Aerospace Defense

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.

Abstract Background & Purpose: This study introduces an innovative framework for aircraft design that integrates fuzzy logic, metaheuristic optimization algorithms, and three-dimensional Pareto front analysis. In the initial phase, the aircraft’s empty and takeoff weights were calculated using conventional design methods. Fuzzy logic was subsequently employed as a decision-making tool to assess the validity of these estimates. Key design parameters such as wing area and engine thrust were identified using a novel computational methodology.

Methodology: The optimization of critical design variables was performed using multiple advanced metaheuristic algorithms, including Harris Hawks Optimization (HHO), Marine Predators Algorithm (MPA), Whale Optimization Algorithm (WOA), and Artificial Bee Colony (ABC). These algorithms simultaneously optimized parameters including empty weight, takeoff weight, stall speed, and flight range. The optimized results, particularly in terms of wing area and engine thrust, were benchmarked against reference models of similar aircraft.

Findings: Three-dimensional Pareto fronts were generated for design parameters such as empty weight, takeoff weight, and fuel weight to evaluate key performance indicators including fuel efficiency, payload capacity, flight range, and overall performance. The results demonstrated notable improvements in key aerodynamic and propulsion characteristics.

Conclusion: The comprehensive analysis conducted in this study led to the development of an optimized model of a propeller-driven aircraft. The proposed approach resulted in significant enhancements in performance metrics and overall efficiency, validating the effectiveness of the integrated optimization and decision-making framework.

Abstract Supercapacitors are gaining attention for their high-power density, long lifespan, and rapid charge-discharge capabilities (Especially its use in defense industries and military equipment). The performance of these devices heavily relies on their electrode materials. A nickel-based metal-organic framework (Ni-MOF) with a high specific surface area was synthesized to enhance energy storage. To improve electrical conductivity and capacitive properties, titanium carbide MXene (Ti3C2 MXene) and graphene (Gr) were incorporated into the MOF. This combination was deposited onto nickel foam (NF) via a hydrothermal method, which allowed for better surface area utilization by reducing aggregation between Gr and MXene layers and facilitating electrolyte transport through the conversion of graphene oxide to Gr. The inclusion of Ni-MOF also enhances the quasi-capacitive properties due to its electroactivity. The Ni-MOF/MXene/Gr/NF electrode achieved a specific capacitance of 845 F g⁻¹ in a 3 M KOH electrolyte, while the cathode (graphene aerogel integrated with activated carbon, C-GA/NF) exhibited a capacitance of 373.5 F g⁻¹. For the asymmetric supercapacitor configuration (Ni-MOF/MXene/Gr/NF‖C-GA/NF), a specific capacitance of 637 F g⁻¹, specific energy of 22.8 W h kg⁻¹, and specific power of 0.69 kW kg⁻¹ were recorded. Additionally, the device maintained 55.2% of its initial capacity after 5000 charge-discharge cycles at a current density of 8 A g⁻¹, indicating excellent stability and cycle life. Taken together, these features facilitate the use of this device in military and defense equipment.

Abstract examines the extent of radioactive pollutant dispersion and radiation dose received due to hypothetical nuclear bomb explosion in central Iran using HYSPLIT and GDAS data, without considering chemical reactions. results indicate that the predominant direction of radioactive fallout is northeast, with a minor amount towards southeast of explosion site. The spread of radioactive materials reaches approximately 300 kilometers from the explosion site within 12 hours, affecting Yazd province, southern regions of South Khorasan, and northern Kerman province. Additionally, dispersion and deposition of particles, as well as dose distribution, are highly dependent on local meteorological conditions. Initially, cloud grows significantly and reaches an altitude of 5,000 meters. Over time, particles settle over time , leading to greater deposition. peak concentration of deposited particles exceeds concentration of those dispersed, with maximum concentration and deposition occurring in northeastern area about 200 kilometers from explosion site, where radiation dose received by individuals exceeds 100 millisieverts. Furthermore, the total dose received by individuals on ground at 0 meter is higher than total dose received at altitudes of 0 to 100 meters. As time passes, density of radioactive cloud decreases, subsequently reducing radiation dose received by individuals. doses received by most individuals are significantly above permissible limits set by International Commission on Radiological Protection (ICRP). In terms of radioactivity, very few areas fall within the controlled zone, while most are classified as prohibited zones. emphasizes the importance of predicting and modeling dispersion of radioactive pollutants and demonstrates that instantaneous atmospheric conditions greatly influence accuracy of predictions.

Abstract In this paper, a rotor position estimation algorithm based on sliding mode observer (SMO) and phase-locked loop (PLL) for permanent magnet synchronous motor (PMSM) control in STM32L431RCT6 microcontroller is presented. The proposed SMO and PLL-based observer has two advantages compared to the conventional SMO observer: reducing the undesirable phenomenon of chattering and improving the accuracy of rotor position estimation. In this research, a hybrid sensorless control system for PMSM drive has been designed, using the I-f startup method and a smooth transition to sensorless closed-loop vector control with SMO and PLL. C programming language is used to implement field vector control, phase-locked loop, sliding mode observer, proportional-integral controllers, space vector pulse width modulation, and coordinate transformations. The proposed method is implemented using STM32 family 32-bit microcontrollers. In the following, the presented laboratory results show the desirable performance of the proposed observer.

Abstract Tetracyclines are a group of antibiotics widely known for their use in treating bacterial infections in both animals and humans. Therefore, accurate and rapid measurement of tetracycline levels is crucial for ensuring the safety of materials. This laboratory study presents an electrochemical aptasensor method for measuring tetracycline, which is based on the formation of a nucleic acid aptamer complex for detecting tetracycline on the electrode surface. This aptamer structure enhances sensitivity and accuracy in tetracycline measurement and it is prepared using cyclic voltammetry and square wave electrochemical techniques. The developed aptasensor in this study can potentially be used for detecting tetracycline in pharmaceutical preparations, contaminated food products, and drinking water. In addition to medical and food applications, this optosensor can be used as a potential tool for identifying chemical and biological agents in defense and military environments. The concentrations of tetracycline used in this experiment range from 10 µM to 10 mM, and they are analyzed using CV, impedance, and DPV graphs within a voltage range of -0.6 V to 0.6 V, a scan rate of 20 mV/s, a step potential of 2 mV, and a current range of 1.6 × 10⁻⁶ to 0 A. The results indicate significant improvement in detection limits and linear range. Additionally, this sensor features high sensitivity, stability, and reusability. This study presents significant improvements in tetracycline measurement and provides high efficiency in various applications of the drug.

Abstract In this article, a contrast enhancement algorithm using wavelet transform is proposed to create natural enhancement in images with various brightness ranges. In the proposed method, the input image is first decomposed into 4 sub-bands by discrete wavelet transformation. Then, in the LL sub band image, the histogram cutting process is performed by the cutting threshold level equal to the average intensities. Next, the cut histogram based on entropy is divided into three parts with approximately equal number of pixels, and before equalization process, each sub-histogram is mapped to the new dynamic range. Finally, we use the inverse discrete wavelet transform to create an improved image. By controlling the enhancement ratio, the proposed method produces an image with maximum details and natural enhancement in the output image. Comparing the performance of the proposed method with previously presented methods, in terms of entropy as well as visual quality based on the mean opinion score, shows the superiority of the proposed algorithm.