulate the selected configuration are determined through a physical-based approach, which has not been previously reported. By following the proposed framework, the conventional trial-and-error calibration process that relies on an extensive testing campaign is minimized. A stacked shell-cohesive methodology has been applied to T800/F3900 unidirectional carbon fiber/epoxy composite panel with 16 plies in a quasi-isotropic layup configuration [(0/90/45/-45)2]s. The flat composite panel was manufactured according to ASTM D8010 standards. Both failure condition and progressive damage analysis have been explored and discussed in comparison with numerical and experimental test cases available in the open literature. Thermal effects on the mechanical performance of composite targets are also discussed based on the application of the constitutive transient thermal coupling method available in LS-DYNA®. The contact heat generated by the conversion of impact-induced damage and the kinetic energy of the projectile is also evaluated and analyzed. New observations regarding modeling techniques, energy transfer, and damage mechanisms in target plates are offered. Additionally, findings related to changes in material characteristics resulting from heat transfer are discussed.

Recent developments in Distributed Satellite Systems (DSS) have undoubtedly increased mission value due to the ability to reconfigure the spacecraft cluster/formation and incrementally add new or update older satellites in the formation. These features provide inherent benefits, such as increased mission effectiveness, multi-mission capabilities, design flexibility, and so on. Trusted Autonomous Satellite Operation (TASO) are possible owing to the predictive and reactive integrity features offered by Artificial Intelligence (AI), including both on-board satellites and in the ground control segments. To effectively monitor and manage time-critical events such as disaster relief missions, the DSS must be able to reconfigure autonomously. To achieve TASO, the DSS should have reconfiguration capability within the architecture and spacecraft should communicate with each other through an Inter-Satellite Link (ISL). Recent advances in AI, sensing, and computing technologies have resulted in the development of new promising concepts for the safe and efficient operation of the DSS. The combination of these technologies enables trusted autonomy in intelligent DSS (iDSS) operations, allowing for a more responsive and resilient approach to Space Mission Management (SMM) in terms of data collection and processing, especially when using state-of-the-art optical sensors. This research looks into the potential applications of iDSS by proposing a constellation of satellites in Low Earth Orbit (LEO) for near-real-time wildfire management. For spacecraft to continuously monitor Areas of Interest (AOI) in a dynamically changing environment, satellite missions must have extensive coverage, revisit intervals, and reconfiguration capability that iDSS can offer. Our recent work demonstrated the feasibility of AI-based data processing using state-of-the-art on-board astrionics hardware accelerators. Based on these initial results, AI-based software has been successively developed for wildfire detection on-board iDSS satellites. To demonstrate the applicability of the proposed iDSS architecture, simulation case studies are performed considering different geographic locations.

Space-based Earth Observation (EO) systems have undergone a continuous evolution in the twenty-first century. With the help of space-based Maritime Domain Awareness (MDA), specially Automatic Identification Systems (AIS), their applicability across the world’s waterways, among others, has grown substantially. This research work explores the potential applicability of Synthetic Aperture Radar (SAR) and Distributed Satellite Systems (DSS) for the MDA operation. A robust multi-baseline Along-Track Interferometric Synthetic Aperture Radar (AT-InSAR) Formation Flying concept is proposed to combine several along-track baseline observations effectively for single-pass interferometry. Simulation results are presented to support the feasibility of implementing this acquisition mode with autonomous orbit control, using low-thrust actuation suitable for electric propulsion. To improve repeatability, a constellation of this formation concept is also proposed to combine the benefits of the DSS. An MDA application is considered as a hypothetical mission to be solved by this combined approach.

One of the United Nations (UN) Sustainable Development Goals is climate action (SDG-13), and wildfire is among the catastrophic events that both impact climate change and are aggravated by it. In Australia and other countries, large-scale wildfires have dramatically grown in frequency and size in recent years. These fires threaten the world’s forests and urban woods, cause enormous environmental and property damage, and quite often result in fatalities. As a result of their increasing frequency, there is an ongoing debate over how to handle catastrophic wildfires and mitigate their social, economic, and environmental repercussions. Effective prevention, early warning, and response strategies must be well-planned and carefully coordinated to minimise harmful consequences to people and the environment. Rapid advancements in remote sensing technologies such as ground-based, aerial surveillance vehicle-based, and satellite-based systems have been used for efficient wildfire surveillance. This study focuses on the application of space-borne technology for very accurate fire detection under challenging conditions. Due to the significant advances in artificial intelligence (AI) techniques in recent years, numerous studies have previously been conducted to examine how AI might be applied in various situations. As a result of its special physical and operational requirements, spaceflight has emerged as one of the most challenging application fields. This work contains a feasibility study as well as a model and scenario prototype for a satellite AI system. With the intention of swiftly generating alerts and enabling immediate actions, the detection of wildfires has been studied with reference to the Australian events that occurred in December 2019. Convolutional neural networks (CNNs) were developed, trained, and used from the ground up to detect wildfires while also adjusting their complexity to meet onboard implementation requirements for trusted autonomous satellite operations (TASO). The capability of a 1-dimensional convolution neural network (1-DCNN) to classify wildfires is demonstrated in this research and the results are assessed against those reported in the literature. In order to enable autonomous onboard data processing, various hardware accelerators were considered and evaluated for onboard implementation. The trained model was then implemented in the following: Intel Movidius NCS-2 and Nvidia Jetson Nano and Nvidia Jetson TX2. Using the selected onboard hardware, the developed model was then put into practice and analysis was carried out. The results were positive and in favour of using the technology that has been proposed for onboard data processing to enable TASO on future missions. The findings indicate that data processing onboard can be very beneficial in disaster management and climate change mitigation by facilitating the generation of timely alerts for users and by enabling rapid and appropriate responses.

Numerical simulations have the potential to be used for designing damage-tolerance composite structures. However, numerical models are currently computationally intensive, and their post-failure evolution and fracture morphology predictions are still limited. In the present work, a numerical methodology to simulate advanced composite joints is presented. The results of a numerical campaign aimed to evaluate the progressive damage and failure analysis (PDFA) of an advanced pin-hole connection under tensile and compressive load are evaluated. A high-fidelity stacked shell-cohesive methodology is employed to simulate the ultimate load, fracture initiation, and propagation of the proposed composite joint. Post-failure erosion methodology is proposed to control the initiation and evolution of composite fractures. The location and extension of the numerically predicted damages are compared with experimental observations. The proposed methodology demonstrates its preliminary ability to be used for designing composite joints up to failure. Specific outcomes are also pointed out.

In recent decades, the increased use of sensor technologies, as well as the increase in digitalisation of aircraft sustainment and operations, have enabled capabilities to detect, diagnose, and predict the health of aircraft structures, systems, and components. Predictive maintenance and closely related concepts, such as prognostics and health management (PHM) have attracted increasing attention from a research perspective, encompassing a growing range of original research papers as well as review papers. When considering the latter, several limitations remain, including a lack of research methodology definition, and a lack of review papers on predictive maintenance which focus on military applications within a defence context. This review paper aims to address these gaps by providing a systematic two-stage review of predictive maintenance focused on a defence domain context, with particular focus on the operations and sustainment of fixed-wing defence aircraft. While defence aircraft share similarities with civil aviation platforms, defence aircraft exhibit significant variation in operations and environment and have different performance objectives and constraints. The review utilises a systematic methodology incorporating bibliometric analysis of the considered domain, as well as text processing and clustering of a set of aligned review papers to position the core topics for subsequent discussion. This discussion highlights state-of-the-art applications and associated success factors in predictive maintenance and decision support, followed by an identification of practical and research challenges. The scope is primarily confined to fixed-wing defence aircraft, including legacy and emerging aircraft platforms. It highlights that challenges in predictive maintenance and PHM for researchers and practitioners alike do not necessarily revolve solely on what can be monitored, but also covers how robust decisions can be made with the quality of data available.

Aiming at the experimental test of the body freedom flutter for modern high aspect ratio flexible flying wing, this paper conducts a body freedom flutter wind tunnel test on a full-span flying wing flutter model. The research content is summarized as follows: (1) The full-span finite element model and aeroelastic model of an unmanned aerial vehicle for body freedom flutter wind tunnel test are established, and the structural dynamics and flutter characteristics of this vehicle are obtained through theoretical analysis. (2) Based on the preliminary theoretical analysis results, the design and manufacturing of this vehicle are completed, and the structural dynamic characteristics of the vehicle are identified through ground vibration test. Finally, the theoretical analysis model is updated and the corresponding flutter characteristics are obtained. (3) A novel quasi-free flying suspension system capable of releasing pitch, plunge and yaw degrees of freedom is designed and implemented in the wind tunnel flutter test. The influence of the nose mass balance on the flutter results is explored. The study shows that: (1) The test vehicle can exhibit body freedom flutter at low airspeeds, and the obtained flutter speed and damping characteristics are favorable for conducting the body freedom flutter wind tunnel test. (2) The designed suspension system can effectively release the degrees of freedom of pitch, plunge, and yaw. The flutter speed measured in the wind tunnel test is 9.72 m/s, and the flutter frequency is 2.18 Hz, which agree well with the theoretical results (with flutter speed of 9.49 m/s and flutter frequency of 2.03 Hz). (3) With the increasing of the mass balance at the nose, critical speed of body freedom flutter rises up and the flutter frequency gradually decreases, which also agree well with corresponding theoretical results.