This paper presents a comprehensive analysis of the dynamics and design of oleo-pneumatic shock absorbers for the landing gear of carrier-based Unmanned Aerial Vehicles (UAVs). Carrier-based operations impose unique challenges due to high-impact landings, necessitating robust landing gear systems capable of withstanding significant g-forces. The study investigates the performance of landing gears designed for carrier operations under various sink rates, utilizing computer simulations to model the dynamics of both sprung and unsprung masses. The design process for an oleo-pneumatic main landing gear of an 8500 kg UAV includes detailed calculations for stroke, shock absorber strut sizing, spring and damping characteristics, and impact force analysis. The research employs both isothermal and adiabatic models to evaluate the variation in pneumatic pressure and air spring force under static and dynamic loadings, revealing the nonlinear behavior of the shock absorber. The damping characteristics are thoroughly analyzed, demonstrating the superior performance of oleo-pneumatic systems in vibration damping. The simulation results confirm that the current design effectively mitigates impact forces, maintaining vertical accelerations within the design constraints and ensuring structural integrity during landing maneuvers. Key findings include the ability of the shock absorber to handle high sink rates typical of carrier-based operations, with calculated vertical accelerations and force values indicating robust performance. The study identifies areas for future research, such as the development of automated loads and stress analysis tools for rapid weight quantification, exploration of active control systems for vibration alleviation, and potential benefits of multi-service applications for landing gear designs. By addressing the challenges of performance and robustness in carrier-based operations, this research advances landing gear technology for UAVs, enhancing their safety and efficiency in various operation environments. The insights gained provide a solid foundation for optimizing landing gear systems, ensuring reliable performance under demanding conditions.

This study presents an innovative application of genetic algorithms (GAs) for optimizing the Cobb–Douglas production function, a cornerstone of economic modeling that examines the relationship between production output and the inputs of labor and capital. This research integrates traditional optimization methods, such as partial derivatives, with evolutionary computation techniques to address complex economic constraints. The methodology demonstrates how GAs outperform classical techniques in solving constrained optimization problems, offering superior robustness, adaptability, and efficiency. Key results highlight the alignment between GA solutions and traditional Lagrangian methods while underscoring the computational advantages of GAs in navigating non-linear and multi-modal landscapes. This work serves as a valuable resource for both educators and practitioners, offering insights into the potential of GAs to enhance optimization processes in engineering, economics, and interdisciplinary applications. Visual aids and pedagogical recommendations further illustrate the algorithm’s utility, making this study a significant contribution to the computational optimization literature. Additionally, the optimization process using genetic algorithms is presented in a step-by-step manner, with accompanying visual graphs that enhance comprehension and demonstrate the method’s effectiveness in solving mathematical problems, as validated by the study’s results.

Additive manufacturing (AM) enables a level of design flexibility that is difficult to achieve with conventional techniques, yet it inherently yields materials marked by significant variability, anisotropy, and sensitivity to defects that challenge classical mechanics-of-materials assumptions. Process-driven microstructural heterogeneity, stochastic defect populations, and residual stresses strongly influence deformation, fatigue, and fracture behavior, often outweighing nominal material properties and constraining the predictive capability of traditional constitutive and fracture mechanics models. Machine learning (ML) has emerged as a powerful means of handling the complexity of AM data; however, many current approaches depend on black-box models that lack physical transparency, extrapolate poorly, and treat uncertainty inadequately. This review contends that ML should augment—rather than replace—mechanics-based modeling, and that dependable prediction of AM material behavior requires mechanics-informed ML frameworks. We critically analyze the central mechanics challenges in AM and evaluate established modeling strategies alongside emerging ML methods relevant to deformation, damage, fatigue, and fracture. Particular emphasis is given to physics-informed and hybrid ML approaches that explicitly incorporate anisotropy, defect sensitivity, residual stress effects, and uncertainty quantification within learning architectures. Recent progress in ML-assisted constitutive modeling, fatigue and fracture prediction, and digital twin development is synthesized, and the implications for qualification, certification, and structural deployment of AM components are discussed.

Exhaust gas recirculation (EGR) has been an efficient emission treatment strategy employed in internal combustion engines (ICEs) to cope with NO_x emission limits since the introduction of Euro 4 regulations for heavy-duty commercial vehicles. A portion of the exhaust gas is fed back into the intake port, replacing O₂ in the fresh air with inert CO₂ from the exhaust gas, resulting in a reduction in the combustion temperature and, hence, a reduction in NO_x emissions. Considering the high exhaust temperature, this process increases the charge mixture temperature and degrades the volumetric efficiency of the engine. EGR coolers have been introduced as vital parts of EGR exhaust treatment systems with the aim of reducing the intake port temperature to increase volumetric efficiency and further reduce combustion temperatures. EGR coolers are heat exchangers (HXs) that generally employ engine coolant to reduce the EGR temperature with effectiveness values around 0.7~0.85 and downgrade with engine usage owing to soot deposition. Increasing the effectiveness of the EGR cooler has a positive effect on engine volumetric efficiency and reduces NO_x, particulate matter (PM), and fuel consumption. The current study involved the design of a microchannel HX for a 500 PS heavy-duty Euro 6 diesel engine EGR cooler. The mechanical and thermal-hydraulic design calculations of the proposed HX were performed using Mathematica software. The optimum HX dimensions for the required boundary conditions were determined, and the performance of the EGR cooler was analyzed for the current and proposed options. Furthermore, Diesel-RK software was used to model the engine performance with NO_x, PM, CO₂ emissions, and fuel consumption predictions. The results show that the newly proposed microchannel HX design improves NO_x, PM, and specific fuel consumption by 6.75%, 11.30%, and 0.65%, respectively.

In this study, the effect of inlet air cooling on greenhouse gas (GHG) emissions and engine performance for a land-based gas turbine engine was investigated under varying ambient temperatures (15–55 °C). The study aimed to reduce GHG emissions while improving output power and fuel efficiency during hot weather operating conditions. For illustrative purposes, a representative gas turbine engine model, approximating the 250 MW class General Electric (GE) engine, was analyzed in a simple cycle. A refrigeration process was integrated with a turboshaft gas turbine engine to chill the incoming air, and the power required for cooling was extracted from the gas turbine’s output power. This mechanical chiller was assumed to provide a 15 °C inlet air temperature. Without inlet air cooling, at 55 °C ambient temperature, the engine’s power output was calculated to decrease by 15.06%, while power-specific fuel consumption and GHG emissions increased by 6.09% and 5.84%, respectively. However, activating the refrigeration or cooling system in the inlet made it possible to mitigate most of the adverse effects of hot weather on the engine’s performance and GHG emissions. Therefore, with inlet air cooling, the power output loss reduces to 3.28%, indicating an 11.78% recovery compared to the 15.06% loss without cooling. Similarly, the rise in power-specific fuel consumption caused by high ambient temperature decreases from 6.09% to 3.43%, reflecting a 2.66% improvement. An important finding of the study is that with inlet air cooling, the increase in GHG emissions reduces from 5.84% to 3.41%, signifying a 2.43% improvement on a hot day with a temperature of 55 °C.