Hypersonic aerospace components experience multiple extreme conditions (up to 3000°C) due to repetitive and abrupt thermo-mechanical shocks, severe aero-thermal heating and repeated thermal gradients. Existing traditional monolithic alloys or coating solutions are unable to meet such concurrent requirements due to interfacial deterioration and limited thermal and wear protection. Development of functionally graded materials through the wire-feed arc additive manufacturing process (WFAAM) become established as a viable large-scale (deposition rate 3-5 kg/h) solution to mitigate critical challenges through tailored compositions and properties. Existing WFAAM-fabricated multi-material systems have emerged that primarily address thermal management and tribological performance as distinct design goals. Therefore, there is limited research on integrated frameworks due to their concurrent incorporation. This study addresses a hybrid critical review and conceptual design approach for future simulation-driven optimization. It introduces an innovative design framework, the WFAAM-driven FGM system, aimed specifically at hypersonic applications. Significant limitations and knowledge deficiencies are examined critically in existing WFAAM-based FGM studies, with a focus on tribology, alloy development and thermal barrier structures. The major aims are (i) to analyze WFAAM-derived FGMs for thermal regulation, specifically on wear and surface degradation mechanisms, and (ii) to analyze graded thermal transition layers to reduce heat flux and thermal shock durability, which is estimated to reach 25-40% in extreme environments. Particular emphasis is placed on the absence of design approaches that balance dilution control, metallurgical compatibility and functional property gradients in high-temperature gradients and shocks. A structured design framework is proposed to develop WFAAM-driven functional gradient metallic systems for extreme performance in hypersonic applications without depending on distinct coating interfaces. This study underscores the capability of WFAAM-fabricated FGMs to combine thermal, mechanical and tribological properties in one structural multifunctional material, suitable for extreme hypersonic aerospace conditions.

Nanosatellites and CubeSat platforms require power management techniques which function effectively because their size and weight limitations together with their power budget restrict their operational abilities until their mission duration finishes. The existing small satellite system design maintains continuous operation for all onboard equipment, which leads to excessive power consumption and decreased mission duration. The research presents a hybrid power conservation system which combines hardware and software components to solve power conservation issues present in small satellite systems. The system integrates hardware-based power management components which consist of power distribution networks and load switching equipment with onboard microcontroller systems that execute intelligent power management through software. The system establishes three operational modes, which include normal operation power-saving mode and survival mode, to control power consumption based on battery status, mission demands and subsystem operational status. The system disables all non-essential subsystems during low-energy operations while keeping necessary functions active to protect mission security. The system operates under evaluation through simulation analysis, which uses actual subsystem power models together with orbital illumination patterns for assessment. The results show that the system achieves a 25-35% reduction in average power consumption, which leads to longer mission time because traditional systems maintain continuous operation of their subsystems. The proposed approach demonstrates an affordable lightweight solution which enhances energy efficiency for nanosatellite missions, making it appropriate for upcoming long-duration CubeSat and small satellite missions.

Nanosatellites and small satellites use multiple compact electronic systems to execute their essential functions of power control and communication and data processing. The entire mission can be affected if electronic components fail because satellites become unrepairable after reaching orbit. Current satellite health monitoring systems depend on fixed threshold values which use telemetry data to monitor temperature and voltage levels. The methods which exist at present can identify problems only after actual damage has begun to occur. The current situation requires development of predictive maintenance methods which can detect early signs of equipment failure before actual breakdowns happen. This research introduces a predictive maintenance system which utilizes telemetry time-series data to monitor satellite electronic equipment. The study analyzes major system components through temperature measurements, voltage readings, current flow data and reset events from essential onboard electronic systems. The process starts with telemetry data cleaning and normalization while the data extraction process begins with essential feature identification. The machine learning models are developed to identify normal and degraded operational patterns which enable them to predict potential system failures. The study demonstrates that the proposed method outperforms traditional threshold-based monitoring systems through its enhanced ability to predict faults. The developed system is well suited for nanosatellite platforms because it requires low computational resources and can be implemented either onboard or at the ground station. This method will enable future satellite operations to perform autonomous health monitoring and extend mission duration while enhancing the dependability of nanosatellite networks.

Aircraft wing ribs form the skeletal backbone of the wing. They maintain the aerodynamic profile and transfer structural loads from the skin to the spars. Conventional manufacturing processes struggle to produce complex geometries, making these components difficult and expensive to manufacture. Recent advances in additive manufacturing (AM) address these limitations. Additive manufacturing enables the production of complex geometries that significantly reduce weight. Most designers use the standard Ashby method to identify the strongest or lightest metal. However, they often overlook whether the material will behave as expected during additive printing. This research focuses on Multi-Objective Material Selection for the design of additive manufacturability of aircraft wing ribs using aluminium-based alloys. A key innovation in this research is the formulation of Hybrid Performance Indices (HPIs). These indices go beyond the traditional Ashby methodology. They mathematically couple structural efficiency metrics with a weighted Processability Factor. The structural metrics include specific density, stiffness, specific strength and Embodied Energy Strength to Embodied Energy Index. The Processability Factor accounts for local material availability, thermal conductivity, printability, recyclability and material cost. This dual evaluation assesses both structural integrity and manufacturing risk simultaneously. The process produces an Additive Pareto Optimal set of candidate materials. This helps engineers predict and prevent issues like warping and residual stress before printing begins. The framework also emphasises sustainability. It prioritises materials that minimise waste and considers Embodied Energy in the selection process. The framework identifies high-performance aluminium alloys that are specifically optimized for the additive manufacturing of aircraft wing ribs. It provides a definitive ranking based on their ability to withstand aerodynamic loads while remaining easy to print. This data-driven approach replaces trial and error with a clear selection matrix for the early design stage. It ensures that the chosen alloy is both structurally sound and manufacturable for aerospace applications.

This work presents the development of an Advanced Runway Health Monitoring System (ARHMS) that combines acoustic sensors with machine learning-based computer vision to enable real-time runway condition assessment. The proposed system addresses critical safety issues such as surface defects, foreign object debris, and structural integrity that are often overlooked during conventional manual inspections. By employing YOLOv8 for accurate object detection and OpenCV for image processing, the prototype was trained on over 600 images from Kaggle datasets and evaluated using a scaled physical runway model. The results demonstrate strong accuracy in hazard detection and highlight the system’s potential for mobile deployment and future predictive capabilities. Key contributions include a hybrid acoustic–vision monitoring framework, analysis of real-world runway incidents, and practical recommendations for preventive maintenance. Despite challenges such as daylight dependency and occasional false positives, this study establishes a promising proof-of-concept for non-intrusive, AI-assisted runway monitoring aimed at significantly enhancing aviation safety.

The results showcase impressive accuracy in hazard detection, with exciting potential for

mobile deployment and advanced predictive capabilities in the future. Key contributions of

this research include the development of a hybrid acoustic–vision workflow, in-depth

analysis of real-life incidents, and actionable recommendations for preventive maintenance.

While we acknowledge challenges such as daylight dependency and the occurrence of false

positives, we also outline a clear roadmap for scaling this system to operational use. This

work not only serves as a compelling proof-of-concept for AI-assisted runway safety but also

underscores the critical need for non-intrusive, real-time monitoring to significantly reduce runway-related accidents.

The operational capacity of single nanosatellite missions is restricted because they can only cover limited ground areas and have infrequent satellite passes, and their missions stop when any subsystem fails. The increasing demand for Earth observation and environmental monitoring capability requires satellite systems which can deliver extensive coverage, operational resilience and flexible sensing capabilities at economical costs. The paper describes a satellite constellation system which uses cluster operations to enable satellites to work together in small groups instead of functioning as separate units. The cluster contains four nanosatellites which each operate their own dedicated payload system that includes optical imaging and environmental sensing and communication relay and navigation and health monitoring. The mission function distribution across the cluster enables operators to achieve space mission objectives while minimizing operational footprint through system complexity, power needs and satellite weight requirements. The system establishes inter-satellite communication links to support its three operational functions, which include real-time data sharing, collaborative task performance and system fault recovery capabilities between different clusters. The system uses multiple clusters stationed in low Earth orbit to achieve complete Earth surface coverage while maintaining enhanced time resolution capabilities. The paper presents system architecture details together with the payload distribution method, inter-satellite communication system and orbital coverage study, which uses actual nanosatellite specifications. The proposed approach to nanosatellite platforms provides scalability and redundancy together with adaptability to upcoming Earth observation, disaster monitoring, climate studies and space-based sensing operations.

Distributed Electric Propulsion (DEP) enables novel aero-propulsive interactions by exploiting propeller slipstream effects to modify the aerodynamic characteristics of lifting surfaces. Several numerical and wind-tunnel investigations have demonstrated the potential of distributed blowing for improving lift capability and aerodynamic efficiency in DEP aircraft configurations. However, experimental flight-test evidence at the full-aircraft scale remains limited. This work presents an experimental investigation of the trimmed aerodynamic characteristics of a DEP flying demonstrator, with specific focus on the impact of propeller-induced blowing on lift, drag, and overall aerodynamic efficiency.

The study is based on flight-test data collected during multiple automated maneuvers and processed through a dedicated identification framework that reconstructs aerodynamic coefficients from locally identified stability and control models. Trimmed lift and drag coefficients are evaluated for each maneuver and used to build CL–α, CD–α and trimmed aerodynamic polar CL–CD relationships. The dataset is clustered according to the propeller advance ratio at trim, adopted as a proxy for the intensity of distributed blowing, and second-order analytical fits are employed to highlight global trends across the explored operating conditions.

The results show a clear and consistent influence of distributed blowing on the aerodynamic characteristics of the aircraft. Increasing blowing intensity produces an upward shift and a steeper slope of the lift curve, a mild reduction in trimmed drag, and a noticeable improvement of the trimmed aerodynamic polar. These combined effects lead to a significant increase in aerodynamic efficiency over the investigated flight envelope.

Despite the inherent scatter of experimental flight data, the observed trends are robust and consistent with physical expectations and previous numerical findings. The present work provides original flight-test evidence of the aerodynamic benefits of distributed propulsion at the aircraft level and supports the potential of DEP configurations for low-speed and high-lift flight conditions.

During the design phase of a fixed-wing remotely piloted aircraft developed at Politecnico di Milano, an experimental investigation was conducted to validate preliminary sizing methods for spoileron. Traditional regression-based and semi-empirical approaches (such as DATCOM and Roskam) were compared with results obtained from a dedicated wind tunnel test campaign.

The need for validation arises from the application of these methodologies to a non-conventional configuration characterized by newly designed reflex airfoils and a distinctive geometric layout, including a highly swept wing, low aspect ratio, and pronounced taper. The lack of prior experimental data on reflex airfoils applied to similar wing configurations equipped with spoileron-type control surfaces raises uncertainties regarding the reliability of estimates derived from traditional approaches, which are generally calibrated on conventional airfoil geometries.

The wind tunnel campaign was conducted using a 1.3 m wingspan aluminum model at a Reynolds number of 1×10⁶, during which roll and yaw performance was systematically mapped by varying the spanwise and chordwise spoiler positions. In an initial phase, the ratio between spoiler chord and local wing chord was kept constant. Subsequently, the sensitivity to variations in this ratio, spoiler spanwise extension, and control surface deflection was analyzed.

Results show qualitative agreement with trends reported in the literature, highlighting maximum effectiveness at intermediate angles of attack (5–8°) and a reduction in effectiveness when the spoiler is placed toward the wing tip. Quantitatively, the measured performance is slightly higher than that predicted using traditional preliminary methods. The results therefore indicate that conventional regression-based approaches can be reasonably applied to wings equipped with reflex airfoils, despite their original calibration on conventional airfoil geometries. Moreover, the observed systematic deviation suggests the need for minor correction factors when applying these methods to similar unconventional configurations, contributing to enhanced confidence in early-stage control surface sizing.

Lighter-than-air platforms are currently gaining the attention of engineers for diverse applications. Beside their superior endurance performance granted by buoyancy as a means for countering weight, electric motors coupled to propellers, with their high thrust-to-weight ratio and small size, allow designers to deploy them within the topology of a novel airship, thus mitigating the inherent controllability issues of these platforms.

Among the missions currently envisaged for airships on Earth are high-altitude pseudo-satellite (HAPS) applications, where the ability of the airship to reposition, further increase its own flight time through solar energy harvesting, and return to base after a long-endurance flight, constitute an interesting alternative for surveillance and intelligence gathering missions traditionally associated with space satellites. Furthermore, the atmospheric and irradiance conditions encountered in the layers of the atmosphere at 18–20 km of altitude are partly similar to those found on non-terrestrial bodies. This naturally suggests an application of airships for non-terrestrial exploration especially on planets where a sufficiently dense atmosphere is present.

Lighter-than-air applications (notably, uncontrolled balloons) have already been attempted on Venus, along with preliminarily studied in the past for both Venus and Mars. However, most of the literature is less recent than the latest developments in technology, especially in solar cells and envelope material, besides that of electric motors and batteries, which nowadays can be exploited to reduce the weight and size of an unmanned airship.

This study, grounded on a pre-existing method for the preliminary sizing of airships already employed for several terrestrial applications, tries to fill this gap by analyzing the sizing solution of an airship for Martian exploration through a parameterized approach. This allows us to study the effect of different technological choices on the sizing solution, highlighting the most relevant bottlenecks towards the feasibility of this concept, dictated by constraints on geometrical dimension and mass.

Reliable characterization of atmospheric turbulence in the urban boundary layer is critical for the flight safety and structural integrity of light aerial platforms operating below 300 m. For unmanned airships, in particular, turbulence represents a well-known complication factor due to their large aerodynamic surfaces and low-speed flight regimes. An accurate understanding of near-ground conditions is essential for these vehicles, especially in Milan, where experimental activities are scheduled as part of the IPROP project. However, conventional high-resolution sensing infrastructure is often too costly or spatially sparse for real-time deployment. This study proposes a low-cost turbulence-intensity proxy, TI_b, derived from standard 10-minute ground-based meteorological data. The metric is defined as TI_b(t) = α|dU/dt|+βσ_U, capturing both impulsive shear transients and stochastic fluctuations.

The methodology was evaluated using data from two Milanese stations during April–May 2025: Lambrate (suburban) and P.zza Zavattari (dense urban core). The results show that the suburban site exhibits higher dynamic activity, with a mean TI_b of 0.142 and peak excursions exceeding 0.36, indicating frequent shear-driven bursts. Conversely, the urbancore shows a smoother environment with a mean TI_b of 0.097. This work maps these proxies to three operational risk categories—Nominal, Elevated Workload, and Caution—providing a practical heuristic for mission planning. The findings underscore the necessity of localized hazard mapping for Urban Air Mobility (UAM) and experimental airship operations in complex metropolitan airspaces.