The increasing prevalence of unmanned aerial vehicles (UAVs) across various fields requires the development of advanced fault detection and diagnostic (FDD) frameworks to prevent the severe consequences of undetected sensor and actuator failures. This review investigates the wide spectrum of FDD methodologies for UAVs, focusing on the paramount role of sophisticated yet intelligent systems in safeguarding operational integrity, particularly in near-human environments. An analysis of 32 seminal publications from well-recognized databases presents a trend towards converging signal processing and machine learning techniques using UAV specific fault detection keywords. This analysis underscores the trend of data-driven models capable of performing real-time diagnostics. The authors increase interest in hybrid methodologies that correlate the precision of signal processing and the adaptive nature of machine learning. These approaches aim to gain fault detection accuracy and achieve better prognostic capabilities. By deconstructing the strengths and weaknesses of various methods, this analysis concludes with the need for further research into upcoming new challenges. The review focuses on the investigation of synergistic strategies and encourages interdisciplinary collaboration for advancements in FDD methods. These initiatives will enhance UAV safety and reliability across a wide range of operational contexts.

The optimization of heat transfer in various engineering applications, such as thermal management systems and energy storage devices, remains a crucial challenge. This study aims to investigate the potential of Casson-based Cu-H₂O nanofluids in enhancing free convection heat transfer within complex geometries. The research examines the free convection heat transfer and fluid flow characteristics of a Casson-based Cu-H₂O nanofluid within a semi-parabolic enclosure that includes a wavy corrugated cylinder. Utilizing numerical simulations based on the Galerkin Finite Element Method, the study investigates the impact of different factors, including the Rayleigh number (10³ ≤ Ra ≤ 10⁶), Casson fluid parameter (0.1 ≤ γ ≤ 1), corrugation number (3 ≤ N ≤ 10), nanoparticle volume fraction (0 ≤ ϕ ≤ 0.15), and enclosure inclination angle (0° ≤ ζ ≤ 60°), on both heat transfer efficiency and flow patterns. The results reveal that increasing the Rayleigh number and Casson fluid parameter enhances heat transfer performance, with the average Nusselt number increasing by up to 165 % as Ra increases from 10³ to 10⁶. An optimal range of corrugation numbers is identified for maximizing heat transfer at higher Rayleigh numbers. The addition of nanoparticles significantly improves heat transfer, with a 20 % increase in the average Nusselt number observed at Ra = 10⁵ when the nanoparticle volume fraction increases from 0 to 0.15. These findings provide valuable insights for designing more efficient thermal management systems in applications such as electronics cooling, solar thermal collectors, and heat exchangers, potentially leading to improved energy efficiency and performance in various industrial and technological sectors.

Latent heat thermal energy storage systems using phase change materials face key challenges, particularly the low thermal conductivity of phase change materials and the limited investigation of insulating effects like air layers within storage geometries. This study numerically examines the impact of air layers on the melting behavior of paraffin wax (RT42) inside a horizontal double concentric tube using a two-dimensional computational fluid dynamics model in ANSYS/FLUENT 16. Three configurations were analyzed: no air layer, a 1 mm air layer, and a 2 mm air layer surrounding the inner heated tube. The enthalpy-porosity method was applied to simulate the melting process, accounting for heat transfer by conduction and natural convection. Results show that introducing a 1 mm air gap extended the melting time by 28.57%, while a 2 mm air gap led to a 57.14% increase, primarily due to the added thermal resistance. This is the first study to quantitatively assess air layer effects in this geometry, offering new insights into optimizing phase change material-based storage systems by considering internal insulation phenomena.

This work presents a suggested analytical solution for a forced vibration of an aircraft sandwich plate with a honeycomb core under transient load. The differential equation of motion is first derived and then solved by using the separation of variables method. The plate’s transient response and maximum transient deflection are studied with various design parameters. First, the analytical results are figured out using the honeycomb structure’s mechanical properties, such as its density, Poisson’s ratio, modulus of elasticity, and modulus of rigidity. Next, the effect of the honeycomb structural properties on the transient response and the maximum transient deflection is determined. Then, the cell size, core height, and cell wall thickness are selected as the honeycomb structural parameters. The ANSYS 19.2 software package is utilized to perform the finite element simulation for the sandwich panel with the honeycomb core. This study conducted modal and transient response analyses to derive the numerical transient response and maximum transient deflection. The results demonstrate a strong concordance between the analytical and numerical results with a 95% conformity rate. Moreover, the results demonstrate an inverse relationship between the transient response and both the core height and cell wall thickness, while it is directly proportional to the cell size. This relationship is derived from the theoretical equations and further validated through numerical simulations, showing strong agreement between analytical and computational results.

In anti-lock brake systems (ABS), the primary goal of the controller is to maximize vehicle deceleration by maintaining the slip ratio at an optimal level. This work presents a fresh approach that enhances ABS performance by integrating a sliding mode controller with a barrier function. This method combines integral sliding mode control with adaptive laws informed by barrier functions, effectively managing external disturbances and uncertainties in inertia. A significant benefit of this approach is that it does not require prior knowledge of the upper limits of these uncertainties and disturbances, thanks to the barrier function-based sliding mode control. The system state is initially aligned with the switching manifold, ensuring robust compensation for any uncertainties and disturbances right from the start of braking. During the sliding mode phase, dynamic properties are finely tuned to ensure that the system’s performance remains consistent. The effectiveness and reliability of the proposed controller have been demonstrated through numerical simulations conducted in MATLAB/Simulink, proving its capability across a range of road conditions.