Aerospace Defense

Abstract Accurate estimation of attitude and heading in UAVs is one of the key challenges in autonomous navigation systems, playing a vital role in the control and guidance of these vehicles. In this paper, a quaternion-based particle filter with a specific sampling method and an extended Kalman filter (EKF) are employed for UAV attitude estimation. By integrating data from inertial sensors (including gyroscopes, accelerometers, and magnetometers) and applying filtering techniques, the proposed methods significantly enhance the accuracy of roll, pitch, and yaw angle estimation. The innovation of this study lies in the comprehensive comparison of the particle filter and EKF performance across two scenarios: simulated data and real-world data collected from a specific attitude and heading reference system (AHRS). The results demonstrate that the particle filter achieves remarkable improvements of over 99.99% in simulated data and over 88.48% in real-world data for roll and yaw angle estimation. Additionally, the analysis of variance and standard deviation of errors confirms that the particle filter outperforms in reducing error dispersion, with the error variance for yaw angle being approximately 100 times lower than that of the EKF. On the other hand, the EKF shows slightly better performance in pitch angle estimation. These findings suggest that a combination of these two filters can serve as an effective solution for precise navigation systems in UAVs. The resampling method implemented in the particle filter also significantly enhances the accuracy of roll and yaw angle estimation.

Abstract The security and survivability of defensive structures against extreme dynamic loads such as blast waves and impacts is a fundamental priority in modern design. Sandwich panels, due to their exceptional strength-to-weight ratio and high energy absorption capacity, are among the primary candidate materials in this field. This research investigates and compares the influence of two widely used core types the porous core and the viscoelastic core on the natural frequencies of a sandwich beam structure. The main objective of this study is to assess the potential of these cores to enhance the strength and safety of defensive structures through the analysis of their vibrational behavior. The present research employs analytical modeling to perform the natural frequency analysis. Using three-layer sandwich beam theory and applying Hamilton's principle, the governing equations of the system are derived. The resulting equations are complex partial differential equations (PDEs). To solve these equilibrium equations, the semi-analytical Navier method is utilized in the spatial domain. To validate the accuracy of the obtained results, comparisons are made with existing solutions for specific cases. Finally, the influence of various parameters such as carbon nanotube volume fraction, porosity coefficient, porosity distribution pattern, geometric and dimensional ratios on the natural frequencies of the sandwich structure is examined. This investigation covers structures with both porous and viscoelastic cores and nanocomposite face sheets. A key finding of this research is that, in most instances, the viscoelastic core exhibits higher natural frequencies and greater strength compared to the porous core.

Abstract This study focuses on the thermal analysis of the cooling system for a specific type of enclosed motor pump. According to the IEC standard, the winding insulation has a specific temperature tolerance; exceeding this limit can damage the insulation and impair motor function. A crucial component of the motor's cooling system is the waterjacket, which serves two purposes: cooling the motor housing and cooling the flow passing through the motor interior. Reducing the motor housing temperature ultimately lowers the winding temperature. To simplify calculations, a thermal analysis of the enclosed motor housing was performed, considering temperature and thermal boundary conditions. The effect of the waterjacket at various flow rates 0.1, 0.3, and 0.5 kg/s and the coil flow at flow rates of 0.05, 0.1, and 0.15 kg/s, along with different diameters, were investigated to determine temperature and pressure drop. The heat generated in different motor components was applied as a heat flux on the inner surface of the motor housing. The results show that increasing the waterjacket flow rate consistently reduces the housing temperature and the coil outlet fluid temperature. Conversely, increasing the coil flow rate increases the coil outlet temperature. Using a water-antifreeze mixture compared to pure water slightly increases the minimum housing temperature. The minimum housing temperature consistently occurs at a coil flow rate of 0.05 kg/s and a waterjacket flow rate of 0.5 kg/s. The pressure drop always increases with increasing waterjaket flow rate

Abstract Introduction: In the present work, the X-ray diffraction (XRD) pattern of ZnSe quantum dots is simulated using a home-made computer program. ZnSe is an important semiconductor with an FCC-zinc blend stable crystalline phase owing a lattice constant of 5.6676 Å, which has widespread applications in optoelectronics, infrared detectors, and aerospace technology applications.
Methods: In this study, XRD patterns are simulated for both bulk and nanoscale ZnSe.
Findings: The results indicate that in the bulk phase, the diffraction peaks are very sharp and narrow, consistent with the standard reference cards. However, at the nanoscale, the peaks broaden and their intensity changes, indicating a reduction in crystallite size and an increase in structural defects. A comparison is made between the simulation and the experimental results, showing that the agreement between the experimental XRD pattern and the simulated one in terms of the number of peaks, their positions, relative intensities, and peak widths indicates good consistency.
Conclusion: This research confirms that XRD simulation is a powerful and efficient tool for the structural analysis of nanomaterials, capable of accurately reproducing the effects of size and defects on diffraction patterns. Therefore, the developed program can play a significant role in the analysis and design of ZnSe semiconductor nanostructures and other compounds.

Abstract The performance of naturally aspirated aircraft engines declines significantly with increasing altitude due to reduced air pressure and density, affecting power, torque, in-cylinder pressure, and combustion temperature. This study presents a comparative analysis of the carbureted (Rotax 912 ULS) and fuel-injected (Rotax 912iS) versions of the Rotax 912 engine using GT-SUITE simulations across altitudes up to 9,150 meters. Results indicate that the carbureted engine suffers an 80% reduction in power at high altitudes due to its fixed fuel-air mixture, while the fuel-injected engine maintains more stable performance by dynamically adjusting fuel delivery. The EFI system also preserves higher in-cylinder pressure and combustion stability under oxygen-scarce conditions. Although EFI mitigates performance loss more effectively than carburetion, both configurations exhibit significant degradation at high altitudes, highlighting the inherent limitations of naturally aspirated engines. These findings underscore the importance of advanced altitude compensation methods—such as turbocharging or optimized EFI mapping—for enhancing reliability and efficiency in high-altitude aviation operations.

Abstract Introduction: Bio-inspired cellular structures and auxetic geometries have attracted considerable attention due to their superior energy absorption capabilities and deformation characteristics. However, the combined effects of sunflower-inspired radial arrangement, re-entrant geometry, and foam filling on the mechanical response and energy absorption performance of cellular structures remain largely unexplored.
Methods: This study experimentally investigates the quasi-static compressive behavior of conventional honeycomb, sunflower-inspired, and hybrid sunflower re-entrant lattice structures in both hollow and polyurethane foam-filled configurations. The specimens were fabricated using three-dimensional (3D) printing technology with polylactic acid (PLA) material and tested under quasi-static compression.
Findings: The results demonstrated that cellular geometry significantly influences the buckling mechanism, deformation stability, and energy absorption capacity. The hybrid sunflower re-entrant structure exhibited a more uniform stress distribution and a more progressive collapse mode than the conventional honeycomb structure due to its auxetic behavior and radial–spiral arrangement. Furthermore, polyurethane foam increased the initial stiffness, delayed the onset of buckling, and improved the progressive crushing behavior. The proposed structure achieved a 112% increase in energy absorption, a 6% improvement in specific energy absorption, and a 3% enhancement in crushing force efficiency compared with the conventional honeycomb structure.
Conclusion:
The synergistic combination of sunflower-inspired geometry, re-entrant auxetic configuration, and polyurethane foam filling considerably enhances the energy absorption performance and deformation stability of cellular structures, indicating significant potential for applications in aerospace, automotive, and sandwich panel systems.

Abstract Introduction: Urban air mobility and air taxi systems have emerged as promising solutions for future transportation demands due to their potential for reducing travel time and improving transportation efficiency. However, achieving an optimal balance among aircraft weight, aerodynamic performance, fuel efficiency, and mission requirements remains a significant challenge during the conceptual design phase. Therefore, the development of intelligent optimization approaches integrated with conventional design methodologies is of considerable importance.
Methods: This study presents the optimal design of an 18-passenger air taxi through the integration of classical aircraft design methods and a Q-learning-based reinforcement learning framework. Initially, baseline parameters, including maximum takeoff weight, empty weight, fuel consumption, and wing area, were estimated using established conceptual design relations. A simulation environment was then developed in which the state space was defined by key nondimensional parameters, including thrust-to-weight ratio, wing area ratio, fuel weight ratio, and empty-weight-to-maximum-weight ratio. The action space consisted of bounded modifications to wing area, aspect ratio, and thrust-to-weight ratio. The reinforcement learning agent was trained for 2000 episodes with a maximum of 40 design steps per episode using an ε-greedy policy under a fixed mission profile defined by cruise altitude, cruise speed, range requirements, and field length constraints.
Findings: The optimization process resulted in a 3.8% reduction in wing area and a 2.1% increase in aspect ratio. Furthermore, the horizontal and vertical tail areas decreased by 2.9% and 3.2%, respectively. These geometric refinements led to reductions in empty weight (4.1%) and fuel consumption (5%). Significant improvements in aerodynamic and performance characteristics were also observed, including increases in lift-to-drag ratio (20%), rate of climb (33.3%), and cruise speed (6.7%). Consequently, the flight range increased by 14.5%.
Conclusion: The findings demonstrate that integrating classical aircraft design methodologies with reinforcement learning provides an effective and data-driven framework for improving air taxi design. The proposed approach successfully achieved simultaneous reductions in aircraft weight and fuel consumption while enhancing aerodynamic efficiency and mission performance, indicating its potential for future intelligent conceptual aircraft design applications.