Additive manufacturing (AM) is increasingly applied in aerospace for producing lightweight brackets, turbine blades, and propulsion components. A persistent challenge, however, involves the fact that process–property relationships learned from one alloy rarely transfer effectively to another. Aerospace alloys such as Ti-6Al-4V, Inconel 718, and AlSi10Mg each exhibit distinct thermal and absorptive behaviours, making qualification costly and reliant on extensive trial-and-error. Conventional machine learning models provide useful predictions but often act as black boxes, limiting both interpretability and cross-alloy applicability. This study introduces a physics-regularized transfer learning framework that adapts knowledge from a source alloy with extensive data (Ti-6Al-4V) to target alloys with limited datasets (Inconel 718, AlSi10Mg). A baseline neural network is fine-tuned for the target alloys, while the training is guided by physics-informed constraints, including volumetric energy density, thermal diffusivity, and absorptivity scaling. Embedding these terms into the loss function encourages the model to remain physically consistent while capturing alloy-specific features. Preliminary results suggest that the proposed approach improves predictive accuracy for surface roughness and porosity compared with both standalone neural networks and conventional transfer learning. Beyond accuracy, the framework represents a step toward generalizable and interpretable AI tools for aerospace AM. By reducing reliance on trial-and-error, such methods can accelerate alloy qualification, lower experimental costs, and contribute to more sustainable and efficient aircraft and spacecraft design.

The growing expansion of commercial aviation has intensified the need for effective noise mitigation strategies in aircraft propulsion systems. Broadband jet noise and tonal compressor noise remain significant contributors to overall aircraft acoustic emissions. This study investigates passive aeroacoustic control techniques aimed at reducing such noise sources through numerical modeling and parametric analysis.

A computational framework based on the Transference Matrix Method (TMM) combined with acoustic impedance modeling is employed to evaluate the sound absorption and transmission characteristics of perforated and microporous liner materials. The acoustic performance is analyzed over a frequency range of 100 Hz to 5000 Hz. Key geometric parameters, including porosity, hole diameter, and cavity depth, are systematically varied to determine their influence on attenuation efficiency under representative operating conditions.

The simulation results indicate that microporous liners exhibit improved absorption capability and a more uniform frequency response compared to conventional perforated liners, particularly within the mid- and high-frequency bands where compressor tonal components are dominant. An optimized configuration with 5% porosity and a hole diameter of 1 mm achieves a maximum transmission loss of approximately 18 dB within the evaluated frequency spectrum.

The findings provide a structured computational approach for the early-stage design and optimization of passive acoustic liners in modern turbofan compressor systems. This study supports the integration of advanced liner configurations to enhance noise reduction performance in aerospace propulsion applications.

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.

The swift growth in the number of nanosatellites and CubeSats in orbit around the Earth has noticeably amplified the danger of collisions with micrometeoroids and orbital micro-debris. While these particles are tiny, the high relative speeds at which they move can inflict serious damage to the structure, impair the sensors, or render the whole subsystem non-functional, especially in small satellites that have little redundancy. A majority of the current nanosatellite missions do not have specially designed real-time impact detection hardware on board and instead depend on post-mission inspection or indirect signs like telemetry anomalies for their detection. This article describes a system consisting of several sensors that is compact, uses less power, and is able to detect micro-debris impact on the satellites in real time, which was the main reason for its development. The entire system is made up of several sensing elements, such as piezoelectric sensors, MEMS accelerometers, acoustic sensors, magnetic field sensors, and temperature pressure sensors, which respectively take up the different physical signatures produced by debris impacts. The sensor outputs are then directed to the signal conditioning stage, where filtering and normalization are performed. After this, a threshold-based event detection logic combined with a weighted multi-sensor data fusion method is applied to enhance detection reliability and lower the number of incorrect triggers caused by normal satellite operations. The system deploys event-driven logic for independent and low-power surveillance. Impact occurrences are recorded, sent, or kept with time stamps. The combination of the sensors increases the dependability, separates the background noise from the impacts, and makes possible the logging, power management, and integration of the nanosatellite without interruption, supporting the safety of long-term missions.

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.

Mars habitation is a highly challenging engineering problem due to its extreme environmental conditions, which make human habitation difficult. The Martian atmosphere is extremely thin, with a surface pressure of approximately 600 pascals, and the surface temperature stays very low, around 211 kelvins. The solar radiation keeps fluctuating, and dust storms create wind-driven surface flows. These conditions impose major design restrictions, which require structures to endure high pressure differentials, extreme cold weather and strong wind conditions. Therefore, it is necessary to address these coupled challenges through an integrated design strategy in the early stages of Mars surface habitat development. This work performs a complete numerical analysis using a multiphysics approach to study a theoretical Mars sub-surface habitat system. Two habitat geometries, cylindrical and hemispherical, are modeled using CAD and analyzed using commercial finite element and computational fluid dynamics tools. The structural performance analysis assesses internal pressurization, which meets human-rated conditions. Thermal behavior is analyzed using steady-state heat transfer analysis of a multilayer wall system comprising an aluminum structural shell, a regolith layer, and a low-conductivity insulation layer. The study includes solar radiation effects through solar flux analysis, which considers four representative Martian years (MY48-MY51). Aerodynamic analysis is also performed to evaluate the habitat stability under Mars wind loading. The results demonstrate that habitat geometry and multilayer wall design significantly affect performance. The hemispherical design decreases structural deformation by more than ten times when compared to cylindrical geometries. The thermal analysis demonstrates insulation effectiveness, which results in 80-90% less conductive heat transfer, while regolith provides extra thermal insulation. The aerodynamic results demonstrate that the curved habitat remains stable when exposed to wind forces. The results create a practical base for initial Mars habitat development, which will later include radiation and dust assessment.

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.