ABSTRACT. This study presents a numerical investigation of the aerodynamic perfor-mance of the NACA 64A-204 airfoil under subsonic (Mach 0.8) and super-sonic (Mach 1.5) conditions using two-dimensional CFD simulations in ANSYS Fluent. A steady-state RANS approach with the SST k–ω turbulence model and a validated C-type grid was employed to capture shock waves, boundary layer separation, and adverse pressure gradients with high accura-cy. Simulations were conducted over a wide angle-of-attack range (0° – 50°), covering both attached-flow and deep-stall conditions. Results reveal clear differences between regimes: while the airfoil sustains attached flow at mod-erate angles in subsonic conditions, supersonic flows generate strong oblique shocks, earlier separation, and reduced aerodynamic efficiency. The analysis highlights maximum lift and efficiency trends, Mach-dependent drag rise, and flow structure transitions. These findings provide not only detailed aero-dynamic insights into the NACA 64A-204 but also practical implications for high-speed aircraft design, offering a CFD-based reference for performance assessment, future experimental wind tunnel validation, and the develop-ment of drag-mitigation or flow-control strategies.

ABSTRACT. Titan, Saturn's largest moon, offers the unusual pairing of Earth-like nitrogen atmosphere, high 1.5-bar surface density, and low 0.14 g gravity – environments well-suited both to efficient, long-range flight but also to cryogenic, chemically complex operations. Preceding missions, such as Cassini-Huygens, have mapped Titan from orbit and via a single descent probe, yet comprehensive in-situ coverage has not been realized. The majority of aerial mission concepts proposed to date have concentrated on balloons or rotorcraft with comparatively little exploration of fixed-wing performance in Titan conditions. This paper presents a first-cut design of a fixed-wing platform – Titan Atmospheric Navigation and Investigation System (TITANIS) – to bridge this gap. TITANIS is designed for multi-mission, such as low-altitude terrain imaging, stratified atmospheric sampling, and micro-probe deployment along methane-lake shorelines. Using Raymer's sizing methods adapted to Titan's 5.4 kg m⁻³ air density, we define a small 1.14 m-span rectangular wing. Three low-Re airfoils (Eppler 387, Eppler 205, Selig 1223) are evaluated in 2-D CFD for –2° to 10° angles of attack; Eppler 387 has the best lift-to-drag ratio at the zero-incidence cruise point and is selected for the baseline. A 3-D CAD model depicts the high-wing, twin-boom H-tail configuration scaled to a stall speed near 7.8 m s⁻¹ in Titan air. An early 5 × 5 severity–likelihood risk matrix determines stall-margin uncertainty, low-temperature material brittleness, and battery capacity loss to be top design drivers, guiding early mitigation strategies. While it is a conceptual framework, the coupled aerodynamic and risk framework provides a quantitative foundation for guiding future power-plant trades, sub-scale testing, and final mission definition of fixed-wing exploration of Titan.

ABSTRACT. The Rotating Detonation Combustor (RDC) is an emerging technology in the field of propulsion that shows great promise in terms of potential for significant improvements in fuel efficiency and power density when compared to conventional combustion systems. The combustion process in RDCs involves both deflagration and detonation, as well as complex fluid dynamics, including oblique and reflected shock waves. Because of this complexity, the chemical mechanisms are crucial for developing accurate numerical models of RDCs. In this study, a two-dimensional RDC simulation was conducted utilizing three distinct chemical mechanisms. The objective of the study was to elucidate the disparities between the chemistry models. For this study, the following chemical mechanisms were selected for comparison: Burke, USCD, and one-step chemistry mechanisms. For comparison, a range of metrics was analyzed, including, but not limited to, parameters such as detonation height and peak values of static pressure and temperature. In addition, the mass fraction of some important species was analyzed to provide a comprehensive overview. The one-step chemistry mechanism serves as the baseline in this study. The results indicate that using the Burke chemical mechanism results in a lower detonation height, while higher values for pressure and temperature are observed. In simulations using the USCD chemical mechanism, the results are comparable to the baseline case, although they do not completely align. Additionally, OH chemiluminescence imaging has been identified as an effective method for recognizing the detonation wave in experimental studies. A significant limitation of the one-step chemistry mechanism is the absence of the OH radical, which is present in both the Burke and USCD mechanisms. Furthermore, numerical Schlieren images were obtained for each chemical mechanism and compared to enable a more detailed analysis of the wavefront.

ABSTRACT. Understanding shockwave formation and its interaction with different aerodynamic bodies is important not only for high-speed vehicle design but also for getting insight regarding complex aerodynamics / thermodynamics along with validation of commercially available flow solvers. This research investigated the shock structures around two basic geometries which are wedge (10°) and a shock cone (25° half-angle) using Schlieren imaging of supersonic wind tunnel of CAE. The research is backed with the analytical textbook classical formulations and steady Computational Fluid Dynamics (CFD) at Mach numbers of 1.5, 1.75, 2.0, and 2.25. The CFD simulations utilized the SST k–ω turbulence model with Sutherland's three-coefficient viscosity model for accurate boundary layer and shock resolution. Schlieren visualization captured the real-time shock angles, while the analytical θ-β-M relationships provided analytical benchmarks for comparison. The results reflected an excellent agreement of shockwave characteristics in between CFD and analytical calculations for attached shocks. The shock angle β and Mach number M correlation are analyzed for both the models. Shock to shock and shock to boundary layer interactions were also studied for double wedge using CFD simulations for enhanced understanding of shockwave phenomenon. This work provided a foundational validation for future high-speed aerodynamic studies and demonstrated the utility of combining experimental, analytical and numerical techniques for characterizing compressible flows.

ABSTRACT. Abstract This study investigates the development of an unmanned aerial vehicle (UAV) based fire extinguishing system tailored for aerodrome environments. In addition, the project aims to modify the drone to solve the challenges that traditional firefighting methods including delays in response time, limited visibility, and restricted access to fire sources. The project proposes a UAV system capable of remote and efficient fire intervention. The project is divided into two designs, each of which is equipped with different systems that are used to fight fires in different ways (vertically and horizontally). Furthermore, the designs are explained as follows: The first design used in horizontal firefighting includes a drone equipped with a carbon fiber cylinder, a nozzle, and an electrically controlled valve. The principle of this design is that when the drone approaches the fire, using the remote control of the drone, the valve releases compressed fire-extinguishing materials towards the flame through the nozzle. Moreover, the purpose of using a cylinder made of carbon fiber instead of Steel, is to contribute to weight reduction and increase strength, allowing high-pressure containment to increase the amount of fire extinguishing material and improve flight performance such as flight time. Finally, in this design, CO2 is the fire-extinguishing material chosen based on the common fire categories encountered in airport environments. The second design is used in vertical firefighting, which includes a UAV equipped with a release system separately controlled and fire-extinguishing balls. the principle of this design is when the drone reaches vertically above the fire zone, using a separate remote sends an order signal to the release system to drop the ball into the fire source, then the fire extinguisher ball explodes after a few seconds, leads to disperse its fire-material contents and firefighting. In addition, the size of the extinguisher ball correlates with the area it can effectively cover. Moreover, the fire extinguisher ball contains specific fire-extinguishing material that is capable of fighting all types of fire.

ABSTRACT. the frequent and disorganized shift changes in aviation workplaces, particularly in airbases, present significant challenges to operational efficiency, personnel accountability, and overall quality control. The lack of a structured handover process not only hampers productivity but also poses risks to safety and documentation accuracy, especially in high-security, fast-paced environments. Traditional attendance and monitoring systems, which rely heavily on manual inputs, are prone to errors and manipulation, making them insufficient for the demands of modern aviation operations. This paper proposes the integration of facial recognition technology into airbase sections as a transformative solution to personnel tracking and shift management. By deploying camera-based systems that detect and identify individuals in real time, the system captures essential data—such as personnel identity, section presence, and timestamp—automatically and relays it to the central quality management system. This not only streamlines the shift change process but also enhances traceability and accountability across aviation units. The proposed system utilizes machine learning algorithms to ensure high recognition accuracy and robust data protection, forming a digital infrastructure for transparent and efficient workforce monitoring. This innovative approach redefines quality assurance practices in aviation environments, offering a scalable and automated solution to a long-standing organizational issue.

ABSTRACT. In-flight pilots working in high-stress conditions in aviation markets need real- time health perception to minimize the chances of physiological disability in- flight. Nevertheless, the existing cockpit-integrated monitoring systems tend to be obstructive, lack coverage, or not available on older airplanes. The present paper would fill this gap by exploring the possibility of incorporating a consumer-grade wearable device (the Smart Ring) to provide continual, non- obstructive physiological monitoring of pilots in flight

ABSTRACT. In this Paper different advanced control methods will be presented and explained to find the optimum solution for the Benchmark control problem. Supporting this with mathematical evidence for the design control. There is always a gap between control theory and the implementation of that theory on an existing control application. Most of the control methods that were suggested or made by control engineers and researchers are not implemented in real control systems. Also, many existing industrial problems are not getting enough research and are not being studied in the academic field. Benchmark problems can help to reduce this gap and can suggest many solutions for the party involved in control theory and application roles. The target is to scan and provide different controls and modeling-related benchmark problems that can act as inspiration for future benchmark methods to provide optimization-suggested solutions. This research will understand different advanced control techniques and how efficiently can get better performance after comparing the results that come from those techniques. Check the requirements for the Benchmark problems and design a module to reach the possible optimum solution.

ABSTRACT. In recent years, chaotic trajectory tracking has emerged as a novel approach to improve the evasiveness and unpredictability of guided projectiles. The integration of chaos-based guidance mechanisms—specifically, the Lorenz, Sprott-A, and Halvorsen systems—into a swarm projectile framework governed by PID controllers is the focus of this study. The major goal of this study was to find out how physically possible it is, how much control effort it takes, and how much energy it takes to follow realistic chaotic courses when there are external disturbances like wind. Three-dimensional projectile motion, wind perturbations, and actuator dynamics were integrated into a simulation-based methodology to resolve this issue. We employed gimbal angle calculations to find changes in pitch and yaw, as well as angular velocities and torque-based effort estimates. The results suggest that Sprott-A is the most suitable for energy-constrained systems, as it required the least angular and control effort, despite the fact that all chaotic systems exhibited trackable behaviour via PID regulation. On the other hand, Halvorsen demonstrated high torque demands and angular variability, suggesting a compromise between actuation burden and unpredictability. The total energy estimations, which included kinetic, potential, and rotational components, stayed within acceptable bounds for all systems. This showed that they could be used to model real-world projectile dynamics. This investigation facilitates the expansion of autonomous guidance systems' body of knowledge by quantifying the performance implications of chaotic reference tracking. These findings indicate that chaos-informed guidance can provide improved manoeuvrability without circumventing mechanical constraints when correctly calibrated, and it is appropriate for integration into swarm coordination and mission planning architectures.

ABSTRACT. General visual inspections are imperative for commercial and military aircraft to identify damage that may imperil flight safety. Currently, these inspections are predominantly carried out by skilled maintenance personnel who meticulously inspect the aircraft's surface to detect and document defects such as cracks, dents, corrosion, and broken fasteners. This manual process is time-intensive, prone to human error, and hazardous. Consequently, the implementation of computer vision and deep learning techniques for the automated detection of cracks, dents and corrosion in aircraft fuselage through advanced image processing techniques has gained traction across various fields. This advancement is facilitated by Unmanned aerial vehicles (UAVs) equipped with high-resolution cameras, where data is captured and processed utilizing sophisticated algorithms to enhance inspection accuracy and efficiency. The study utilized a dual-method approach, incorporating the Roboflow platform alongside EfficientNet-B7-based convolutional neural networks (CNNs) and Canny Edge Detection to facilitate highly precise crack pattern identification and analysis. By utilizing a dataset of more than 3,000 images, this sophisticated framework significantly enhances detection accuracy, even in structurally complex and challenging-to-access regions. This research demonstrates the effectiveness of integrating MATLAB-based image processing with deep learning techniques to establish a robust model for aircraft crack detection, offering valuable insights for aviation maintenance and safety.

ABSTRACT. Electronic Warfare (EW) involves utilizing the electromagnetic spectrum to gather intelligence on an adversary's activities while simultaneously disrupting their use of the spectrum. This is achieved without hindering the friendly forces' access to the electromagnetic spectrum. EW encompasses the entire electromagnetic spectrum and is employed across land, sea, air, and space. This module will specifically explore its application in airborne operations. EW targets all forms of hostile equipment that transmit or receive electromagnetic signals. This includes radar systems, various communication channels, weaponry guided by optical, infrared, or ultraviolet signals, and an array of devices that detect potential targets by analyzing emissions across these frequency bands. Electronic Warfare (EW) has emerged as a pivotal aspect of modern military operations, enabling strategic dominance through the manipulation of the electromagnetic spectrum. This paper explores the evolution, classifications, technological advancements, applications, and future trends of EW, highlighting its significance in contemporary and future combat scenarios. The study aims to provide an in-depth understanding of EW’s role in enhancing operational effectiveness while addressing the challenges faced in this dynamic domain.

ABSTRACT. Dissimilar welding of titanium alloys and stainless steels is a technologically challenging process primarily due to the formation of brittle intermetallic compounds (IMCs) at the interface, which severely degrades the mechanical properties of the weld. This study investigates the influence of two refractory interlayer materials thicknesses 0.1 mm and 0. 2mm Niobium (Nb on the weld quality of Ti-6Al-4V and SS-304 during automated Pulsed Gas Tungsten Arc Welding (P-GTAW). Both interlayers thicknesses were tested using identical welding parameters, and 0.1 mm thick copper filler wire. Tensile testing, Hardness profiling and optical microscopy were conducted to evaluate mechanical performance and microstructural evolution. The results clearly demonstrate that the use of thicker interlayer material significantly improved the ultimate tensile strength (UTS) and reduced the formation of brittle intermetallic compounds compared to 0.1 mm Nb. The thicker Nb interlayer (0.2 mm) was found to notably enhance joint strength and microstructural integrity compared to a thinner one, by providing better separation and diffusion control between the dissimilar metals. These findings highlight the crucial role of interlayer material selection in dissimilar metal welding and provides a way toward achieving stronger Ti-steel joints for aerospace and nuclear applications.

ABSTRACT. The increasing demand for lightweight and high-performance materials in structural applications has led to the development of metal matrix composites with enhanced mechanical properties. However, the fatigue behavior of such composites under cyclic loading remains a critical challenge, especially for applications involving dynamic stresses. This study aims to evaluate the fatigue life and fatigue limit of Al6061 alloy reinforced with silicon carbide (SiC) particles at varying weight fractions. The composite specimens were fabricated using the stir casting technique with SiC content of 3, 6, 9, and 12 wt%. A stress-life approach under constant amplitude loading was employed to assess the fatigue behavior of the composites. Experimental results indicate that the fatigue life significantly improves with increasing SiC content. Notably, the composite exhibits a fatigue life exceeding 10⁵ cycles when subjected to stress levels below 150 MPa. These findings demonstrate the potential of SiC-reinforced Al6061 composites for fatigue-resistant structural applications.

ABSTRACT. Integrating additive manufacturing (AM) with continuous carbon fiber-reinforced thermoset composites (CCFRTCs) offers significant potential for producing high-performance, near-net-shape composite structures with tailored mechanical properties. This study explores the development of additively manufactured hybrid fiber metal laminate (FML), combining the lightweight and high strength of brittle CCFRTC with the ductile aluminum metal. Additive deposition of reactive resin-infused fiber tow (ADRRIFT) process is employed to print a composite on a surface-treated metal substrate, with a strong interfacial bond for load transfer. This enables significant increases in design and manufacturing complexity, allowing for faster prototyping of efficient and multifunctional structures. The research investigates processing parameters, fiber-metal adhesion, and mechanical performance under impact loading. The results revealed that the mechanical properties are highly dependent on the fiber architecture, the metal and polymer composite interface, and the interlaminar strength of the polymer composite. FML impacted at 30 J exhibited up to 76% increase in peak load, 13% increase in absorbed specific energy, and 11% increase in density compared to the AM monolithic composite. Furthermore, this work lays the foundation for advancing hybrid metal composites through the ADRRIFT process, enabling the creation of complex geometries with freedom of material and fiber architecture not possible with conventional manufacturing processes.

ABSTRACT. As global demand for satellite deployment into Low Earth Orbit (LEO) and Sun-Synchronous Orbit (SSO) continues to accelerate, new geographic regions are exploring entry into the space launch sector. This study presents a strategic evaluation of Oman as a potential launch site for orbital missions, with a focus on LEO and SSO access. Leveraging Oman’s unique geographic position—situated near the equator with access to expansive coastal regions—the analysis examines key selection criteria, including latitude advantages, range safety, trajectory optimization, logistical infrastructure, and geopolitical stability.

Using a multi-criteria decision framework, we assess optimal launch azimuths for polar and sun-synchronous trajectories, highlight necessary downrange safety corridors over the Arabian Sea, and model orbital insertion efficiencies achievable from potential coastal sites such. Environmental impact assessments and regulatory frameworks are also considered to ensure sustainable development.

Preliminary findings indicate that Oman offers competitive benefits for mid-inclination and near-polar launches, especially for emerging commercial and government-led small satellite missions. The establishment of launch capabilities in Oman could serve regional demand while enhancing national technological leadership in aerospace sectors. This work aims to provide foundational guidance for policymakers, industry stakeholders, and academic researchers involved in developing Oman’s space infrastructure roadmap.

ABSTRACT. This study presents a detailed numerical investigation of the flow physics over SD7062 airfoil at low-to-moderate Reynolds numbers (Re=1.25×105 and 4×105) using the transitional shear-stress transport (SST) γ-Re_θ turbulence model. Two-dimensional incompressible flow simulations are conducted across a wide range of angles of attack (0°–20°) to characterize mean aerodynamic coefficients, laminar-to-turbulent transition locations, and wake structures. The results reveal smooth stall behavior, increasing stall angle (from α=14° to α=15°), lift performance and aerodynamic efficiency (lift-to-drag ratio is doubled) with higher Reynolds numbers. Transition onset is found to shift toward the leading edge with increasing angle of attack and Reynolds number. Two distinct wake regimes, steady vortex sheet and unsteady alternating vortex shedding, are observed.

ABSTRACT. The design of fixed-wing vertical take-off and landing (FW-VTOL) unmanned air vehicles (UAVs) requires careful aerodynamic evaluation of both airfoils and wing geometries to balance lift, efficiency, and stability under varying flight conditions. This study presents a systematic evaluation of 35 airfoils using XFLR5 software, focusing on key aerodynamic parameters such as lift coefficient (Cl), drag coefficient (Cd), lift-to-drag ratio (Cl/Cd), moment coefficient, stall angle, and maximum lift values. Each airfoil is assessed under a range of Reynolds numbers (50,000 to 200,000), angles of attack (0° to 20°), freestream velocities (5-30 m/s), and varying chord lengths. The results revealed significant differences in performance; the S1223 airfoil exhibited the highest maximum lift coefficient, while the E374 showed the lowest. The lift-to-drag ratio is also highest for S1223, highlighting its suitability for low Reynolds number operations typical in small UAVs. Following airfoil selection, further analysis is performed on wing geometries using the NACA 0012 airfoil to explore the influence of taper ratio, aspect ratio, and leading-edge sweep angles on aerodynamic behaviour. Wings with taper ratios of 0.4 and higher demonstrated better stall characteristics and higher lift coefficients, while increasing sweep angles led to a reduction in lift and efficiency, especially at lower Reynolds numbers. The combination of these analyses provides a robust aerodynamic database and practical guidance for UAV designers seeking to optimize FW-VTOL platforms for specific mission profiles. This work contributes to the design methodology of FW-VTOL UAVs by identifying suitable airfoil and wing combinations for efficient performance across a broad range of flight conditions. The study serves as a foundational step toward integrated aerodynamic-propulsion design and will be extended in future work through high-fidelity CFD simulations and experimental validation of selected configurations.

ABSTRACT. This study investigates the aerodynamic performance of fixed-pitch propellers us-ing QBlade, an open-source simulation tool based on Blade Element Momentum (BEM) theory. Four commercial APC propellers—6×4, 6×5, 8×4, and 8×5—are analyzed across a range of advance ratios. The study aims to evaluate QBlade’s effectiveness in estimating performance parameters such as thrust coefficient, power coefficient, and propeller efficiency. Experimental data from the University of Illinois Urbana-Champaign (UIUC) propeller database are used for validation. Results show that QBlade can effectively predict performance trends, with rea-sonable agreement observed between numerical and experimental values. The ef-fect of propeller diameter and pitch on aerodynamic performance is discussed thoroughly.

ABSTRACT. This work presents the aerodynamic analysis of an unmanned aerial vehicle (UAV) model, which has been modified by incorporating a wingtip device, commonly referred to as a winglet. The integration of this feature aims to enhance the overall aerodynamic performance of the aircraft. In order to evaluate its effectiveness, simulations were conducted by ANSYS Fluent and the modified configuration was compared to the original baseline geometry. Through these simulations, key aerodynamic parameters such as lift and drag coefficients, pressure and velocity distributions, and vortex formation were analyzed. This comparison allows for a comprehensive assessment of the improvements achieved through the winglet implementation on the UAV's wing design.

ABSTRACT. Hazardous material storage facilities are indispensable across various industrial sectors, including mining operations, defense manufacturing, research facilities, and construction. Ensuring safe storage of these materials is vital for accident prevention and public safety. However, storing these materials presents a complex optimization challenge, constrained by geometric and capacity limitations, hazard divisions, compatibility groups, regulatory safety standards, temperature, and stacking restrictions. Current practices predominantly rely on human judgment and historical trends, resulting in inefficiencies, delays, frequent adjustments, and elevated safety risks in high-consequence environments. This study aims to enhance the safety and efficiency of hazardous material storage by conceptualizing the problem as a multi-container loading problem, which is an NP-hard combinatorial challenge. The objective is to minimize the wasted space while satisfying a comprehensive array of explicit constraints. To achieve this, a Mixed-Integer Linear Programming (MILP) model is developed that employs preprocessing techniques to address stacking constraints, thereby streamlining the optimization process. The model is progressively refined by incorporating depot-specific limitations, operational needs, and practical storage requirements to enhance real-world applicability and is solved using exact solvers with a rolling horizon approach to boost computational efficiency. The MILP model is evaluated on specially designed synthetic instances that replicate real-world scenarios. Extensive computational experiments demonstrate that the model significantly improves storage efficiency and safety. Furthermore, these experiments elucidate the impact of individual constraints on the container occupancy, runtime, and packing configurations. The mathematically validated decision-support framework provides depot managers with actionable insights for handling high-consequence logistics, thereby enhancing safety compliance, minimizing delays, and optimizing space utilization.

ABSTRACT. Foreign Object Debris (FOD) presents a significant threat to aviation safety, impacting both civilian and military air operations worldwide. FOD refers to any object, whether natural or man-made, that poses a risk of damage to aircraft through ingestion, collision, or interference during ground operations or flight. This paper examines the critical impact of FOD on aircraft performance, operational costs, and passenger safety, highlighting notable incidents where FOD was a contributing factor to accidents and maintenance challenges. This study, while highlighting the current, well-established preventive measures such as regular runway inspections, advanced FOD detection systems, strict personnel protocols, and improved material management practices, are discussed as key strategies for mitigation, it emphasizes the need for novel approaches and improvements to them through emerging technologies to contribute to the evolving landscape of aviation safety. Furthermore, the importance of fostering a strong safety culture among airport authorities, ground crews, and airline operators, is reinforced as a critical element in minimizing FOD-related risks. One of the key contributions of this study is its exploration of identifying latent FOD hazards through cutting-edge technological innovations. For example, paved airfield surfaces dislodging and being ingested into engines and propellers are common risks. Additionally, bird repellents have not been effective in removing bird nests inside hangars, posing a persistent hazard. Has there been an effective method yet to prevent bird strikes, particularly during landing and takeoff? Adopting advanced technologies can significantly enhance the aviation industry's ability to prevent FOD events, ensuring safer skies and more efficient airport operations.

ABSTRACT. Introduction Airworthiness Safety Management Systems (SMS) are essential frameworks designed to enhance safety in aviation operations by systematically managing risks and ensuring continuous improvement. In military aviation, where operational demands are complex and high-stakes, integrating logistics into SMS is crucial for maintaining safety, efficiency, and mission readiness. This research explores the relationship between SMS and logistics in military aviation, emphasising how logistical processes—such as supply chain management, maintenance scheduling, and resource allocation—directly influence safety outcomes. Although logistics plays a crucial role, it often receives lesser priority. This research emphasises the connection between logistics elements and their significance to the SMS in military aviation.

Research Methods This study was conducted as quantitative research, and the inferential statistics were analysed by establishing a null and an alternative hypothesis. The data that was gathered was later analysed to find the Pearson Correlation to determine the relationship among the variables. The statistical significance of the relationship was further analysed using the Chi-square method.

Findings By analysing operational data from a case study of a selected military aviation entity, the study identifies key logistical factors that contribute to safety risks, including delays in parts delivery, inadequate maintenance practices, and insufficient training of personnel. The research also highlights the role of advanced technologies, such as predictive analytics and real-time monitoring systems, in optimising logistics to support SMS objectives. Furthermore, the study examines the organisational and cultural challenges in aligning logistical operations with safety protocols, proposing strategies to foster collaboration between safety and logistics teams.

Recommendations The findings underscore the importance of a holistic approach to SMS in military aviation, where logistics is not merely a support function but a core component of safety management. Recommendations include developing integrated frameworks that synchronise logistical and safety processes, enhancing training programs for personnel, and adopting data-driven tools to mitigate risks. This research contributes to the broader discourse on aviation safety by demonstrating how effective logistics management can enhance the resilience and reliability of military aviation operations, ultimately safeguarding lives and assets in high-risk environments.

ABSTRACT. Fatigue failure remains a critical concern in aerospace structural components. The combined effects of thermal treatment and mechanical processing in engineering components often result in the formation of residual stress fields. While tensile residual stresses can exacerbate existing flaws and reduce component lifespan, com-pressive residual stresses are beneficial as they inhibit crack propa-gation and enhance fatigue life. Shot peening is a widely used sur-face treatment technique aimed at inducing beneficial compressive stresses. This study investigates the effectiveness of shot peening in enhancing fatigue resistance by introducing compressive residual stresses in aerospace-grade 7449 aluminum alloys. Specimens were subjected to shot peening at three intensity levels, followed by residual stress measurements using X-ray diffraction. The experimental results were compared with numerical simulations conduct-ed through Finite Element Analysis (FEA), showing strong correlation. The study demonstrates that optimized shot peening parameters can significantly retard fatigue crack initiation and propagation, thereby offering an effective strategy for fatigue life extension in critical aerospace components.

ABSTRACT. This paper proposes a model where the Spiral Optimization Algorithm (SOA) would be used to optimize the system of minimized carbon emissions as well as energy costs of EV recharging stations. Four different charging modes are presented in the proposed framework: peak, off-peak, stochastic, and Electric Power Research Institute (EPRI) charging modes that represent a variety of different ways of consuming energy and different load patterns. The spiral inspired evolutionary algorithm SOA also provides a light weighted optimization mechanism whereby candidate solutions are repeatedly processed along the path of a spiral motion in an attempt to converge into the global optimum solution. Included in the approach is the smart scheduling techniques that could be used to distribute the load, minimize the load on a power grid and facilitate the functioning of a supply and demand balance, especially under the circumstances when the renewable energy sources are highly penetrated. The model was applied in MATLAB, Simulink, and the simulation study has been done testing it and comparing with those traditional optimization techniques.

ABSTRACT. In recent years, there has been an increasing focus on the integration of artificial intelligence (AI) to Unmanned Aerial Systems (UASs) for improved health monitoring and fault detection system. This paper presents a hybrid deep learning architecture based on Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) networks trained with synthetic sensor data including accelerometer readings, thermal behavior, voltage patterns, and communication anomalies for fault detection. This study aims to develop a modular framework for fault detection that can be simulated within the context of software-defined avionics so that it may become possible to implement and test. To simulate realistic subsystem anomalies, a number of fault injection events were incorporated into the simulation, including thermal spikes, voltage drops, and increased packet losses. A time-series windowed dataset was created, and the proposed CNN-LSTM model was trained and validated using a 70:30 train-test split, achieving a test accuracy of 98.5% with a corresponding area under the ROC curve (AUC) of 0.99, demonstrating high level of classification performance. A true positive rate of 96% for fault detection was found in the confusion matrix, suggesting the model's resilience in identifying unusual activity. This study offers a new insight into the use of interpretable artificial intelligence in avionics systems and suggesting that the simulation-based framework proposed could enhance both diagnostic accuracy and lead to more reliable and transparent modular avionics architectures.

ABSTRACT. The rapid development of Advanced Air Mobility (AAM) presents significant safety challenges in integrating Unmanned Aerial Systems (UAS) into shared airspace. Ensuring safe and efficient operations requires advanced navigation, communication, and collision avoidance technologies to support the coexistence of manned and unmanned aircraft. This study examines key safety challenges in AAM, including airspace management, real-time situational awareness, and regulatory compliance.

The rapid emergence of Advanced Air Mobility (AAM) introduces critical safety challenges in integrating Unmanned Aerial Systems (UASs) into shared and in-creasingly congested airspace. Safe and efficient operations require robust solu-tions in navigation, communication, and collision avoidance to enable the coexist-ence of manned and unmanned aircraft. This paper investigates key safety con-cerns in AAM, including airspace management, real-time situational awareness, and regulatory compliance, with a particular focus on navigation safety. To ad-dress these challenges, we present a case study on the application of Visual Simul-taneous Localization and Mapping (Visual SLAM) for UAS navigation. Leverag-ing onboard cameras and AI-based feature tracking, Visual SLAM offers a resili-ent alternative to GPS, especially in GPS-denied or signal-degraded environments. Our evaluation across multiple operational scenarios demonstrates the effective-ness of this approach in improving navigation accuracy, obstacle detection and avoidance, and autonomous flight safety within AAM ecosystems. Furthermore, we explore the integration of Visual SLAM with Air Traffic Management (ATM) and UAS Traffic Management (UTM) systems, emphasizing interoperability and computational efficiency as critical enablers of large-scale deployment.

ABSTRACT. The project Real Time Tracking and Environment monitoring device for Outdoor Activities provides a comprehensive solution to enhance safety and connectivity for outdoor activities. It uses GPS technology for real-time sharing of location with teammates within the network. Location sharing with other members can be done using Radio Frequency technology. It enables users to share their location without relying on the cellular network and WIFI because remote areas face connectivity issues. Moreover, environment monitoring sensors are used to monitor temperature and atmospheric pressure which is used for local weather prediction. This system also features an Android Application, the Application has several tabs including the Track Location tab. This Tab displays the location of each user in the network on the map without Wi-Fi. Bluetooth low energy is used for data transmission between the device and android application. Weather Prediction is also displayed on the Android application. Moreover, the application also has an SOS Button which provides an extra layer of security for users and an SOS alert is displayed on the Application of each user by pressing the SOS button. It allows users to send SOS alerts in case of emergencies such as sudden storms. This device enhances safety and connectivity for outdoor Enthusiasts such as Hikers, Military operations in remote areas, and rescue operations

ABSTRACT. A blockchain is essentially a distributed database of records, or public ledger of all transactions or digital events that have been executed and shared among participating parties. Each transaction in the public ledger is verified by consensus of a majority of the participants in the system. Once entered, information can never be erased (Ølnes et al., 2017). The blockchain con-tains a certain and verifiable record of every single transaction ever made. Bitcoin, the decentralized peer-to-peer digital currency, is the most popular example that uses blockchain technology. The blockchain technology has worked flawlessly and found wide range of applications in both financial and non-financial world. The main hypothesis is that the blockchain establishes a system of creating a distributed consensus in the digital online world (Crosby et al., 2016a). This allows participating entities to know for certain that a digital event happened by creating an irrefutable record in a public ledger (Pournaras, 2020). It opens the door for developing a democratic open and scalable digital economy from a centralized one. There are tremendous opportunities in this disruptive technology, and the revolution in this space has just begun (Denis et al., 2020). This article aims to evaluate and critical-ly analyze the improvement in network delays in supply chain of Aviation Maintenance Organization operations. Moreover, this paper also evaluates and compares increase in efficiency in terms of exchange of data / infor-mation between nodes and clients (stakeholders) of an Aviation sector organ-ization.

ABSTRACT. Progress in power electronic-based converters is critical for developing high-power, cost-effective, and reliable charging solutions for electric vehicle (EV) batteries, particularly as the adoption of EVs continues to rise. This study introduces an innovative isolated hybrid three-port converter that integrates a single-phase full-wave bridge rectifier, a series resonant converter (SRC), and a dual active bridge (DAB) converter to achieve these objectives. The proposed three-port configuration reduces the number of components, leading to lower system costs and simplified design. Additionally, the inclusion of a multilevel rectifier (MLR) at the input stage enhances the input voltage waveform quality and reduces voltage stress on power switches. By incorporating fuzzy logic, the total harmonic distortion is significantly reduced to 0.19%. A simplified decoupled control strategy, utilizing pulse width modulation (PWM) and phase shift techniques, is implemented to manage power flow across all three ports simultaneously. The system is modelled and simulated in MATLAB/Simulink, with experimental validation conducted in a laboratory environment.

ABSTRACT. Strategic autonomy in space is a contemporary concept in the evolution of modern space powers. The notion of space being a cost-intensive endeavour has transformed into sustainable space through amalgamation of space-technology and space-economy into space ecosystems. Hence strategic autonomy in space now rests on four pillars, that of space-technology, -policy, -infrastructure and -collaborations. Nascent space powers are nations or entities that are pursuing indigenous development of space capabilities albeit they are posed with unique circumstances and must navigate through challenges that could be technological, economic, and geopolitical. This implies that nascent space powers including Pakistan need to work on developing space policy and regulatory frameworks that nurture the growth of indigenous space ecosystems for subsequent development of spacecraft and launch system, as well as space explorations while actively collaborating with international partners but without undue reliance on external entities. Thus, for Pakistan as well as nascent space powers in general, the question as to how strategic autonomy is attainable in space operations is becoming increasingly paramount. This research identifies the 4Ms strategy – Money, Mining, Military and Morphing as the key domains in which space technology readiness levels must be attained apriority. The strategy is implementable through a multi-sectoral regulatory framework. Moreover, implementing a bottom-up technology development approach vis top-down will be a more sustainable path for the development of autonomous space capabilities. Given the relentless nature of space contestation in the new space economy, the most plausible way by which nascent space powers including Pakistan can survive and thrive in this era, is by incubating a multi-sectoral space economy with adequate incentives and regulatory support for selectively entering into strategic collaboration with major space powers with the overall objective of enhancing the technology readiness levels for indigenously developing dual-use and ubiquitous space technologies and services.

ABSTRACT. Friction Stir Additive Manufacturing (FSAM) is a layer-by-layer solid-state AM process derived from friction stir lap welding. By joining metal plates below melting, FSAM produces fine equiaxed microstructures with superior mechanical properties and minimal melt-related defects (such as porosity or solidification cracks) compared to fusion-based AM. However, the process’s thermal–mechanical lap-weld character also introduces its own defect spectrum. Major defect classes reported in the literature include geometrical distortions (macroscopic misalignment or unbonded side edges), surface irregularities (e.g. flash, roughness, wormhole marks), internal flaws (e.g. interlayer voids, tunnels and “kissing” bond cavities), and metallurgical anomalies (e.g. incomplete bonding or oxide entrapment between layers). The review critically examines these FSAM defects – discussing their root causes and how they are detected or characterized. Defect detection in FSAM relies on both destructive techniques (cross-sectional microscopy and mechanical testing) and nondestructive methods (including ultrasonic scanning and X-ray computed tomography). Recent studies highlight several mitigation strategies aimed at minimizing defect formation. These strategies include optimizing tool geometry, fine-tuning process parameters, and applying enhanced axial force or post-weld forging to improve material consolidation and structural integrity.

ABSTRACT. Particle image velocimetry (PIV) has become a sophisticated technology utilised in both research and industry to measure flow properties in a non-intrusive manner. However, the cost remains prohibitive for students to implement this method in most undergraduate aerodynamics laboratories. Furthermore, the high-powered lasers typically employed in PIV systems can pose a safety hazard in the presence of large groups of students. This paper proposes a cost-effective PIV design and analysis system that can be readily implemented in aerodynamics laboratories. The system utilises a low-power, stationary laser light source to reduce expenses and enhance laboratory safety. An open-source analysis code is employed to further reduce costs. In laboratory exercises with this system, students will grasp the PIV data collection process, apply MATLAB to analyse the data, and describe the observed flow characteristics. The paper also provides detailed information on the system, with the aim of enabling others to construct similar systems for use in their own laboratories. Example applications for pumping liquid in a mini-pool and visualisation of the jet flow of a synthetic jet actuator are applied as applications of the PIV system at the end of the study.

ABSTRACT. An innovative and sustainable water treatment system that synergizes mechanical filtration through a vertically rotating disc with ultraviolet (UV) light disinfection is utilized, advancing traditional biological treatment methods. A rotating disc enhances particle removal while a UV chamber effectively disinfects the water, avoiding the implementation of any chemical product, thus promoting an eco-friendly solution with low operational costs. Computational Fluid Dynamics (CFD) simulations using ANSYS Fluent confirmed the significant impact of rotational speed on film thickness and filtration efficiency, validating the core design of the filtration system. The system has been significantly enhanced to integrate real-time chemical monitoring through pH sensors. If the treated water does not meet predefined safety standards, an intelligent feedback mechanism, powered by a motorized return system, redirects the water back for additional filtration and disinfection. This closed-loop feature minimizes waste, ensures consistently high-water quality, and reduces dependency on chemical treatments. Human intervention is minimized through implementation of AI-embedded monitoring technologies, optimizing the system autonomy. The innovative water treatment system demonstrates a practical application of smart technologies to support water security, a national priority under the Oman 2040 Vision’s environmental sustainability pillars. The design’s modularity, affordability, and environmental sensitivity make it a compelling solution for both local and global water treatment challenges. By offering a chemical-free, energy-efficient, and intelligent approach to water purification, this research sets a new benchmark for sustainable innovation in the water treatment sector.

ABSTRACT. In this study, artificial neural network (ANN) applications were used to pre-dict propulsive properties of small propellers utilizing experimental data and detailed geometry information of the propellers. Scaled conjugate gradient (SCG) algorithm was used in training of the ANN. The amount of geometry information of the propellers were varied as input to the algorithm and it was shown that more geometry information results in better prediction such that the mean relative error of efficiency prediction was reduced from 9.21% to 4.36%. Input layer has diameter, pitch, RPM, advance ratio (J), chord and twist distributions. Hidden layer has neurons number varying from 1 to 100. Output layer has thrust coefficient, power coefficient and efficiency. High coefficient of determination (R2) values were obtained around 0.99 and low percent errors obtained around 1.8% which suggests that ANN applications are useful tools in predicting propeller parameters and designing for propeller driven air vehicles.

ABSTRACT. Computational Fluid Dynamics (CFD) analysis traditionally depends on manual interpretation of complex flow patterns through methods which are both subjective and time-consuming and require extensive domain expertise. This research presents an innovative framework that synergizes synthetic physics-informed CFD data generation with explainable deep vision models to enable automated flow pattern classification. The study constructs a com-prehensive synthetic dataset using mathematical models that replicate realis-tic fluid flow behaviors across three regimes: laminar flows (Re: 2000) characterized by sinusoidal functions yielding smooth parallel streamlines, turbulent flows (Re: 2000) modeled with multi-scale chaotic and stochastic com-ponents, and separated flows exhibiting recirculation zones with exponential decay properties. Being aligned with established fluid mechanics principles, this physics-informed approach facilitates controlled parameter adjustments. The framework utilizes ResNet-50 as convolutional neural networks (CNN) attaining a test accuracy of 93.83%, and ViT-Base vision transformers achieving a test accuracy of 99.33%, to interpret velocity and vorticity field visualizations for flow pattern classification, enhanced by the Explainable Artificial Intelligence (XAI) technique Grad-CAM, which provides visual explanations to ensure model reliability. This approach can significantly benefit aerodynamics by improving the prediction of airflow behavior around air-craft. Furthermore, it offers potential for real-time aerodynamic analysis, supporting the development of more efficient and safer aviation technologies.

ABSTRACT. In 2022, the department introduced structured formative assessments into the Engineering Mathematics module to enhance student learning and exam readiness. Early implementation showed promising improvements in student engagement and performance. However, subsequent misuse of artificial intelligence (AI) platforms by students to complete these assessments raised concerns about authenticity and the actual learning achieved. This study explores the correlation between formative assessment scores and summative exam performance, focusing on the impact of AI use. Results reveal a growing discrepancy between formative success and summative outcomes, highlighting the urgent need to re-evaluate assessment strategies in technology-rich learning environments.

ABSTRACT. In the context of aviation industry, No Fault Found (NFF) is phenomena that develops from a pilot experiencing a fault but post-flight checks fail to reproduce the reported fault. Components declared as ‘NFF’ are evidence that a serviceable component was removed, and attempts to troubleshoot the root cause have been un-successful. The occurrence of NFF remains a significant operational challenge, contributing to unnecessary maintenance actions, increased operational costs, and resource wastages. Previously six paradigms (System Design, Fault Diagnosis, Reliability Engineering, Data Management, Human Factors & Economic Analysis) of NFF have been explored, but implications of NFF (lost man hours, maintenance cost, packaging / handling costs, machine down time and transportation cost) etc. have not been interlinked with lean / waste factors (defects, over production, waiting, non-utilized talent, transportation, inventory, motion & extra processing). This study proposes seventh paradigm of “Lean Framework” by undertaking a comprehensive industrial assessment of NFF phenomenon at Aviation Maintenance, Repair, and Overhaul (MRO) sector through a structured research process comprising a detailed literature review, an industrial survey for gap analysis, the development and dissemination of a targeted questionnaire, and subsequent quantitative data analysis. Based on responses from 50 aviation maintenance professionals, and with a survey reliability confirmed by a Cronbach’s Alpha value of 0.801, the study identifies the key eight lean wastes attributable to NFF events. High mean scores across all waste categories highlight resource wastages resulting from NFF events. In response to these findings, human centered six soft lean practices have been proposed as effective solutions: leadership commitment, supplier collaboration, team-based problem solving, continuous process improvement, technical personnel training, and stakeholder feedback system. Data analysis revealed strong endorsement of the proposed soft lean practices, emphasizing the urgent need for systematic intervention. The results highlight that addressing NFF requires not only technical diagnostic improvements but also strategic organizational and behavioral changes. The proposed lean framework offers a practical, scalable approach for Aviation MRO organizations aiming to reduce NFF wastes, optimize operational performance, and promote a culture of continuous operational excellence.

ABSTRACT. The aviation industry's unwavering commitment to safety hinges on a robust maintenance culture. This article explores the multifaceted nature of maintenance culture within aviation, highlighting its critical role in ensuring airworthiness and preventing unwanted outcomes. A strong maintenance culture transcends mere adherence to regulations; it promotes a proactive, safety-conscious environment where every individual, from mechanics to management, prioritizes quality and vigilance. Central to this culture is a commitment to continuous improvement, driven by rigorous inspections, meticulous documentation, and comprehensive training. Effective communication and a transparent reporting system are essential, allowing for the rapid identification and resolution of potential issues. Furthermore, strengthening a "just culture" encourages the reporting of errors without fear of reprisal, enabling valuable lessons to be learned and systemic weaknesses to be addressed. The integration of advanced technologies, such as predictive maintenance and digital record-keeping, further enhances the efficiency and reliability of maintenance operations. These technologies facilitate data-driven decision-making, allowing for the early detection of anomalies and the optimization of maintenance schedules. However, the human element remains paramount. The cultivation of a positive safety climate, characterized by trust, respect, and shared responsibility, is crucial for maintaining high standards. This necessitates strong leadership, which champions safety as a core value and empowers employees to actively participate in safety initiatives. Ultimately, a thriving maintenance culture in aviation is a dynamic and evolving system, constantly adapting to new challenges and technologies, ensuring the continued safety and reliability of air travel.

ABSTRACT. The demand for higher efficiency and reduced weight in electric machines has driven designers to explore advanced materials and innovative assembly techniques. One such method is shrink-fitting, which offers advantages over traditional mechanical joints such as keyways, particularly in reducing overall weight and complexity whilst maintaining sufficient holding torque or axial resistance to load through friction. In electric machines, shrink-fitting can be applied between the motor housing and the stator to provide a structurally integral assembly. This method also enhances thermal conductivity and heat dissipation though the increased stressed on the stator can interduce higher iron losses. This paper investigates the mechanical effect of the shrink-fit process on stator materials, with a focus on understanding the frictional holding torque as first stage in the overall optimization of shrink-fit of stators in electric machine housing. Several physical parameters are experimentally examined, including material properties, surface topography, coefficient of friction at the interface under various pressure, and the effects of manufacturing processes such as wire EDM, grinding and punching

ABSTRACT. SkyScan is a UAV-based traffic monitoring and surveillance system designed to overcome the limitations of traditional methods, such as blind spots, limited scalability, and environmental constraints. This work enhances real-time object detection by proposing a modified YOLOv5n architecture, tailored for the VisDrone dataset. Key improvements include the addition of a fourth detection scale (P6/64), integration of a Convolutional Block Attention Module (CBAM), and deeper C3 layers to boost feature extraction and fusion. To further refine detection accuracy, especially for small and overlapping objects, SkyScan incorporates the Slicing Aided Hyper Inference (SAHI) technique in the post-processing stage. Optimization techniques such as quantization and resource-efficient inference are also employed. Comparative evaluation with YOLO variants (YOLOv5n, YOLO7n, YOLO12n) demonstrates that the enhanced model achieves a notable increase in mAP@0.5, reaching 0.35 compared to 0.30 from the baseline YOLOv5n. SkyScan ensures real-time object detection, tracking, and density estimation, offering a scalable and efficient solution for smart city surveillance with improved detection reliability

ABSTRACT. Real-time 3D mapping and localization for Unmanned Aerial Vehicles (UAVs) performing in-flight inspections of civil aviation infrastructure is crucial for ensuring safety, reducing downtime, and enabling predictive maintenance. However, existing SLAM frameworks such as ORB-SLAM3 and NICE-SLAM struggle with the dynamic conditions encountered during UAV flight, including rapid viewpoint changes, moving obstacles, and non-rigid scene elements. We propose DMN-SLAM-UAV, a lightweight neural-implicit SLAM system tailored for UAV inspection tasks that integrates a real-time dynamic-object segmentation front end, a hierarchy of five compact MLP decoders for multi-scale geometry and appearance residuals, and an incremental octree (i-Octree) for efficient map storage. The i-Octree enables sparse, log-time updates, allowing onboard execution on an NVIDIA Jetson Orin with an average per-frame latency of 48 ms. We evaluate DMN-SLAM-UAV on a newly curated Dataset of Aerial Inspection Sequences (DAIS)—20 flight trajectories over runways and fuselage sections—and benchmark against ORB-SLAM3 and NICE-SLAM. Our approach reduces absolute trajectory error (ATE) by 37.6% and improves reconstruction completeness by 23.4% compared to ORB-SLAM3, while achieving 42% faster mapping updates than NICE-SLAM. Ablation studies confirm the contributions of dynamic masking and the multi-MLP architecture with statistical significance (p < 0.01). Furthermore, we release the DAIS dataset and our open-source implementation to foster future research. This work paves the way for robust, scalable UAV-based inspection solutions in civil aviation and beyond.

ABSTRACT. This paper introduces CMI4.1, an enhanced and data-driven smart maintenance maturity framework designed to advance the implementation of Condition-Based Maintenance (CBM) and improve the estimation of Remaining Useful Life (RUL) of mechanical components, specifically within the context of Pakistan’s aircraft manufacturing sector. The proposed model builds upon the previously published CMI4.0, which was developed by the same authors as a localized adaptation of the IMPULS Industry 4.0 readiness model by the German Mechanical Engineering Association (VDMA) and refined through expert consensus using the Delphi method. CMI4.1 expands the scope of its predecessor by incorporating additional enablers aligned with intelligent manufacturing environments, with a strong focus on digitalization, data integration, and AI readiness. The framework leverages machine learning and deep learning algorithms to analyze high-dimensional sensor data, enabling predictive maintenance, early anomaly detection, and real-time decision-making. Empirical validation within industrial settings confirms that CMI4.1 enhances CBM readiness assessments, minimizes unplanned downtime, improves operational reliability, and offers a scalable roadmap for smart maintenance transformation in emerging economies.

ABSTRACT. This article presents a comparison of two ultrasonic tightening control methods for evaluating the tightening state of preloaded bolted assemblies. The first method enables relative monitoring of clamping force evolution over time. The second method is based on coupling an Electromagnetic Acoustic Transducer (EMAT) with a piezoelectric sensor. This hybrid configuration combines the non-contact generation capability of the EMAT with the high sensitivity of the piezoelectric sensor. It relies on measuring the variation in ultrasonic wave propagation time to estimate bolt elongation and preload in an absolute manner. A major challenge addressed is the effect of temperature, which influences ultrasonic velocity. A compensation strategy is developed to correct these temperature-induced variations and ensure accurate tightening assessment. This approach enhances inspection flexibility and allows reliable monitoring without disassembling the joint.

ABSTRACT. Aircraft wings are traditionally designed for optimal performance within a narrow flight regime, resulting in aerodynamic inefficiencies under off-design conditions. Morphing wing technology offers a transformative approach by enabling dynamic shape adaptation during flight, thereby improving aerodynamic efficiency, fuel economy, and operational versatility. The increasing demand for enhanced aircraft performance and fuel efficiency has motivated the exploration of advanced morphing wing technologies. This paper reviews disruptive morphing wing topologies that offer significant deviations from conventional designs, enabling unprecedented aerodynamic performance. The review identifies a gap in the comprehensive analysis of novel morphing concepts, focusing on their potential for radical performance improvements. A systematic literature review was conducted, analyzing studies on unconventional morphing wing designs, actuation mechanisms, and control strategies. The main finding is that several disruptive topologies, such as continuous span morphing and 3D wing shape adaptation, demonstrate the potential for substantial aerodynamic benefits, including lift enhancement, drag reduction, and improved maneuverability. The principal conclusion is that these advanced morphing wing concepts hold promise for future aircraft designs, offering the potential to overcome the limitations of traditional fixed-wing configurations.

ABSTRACT. Over the past decades, the investigation of wave transmission through peri-odic structures has gained interest among researchers, particularly in analyz-ing frequency band gaps, which have applications in vibration isolation, fre-quency filtering, etc. Tensegrity structure, a self-equilibrated network of pre-stressed tension and compression elements, enables geometry-driven dynam-ic control of wave transmission that overcomes previously manufactured-fixed structures and finds growing applications in civil engineering, robotics, space technology, etc. Motivated by this feature, the present study focuses on the numerical study of tunable wave transmission in periodic one-dimensional tensegrity architectures. First, a pre-stressed controlled two-dimensional planar tensegrity structure has been chosen for the analysis. Next, a stable equilibrium configuration was obtained using the minimiza-tion principle of its total potential energy by solving a nonlinear optimiza-tion solver. Nonlinear, followed by linearized equations of motion, were de-rived using the Lagrangian approach, and their responses are validated with the dispersion relations obtained by solving the Floquet-Bloch method under impulse load conditions. Two modes of wave transmission, i.e., symmetric and anti-symmetric modes, were identified and analyzed separately. The var-iation of band gaps over frequencies for each wave transmission mode has been analyzed in detail in a non-dimensional framework.

ABSTRACT. Drones, or Unmanned Aerial Vehicles (UAVs), have emerged as a revolutionary technology with diverse applications across industries due to their versatility in tasks deemed unsafe, inefficient, or impractical for humans. Rotary-Wing UAVs (RWUAVs), with their Vertical Take-Off and Landing (VTOL) capabilities, offer superior maneuverability in confined spaces compared to fixed-wing designs.

Drones are transforming military, civil, and commercial sectors. Military applications include Intelligence, Surveillance, and Reconnaissance (ISR) missions, enhancing battlefield awareness without risking human lives. In the civil sector, drones provide innovative solutions for monitoring, mapping, and inspection, improving productivity and safety. The integration of AI, autonomy, and improved battery and communication systems is expected to further expand drone applications. Projections indicate significant growth in commercial drone usage, including urban air mobility and express deliveries. The future of drones lies in their potential to reshape global interaction efficiently and sustainably.

However, the increasing use of drones necessitates assessing operational risks, particularly in adverse weather. Atmospheric/in-cloud icing poses a critical challenge in cold regions like the Arctic, where supercooled water droplets freeze on drone surfaces. This phenomenon significantly impacts performance, safety, and reliability. Drones are more susceptible to icing than manned aircraft due to lower operating speeds, higher humidity at low altitudes, and limited power. Therefore, the impact of ice accretion on drones requires specific examination.

Despite the broad applications of drones, research on ice accretion physics and its impact, especially on rotary-wing configurations, remains limited. This presentation will focus on the design and operation of drones in icing conditions and how icing affects their aerodynamic performance under various environmental and geometric factors.

ABSTRACT. Stop drill-hole has been used extensively in the past by researchers and engineers, particularly for aero-structures, to extend the fatigue life in those metallic specimens or structures that contain a crack. Indeed, drilling a hole in the tip of a sharp crack turns the crack into a round-tip notch resulting in a significant reduction in stresses around the initial crack. In this paper, an innovative method is introduced according to which instead of a single hole, two holes are drilled at the crack tip. The main purpose of using double stop-hole method is to reduce the stress singularity at the crack tip further and also to reduce the stress concentration in the vicinity of the stop holes in the cracked structural elements. The fatigue crack growth retardation is examined in this research both experimentally and numerically to explore the efficiency of the double-stop drill hole method. Different geometry parameters are considered in the finite element simulations to find the favorite positions of the two holes relative to the crack tip for better efficiency. Based on the finite element results, single edge-notch tension specimens made of an alloy steel are subjected to fatigue loading and the experimental results are compared for three different cases: plain specimens, specimens containing only a single hole and specimens possessing two holes. The numerical and experimental findings both reveal that the fatigue life enhancement resulted from the double stop-hole method is meaningfully larger than the that of the classical single-stop hole method. According to the finite element and experimental results obtained in the present research, one can suggest that the double stop-hole method can be considered as an efficient, inexpensive and simple technique for enhancing the fatigue life in cracked structures.

ABSTRACT. As global industries increasingly prioritize sustainability and performance, the development of advanced surface coatings plays a vital role in achieving greener and more efficient systems. This keynote introduces a novel approach through Liquid Additive Manufacturing (LAM) using hybrid materials derived from renewable resources. Specifically, the focus is on hybrid coatings composed of environmentally friendly silicon carbide (eSiC) extracted from rice husk waste, combined with graphene oxide (GO), applied onto titanium alloy material. This hybrid coating not only offers superior tribo-mechanical properties—such as high hardness, wear resistance, and reduced friction, but also addresses circular economy principles by recycling agricultural waste. The LAM approach, with its lower energy and equipment costs compared to conventional methods like CVD or PVD, provides a scalable, industry-friendly solution. This talk will highlight the promise of LAM, development of GO-eSiC hybrid coatings, material performance, and real-world applicability of this coating technology.

ABSTRACT. Aircraft line maintenance plays a critical role in ensuring flight safety, regulatory compliance, and operational efficiency, especially within the high-frequency, cost-sensitive environment of low-cost carriers (LCCs). As LCCs rely heavily on tight schedules and minimal ground times to maintain profitability, the effective scheduling and execution of line maintenance tasks during aircraft turnaround has emerged as a key area of research and operational innovation. This paper presents a comprehensive and systematic review of the hybrid optimization approaches developed and applied over the past two decades (2005–2025) to improve line maintenance scheduling with the explicit goal of minimizing aircraft turnaround time (TAT). The review encompasses over 100 academic and industrial sources, categorizing and analyzing contributions across three major theoretical domains: dynamic programming, heuristic/metaheuristic techniques, and machine learning–driven predictive analytics. It further investigates how hybrid models—combinations of algorithmic approaches—have evolved to address the inherent complexity, uncertainty, and real-time demands of line maintenance environments. In parallel, the review assesses the adoption of these models in industrial settings, highlighting successful applications, limitations, and scalability issues faced by major LCCs worldwide. A structured methodology was adopted to select, classify, and evaluate literature based on relevance, scientific rigor, and practical implications. Key performance indicators, such as reduction in TAT, resource utilization, task delay mitigation, and system robustness, are synthesized across studies using comparative tables and graphical summaries. This paper not only maps the chronological evolution and interdisciplinary convergence of hybrid optimization techniques in aviation maintenance but also identifies research gaps, including limited integration with real-time operational control systems, underuse of historical reliability data, and insufficient consideration of human factors in scheduling models. The review concludes with a set of forward-looking recommendations for academics and industry practitioners, advocating for the integration of predictive maintenance data, reinforcement learning, and digital twin environments in future line maintenance optimization research. Overall, this work contributes to the growing field of intelligent aviation operations by consolidating a fragmented body of knowledge and providing actionable insights for reducing TAT and enhancing maintenance decision-making in low-cost airline operations.

ABSTRACT. Machine learning (ML) is undeniably turning into a mainstream idea since its primary purpose is to enhance a system’s throughput by allowing a more intelligent usage of materials, processes and managing their resultant properties. In industrial applications, usage of ML not only decreases the lead time of the manufacturing process involved but also increases the quality and properties of the parts produced. Similarly, ML gives us an opportunity of creating completely or partially autonomous frameworks. Similar benefits are provided by additive manufacturing (AM) in the manufacturing regime as it too offers a unique combination of freedom of design, ultimate control over process parameters and eventual quality of a resultant product. AM involves the creation of parts in 3D by melting metallic powders in accordance with a 3D model. This talk discusses the utilization of machine learning techniques in various areas of additive manufacturing regime ranging from material selection and alloy development to parameter optimization, establishing process structure-property relationship and defect detection. Consecutive steps of the process i.e. data gathering, population establishment, model selection, training and application have been discussed. In the end, certain challenges associated with the long-term incorporation of machine learning in additive manufacturing and their probable solutions have been discussed.

ABSTRACT. Accurate prediction of high-Reynolds-number wall-bounded turbulent flows is essential for the understanding and flow control of many engineering applications such as aircraft, turbomachinery, and marine vehicles. Additionally, most practical flows exhibit nonequilibrium effects such as pressure gradient, flow separation, and mean three-dimensionality. However, the direct numerical simulation (DNS) of high-Reynolds-number wall-bounded turbulent flows is not feasible owing to the prohibitive computational cost of resolving small-scale eddies near the wall. Wall-modeled large-eddy simulation (WMLES) presents an affordable predictive alternative to the DNS via the approximate modeling of flow physics near the wall (through a wall model) while resolving the outer scales directly on the computational grid. In this work, we focus on two aspects of wall models: (i) development and implementation of new/existing wall models, and (ii) application and comparison of different wall models in various nonequilibrium turbulent boundary layers. In the first part, we develop a novel spectral formulation for the ODE equilibrium wall model, showing its superior efficiency to the traditional approach. Furthermore, we extend the integral nonequilibrium wall model to an unstructured grid LES solver. In the second part, we explore three wall models with varying degrees of computational complexity and physical fidelity, to assess their performance in two controlled but reasonably realistic nonequilibrium flows over a flat plate. The first flow features a turbulent boundary layer undergoing a series of complex pressure gradient effects, while the second exhibits turbulent flow separation induced by suction and blowing. In the latter case, the more complex model clearly exhibits superior wall shear stress predictions. However, the same is not necessarily true in the former case, highlighting the need to adapt the existing wall models to different flow physics by modifying their underlying formulation or assumptions. Finally, a physic-based decomposition of skin friction, that shows separable contributions from various physical processes in the flow, is employed to explain the differing mechanisms of success/failure of wall models in different flows.