Whirl flutter is a stability problem that arises from the interaction of aerodynamic, inertial, elastic, and gyroscopic forces and moments generated by rotating propellers or rotor blades. While efforts to prevent whirl flutter are typically addressed during the design phase through analyses and testing, additional active control methods can also be employed to maintain stability during flight. In this study, an active whirl flutter suppression system was implemented using the aircraft’s existing aileron and rudder control surfaces, without the addition of any extra hardware. For this purpose, numerical simulations were conducted using a six-degree-of-freedom dynamic model of an unmanned model aircraft, based on Reed’s fundamental whirl flutter equations and incorporating a twin-engine propulsion system. The model includes a PID-based control algorithm that receives angular velocity feedback from rotors and generates corrective deflection commands for the aileron and rudder. Simulations were performed for two actuator configurations with nominal (450°/s) and low (200°/s) rate limits. The results showed that the critical whirl flutter speed, initially 58.3 m/s, increased to 66.77 m/s in the nominal scenario and to 66.45 m/s in the low-rate scenario.
The efficient deployment of drones to establish an effective communication network is a challenging problem in a variety of use cases, from disaster management to rural coverage. In this study, we present a hybrid optimization strategy using Ant Colony Optimization (ACO) and Deep Reinforcement Learning (DRL) for optimizing drone placement and mobility in a target geographic area. The proposed method leverages ACO's global search capability coupled with DRL's adaptive learning capability for optimizing network coverage and guaranteeing optimal connectivity among drones and a central hub. The hybrid technique is contrasted with a solo ACO approach, with the former exhibiting superior performance regarding coverage, connectivity, and deployment efficiency.
The widespread adoption of smartphones has made them increasingly vulnerable to malware, posing significant risks to the security of sensitive user information. This work investigates the detection of Android malware using advanced machine learning and deep learning techniques. We use the CIC_MALDroid2020 dataset,containing 11,598 samples,to evaluate the performance of various models on binary and multi-class classification tasks. A Genetic Algorithm (GA) is applied to optimize both feature selection and hyperparameters for the machine learning models. The machine learning algorithms evaluated in this work include XGBoost, Random Forest, and Bagging Classifier, while the deep learning models include CNN, LSTM, GRU, and ANN. Additionally, we studied the effects of handling class imbalance in the dataset using the SMOTETomek algorithm. The experimental results show that the Random Forest model achieves the highest accuracy of 99.15% in binary classification. Overall, this study confirms the effectiveness of machine learning and deep learning techniques in Android malware detection and highlights the critical role of dataset balancing and hyperparameter optimization in achieving high detection performance.
The aviation industry is increasingly seeking alternative fuels to reduce its reliance on fossil fuels and mitigate environmental impacts. Hydrogen emerges as a promising candidate due to its high energy content and zero carbon-based emissions. This study evaluates hydrogen's thermodynamic performance in aircraft engines, comparing it with conventional kerosene. Results show that hydrogen eliminates 𝐶𝑂2and 𝐶𝑂emissions entirely, while reducing 𝑁𝑂𝑥emissions by 83%. Energy efficiency improves from 69% to 71%, and exergy efficiency increases from 34% to 40%. Additionally, hydrogen demonstrates a 29% reduction in specific fuel consumption (SFC). Environmental assessments reveal significant decreases in global warming potential (GWP) and environmental impact indices. However, challenges such as storage volume, combustion chamber redesign, and material adaptations are noted. The findings highlight hydrogen's potential as a cleaner, more efficient alternative for sustainable aviation, provided technical hurdles are addressed.
Vertical take-off landing (VTOL) drones have been widely used in recent years, and many unique configurations have emerged thanks to their design flexibility. Some of these are over-actuated systems. Therefore, control solution is not unique. Moreover, control channels and effectors (i.e., actuators) may not be directly related similar to the conventional aircrafts. Control allocation (CA) becomes complex and critical for over-actuated and coupled control dynamic systems. Considering this, a CA block is designed for a coupled dynamic and over-actuated hexacopter VTOL on top of a base linear quadratic regulator(LQR) controller. CA aims to improve stability characteristics of LQR controller in case of actuator saturation. Therefore, a constrained optimizationproblem is solved to allocate the limited control authority properly. CA is tested via stimulating realistic actuator saturations, andit is observed that CA significantly improves the disturbance rejection characteristics in case of actuator saturation.
The laser guided bombs aresemi-active laser-guided precision strike munitions, designed to carry out attacks with high accuracy directed at the target. The laser guidance systemsilluminate the target precisely, ensuring high accuracy for both stationary and moving targets, thereby reducing civilian casualties and collateral damage. Additionally, these munitions offer flexible usage options, provide cost-effective solutions, and allow the operator manual control over the target. In laser-guided bombs, the effect of increased range on the impact angle can make it harder for the bomb to approach the target at the correct angle, while the effect of laser designation time on the impact angle and target deviation directly determines accuracy and targeting precision. In this study, analyses were conducted for the two scenarios mentioned, and the results were presented. It was observed that for the firing conditions considered, a 1 km increase in range led to approximately a 25% reduction in the impact angle. No significant trend was observed regarding the effect of increasing or decreasing the laser designation time on target deviation and impact angle.
As technology progresses, unmanned aerial vehicles (UAVs) have gained considerable popularity across both military and civilian domains, owing to benefits like affordability, ease of manufacturing, and a notable payload capacity for their size.However, the illegal use of UAVs has also increased, leading to the emergence of a new research area focused on detecting and tracking these vehicles to prevent such misuse and mitigate potential threats. This reviewpaper provides a detailed examination of current methods and approaches for ground-based UAV detection and tracking, along with an evaluation of existing studies and recommendations for future research.
In aviation, weight is crucial for aircraft performance and payload capacity. Traditional design methods, which rely on trial and error, aim to create lightweight and strong structures but can be time-consuming. Topology optimization, a mathematical technique, speeds up finding the optimal material distribution within a given shape under specific loads and conditions. This study employed ANSYS's CFD and Structural Optimization modules for the topology optimization of a NACA 0012 airfoilwing section under various conditions. The CFD module provided aerodynamic loads to the structural topology optimization module. The study analyzed both 2D and 3D geometries under different conditions, including single-point and multi-point optimizations for 2D sections with varying angles of attack, which provided useful comparisons. For the 3D test case, CFD analysis and topology optimization were performed on a rectangular wing at an inflow speed of 0.5 Mach and a 0⁰ angle of attack. Such combined structural and flow analysis is rare in the literature. This research provides newinsights, highlighting the sensitivity of topology optimization to boundary conditions and computational meshes. Despite challenges from complex geometries, this approach is expected to grow in popularity, especially with advanced production methods like 3D printing and additive manufacturing
n this study, the low-and high-velocity impact behavior of carbon fiber composite sandwich panels was investigated using numerical, experimental, and analytical methods. The damage mechanism on the sandwich panel was investigated in terms of ballistic limit, energy absorption, and projectile residual velocity. In the experimental studies, a sandwich panel with the same material properties and dimensions was tested by using a single-stage gas gun. The impact velocities of the spherical projectile with a diameter of 10 mm were selected as 20, 50, and 100 m/s. The results show that no perforation occurred at the impact velocity of 20 m/s, and the error percent between the FE analysis and experiments in terms of residual velocity is in the range of 2.66% and 14.38% at the impact velocities of 50 and 100 m/s. The discrepancy between the experimental and analytical results is about 9% in terms of residual velocity at different impact velocities.
This study examined functionally graded foam beam thermal buckling. The functionally graded beam was made of biocompatible Ti6Al4V metal (bottom surface) and ceramic zirconia (top surface), which changed function with thickness. Three types of foam structures were assumed along the functionally graded beam's thickness to imitate bone structures. symmetric and homogeneous foam structures feature open-cell foam void ratios up to 60%. Using the Hamilton principle and higher-order beam theory, equations of motion were generated and solved using the Navier technique. The impacts of ceramic and metal materials, foam structure type, and foam void ratio on FGM beam thermal buckling were examined and presented. The uniform foam distribution model (Model 1) has the highest buckling temperatures. Additionally, raising foam void ratio increased thermal resistance in all models. Beams with greater titanium content had higher buckling temperatures.
The rise of drone technology offers promising enhancements to inspection practices in general aviation (GA), especially for light aircraft in the Philippines,where manual methods prevail. This study assesses the feasibility and readiness for drone-based inspections using a multi-method qualitative approach involving document analysis, international benchmarking, and a local case study. Findings show that the Civil Aviation Authority of the Philippines (CAAP) lacks specific regulatory guidance, unlike ICAO and EASA frameworks. International benchmarks from the U.S., Switzerland, and France underscore the efficiency and safety benefits of UAV use. The Philippine case study indicates strong stakeholder interest but reveals gaps in regulatory awareness, technical capacity, and workforce preparedness. Despite the technical feasibility, institutional readiness remains limited. The study recommends a phased approach toimplementation, regulatory reform, and integration of UAV training in AMT education to facilitate safe adoption in Philippine GA maintenance operations.
This paper presents the modeling and control design for a turboshaft engine. Nonlinear dynamic models of the engine were linearized at various operating pointsand controllers were designed for each linearized model to ensure optimal performance under specific operating conditions. To ensure smooth transitions between operating conditions, a gainscheduling technique was employed. Simulation results demonstrate the effectiveness of the proposed control strategy in tracking reference inputs and maintaining system stability. The transient response of the power shaft was characterized by an overshoot of around4percent, while the other shaft exhibited an overshoot of approximately 0.4 percent.
Since the invention of the aircraft, the need forrunways for takeoff and landing only overland haslimited their uses. However, this situation has changed with the invention of aircraft carriers. An aircraft carrier with its flight deck is a warship that acts as an open-sea air base, and it is used to conduct operations in the designated areas. In this work, a prototype landing assistance system is proposed to work alongside landing signal officers (LSOs) on aircraft carriers. The system is based on visual information from the approach and landing phases and includes a voice assistant. The main goal of this system is to assist the LSOs rather than replace them by reducing their workload and minimizing fatigue-related human errors. The system is designed to be activated or deactivated by the LSO as required, ensuring flexibility and adaptability to varying operational needs. The proposed system is designed to be easy to integrate with the equipment already on aircraft carrierswhile keeping mobility in mindand tested in differentweather and lighting conditions in a simulation environment
The Landing and Take-Off (LTO) cycle is a critical component in avionics, which has a crucial effect on the objectives, such as decreasing fuel consumption, emissions, and increasing operational efficiency in airport environments. Understanding and managing the variability within LTO processes promise to maintain the mentioned objectives by achieving better performance in civil avionic operations together with fulfilling environmental standards. This study conducts a variability analysis of the LTO cycle for medium-sized airplanes to analysis the times of the LTO operations. A normality analysis using the Shapiro-Wilk test is first applied to check the distribution of the dataset. Then, an 𝑋𝑋-𝑠𝑠 variable control chart (VCC) is applied to monitor the variability in the operations of LTO cycle. Furthermore, an Average Run Length (ARL) analysis is conducted to evaluate the sensitivity of the control chart to process shifts. Based on the results and analysis, it is believed that the proposed methodology provides a robust framework for monitoring and improving the stability of LTO cycle operations, contributing to the optimization of process performance in civil aviation and the reduction of environmental impact.
In the context of personnel assignment, the allocation of tasks to individuals with appropriate expertise and willingness not only reduces costs in terms of time and resources but also increases satisfaction for both the assigning and assigned parties. To achieve these positive outcomes, the key lies in matching the right person with the right job. Mathematically, this suitability is determined through personnel assignment models within the framework of linear programming. In the present study, a personnel assignment model was employed to allocate lieutenant-ranked officers who had not yet been assigned to pilot, navigation, or unmanned aerial vehicle pilot duties within the Air Force to one of the 14 defined branches within the institution. The model's formulation considered the basic requirements of each branch, the candidates' suitability for the respective roles, the characteristics of their academic backgrounds, and other specific conditions, utilizing a 0-1 integer linear programming assignment model. The assignments made within the framework of the model were found to be in full compliance with the regulatory expectations.
As new technical solutions emerge with developing technology, availability of spare parts and suppliers utilized for continued maintainability of satellite systems throughout their service lives diminishes on a yearly basis. Consequently, older technology becomes harder to support, with subsystems and spare parts within the system inevitably becoming obsolete. Obsolescence Management aims to enable continued maintainability of systems, preserving capabilities as defined in initial project requirements and (if required) enhancing system capabilities through modernization, thereby ensuring continued operation throughout their service lives. Throughout this study, Space systems has been introduced due to the obsolescence necessities and TOPSIS decision making method is used for predefined Logistics’ Breakdown Structure (LBS) items which is selected from different Satellite Ground Segment Equipment. With this decisionmaking method, High and Low risk component’s obsolescence management approach has been defined
The objective of this study was to develop a webbased platform to increase the interest of students who are new to cybersecurity and to help them acquire security paradigms in a multidimensional way. The main objective of the study was to provide an effective gateway to cybersecurity by increasing the self-confidence of a student who has no cybersecurity background or who is new to the field. In fact, the Capture the Flag (CTF) provides an applied environment where students can apply the lessons learned in theory to real cases. To test the validity of the developed training system, a research was conducted with the targeted student group. The findings indicated that the CTF approach can be an effective tool in the education of a student who is new to cyber security. The feedback from the students indicated that 77.8% of the CTF logic and foundation was gained. Furthermore, the students reported that the tasks and scenarios, which were designed to reflect the multifaceted nature of cyber security, had a positive impact on their confidence in the field. It was concluded that CTFs can be evaluated as an effective training tool in the direction of cyber security education, based on both the literature review and the feedback received from the students in practice. The data supporting this argument were included in the study.
Tensegrity structures, composed solely of axial load carrying elements, have been considered for space applications from various aspects. Resilient tensegrity-based robots, designed for the exploration of celestial bodies, are planned to be launched from spacecraft in orbit and require no additional landing mechanisms as the impact loads are absorbed by the robot itself. Recent modeling studies for tensegrity structures’ damping capabilities employ an analytical approach where the governing equations are derived for specific geometries and solved using iterative schemes. In contrast, this work proposes a finite element model employing an elastoplastic material model to evaluate the energy absorption capabilities of tensegrity structures. The proposed model assesses the damping capabilities of tensegrity structures through hysteresis curves under cyclic compression loads, with struts, compressioncarrying elements, exhibiting distinct nonlinear behavior during loading and unloading. Based on the developed model, a comparative analysis of cylindrical tensegrity structures with three and four struts is carried out. Prestress values and applied external loads are varied to carry out a comprehensive analysis.
Within the aviation industry and the data science community, the integration of neurosphysiological measurements, particularly EEG data analysis has become prominent. The biggest concern in aviation is safety. Pilots are one of the most important factors in ensuring this safety. If we can understand and predict pilots' cognitive states, and warn them, when necessary, this could save lives. Through detailed analysis and comparison of input types, we aim to demonstrate the transformative potential of EEG-based cognitive state classification. The research explores whether high-level feature extraction enhances cognitive state classification, and determines which input type—raw signals or low-level frequency band features—yields superior results. The results demonstrate significant advantages in utilizing low-level representations over raw features across various layer configurations. The statistical analysis underscores the significance of the choice of input type, with notable differences in training and inference times, as well as key evaluation metrics between raw and low-level channels.
Work-related musculoskeletal disorders (WMSDs) are health problems that occur in muscles, tendons, ligaments, joints, and nerves as a result of exposure to ergonomic risk factors in the workplace. Ergonomic risk burden is a measure of a worker's risk of developing musculoskeletal disorders (MSDs) at work. This risk arises from exposure to ergonomic risk factors. Like many occupational groups, pilots are likely to be exposed to similar threats. They work in ergonomically demanding conditions such as prolonged static postures, vibration, and repetitive movements and are exposed to various risks. This increases the risk of developing musculoskeletal disorders. WMSD is one of the leading causes of unemployment and disability among pilots and can lead to long-term health problems. The Quick Exposure Check (QEC) method is a tool that can be used to quickly and easily assess pilots' exposure to these risk factors. This paper presents an application of the Quick Exposure Check method on pilots in the aviation field. It was assessed that different aircraft types may create different ergonomic risk burdens on pilots and the body parts at risk from ergonomic exposure were determined as back, shoulder/arm, wrist/hand and neck.
