Airplane manufacturers are frequently faced with formidable challenges to improving both aircraft performance and customer safety. Ice accumulation on the wings of aircraft is one of the challenges, which could result in major accidents and a reduction in aerodynamic performance. Anti-icing systems, which use the hot bleed airflow from the engine compressor, are considered one of the most significant solutions utilized in aircraft applications to prevent ice accumulation. In the current study, a novel approach based on machine learning (ML) and the Internet of Things (IoT) is proposed to predict the thermal performance characteristics of a partial span wing anti-icing system constructed using the NACA 23014 airfoil section. To verify the proposed strategy, the obtained results are compared with those obtained using computational ANSYS 2019 software. An artificial neural network (ANN) is used to build a forecasting model of wing temperature based on experimental data and computational fluid dynamics (CFD) data. In addition, the ThingSpeak platform is applied in this article to realize the concept of the IoT, collect the measured data, and publish the data in a private channel. Different performance metrics, namely, mean square error (MSE), maximum relative error (MAE), and absolute variance ($R^2$), are used to evaluate the prediction model. Based on the performance indices, the results prove the efficiency of the proposed approach based on ANN and the IoT in designing a forecasting model to predict the wing temperature compared to the numerical CFD method, which consumes a lot of time and requires high-speed simulation devices. Therefore, it is suggested that the ANN-IoT approach be applied in aviation.

Tip vortices are one of the most critical phenomena facing rotary wings such as propellers and wind turbine blades and lead to changes in the aerodynamic parameters of blades. The winglet (WL) device is considered one of the most significant passive flow control devices. It is used to diminish the strength of vortices at the blade tip, enhance the aerodynamic characteristics of turbine rotor blades, and thereby increase the overall turbine efficiency. The main objective of this research is to improve the aerodynamic characteristics of wind turbines by adding a winglet at the blade tip. An optimum turbine blade profile was taken to build the turbine rotor geometry. The turbine has three blades with a radius of 0.36 m, and the NACA4418 airfoil blade sections were used to build the blade profile. The computational domain was created by ANSYS software, and the model was validated for spalart-allmaras and k-ω SST turbulence models with experimental measurements. The computational model was solved for blade shapes without and with tip winglets. Various winglet height lengths per blade radius (WHLR) of 0.008, 0.02, 0.04, 0.05, 0.06, 0.07, and 0.08 were studied for a 90-degree cant-angle and a constant design tip speed ratio of 4.92. Generally, the results illustrate that the performance characteristics of the turbine rotor were improved by using the tip winglet. The lift-to-drag ratio coefficient (C_L/C_D) and power coefficient (C_p) are increasing with increasing WHLR until they reach the highest improvement value, and then they start to decrease gradually. The optimum WHLR is about 0.042, with a percentage improvement in the lift-to-drag ratio (C_L/C_D) and power coefficient (C_p) related to the blade without winglet of about 11.6% and 6.9%, respectively, and an increase in the thrust force of 14.8%. This is mainly caused by decreasing the vortex strength near the tip region and improving the characteristics of stall behaviors.

Improving the aerodynamic performance of wind turbine blade is considered one of the most significant factors to maximize the efficiency. This research aims to improve the aerodynamic performance for various ranges of operation wind speed using trailing-edge flap. An optimum blade shape was taken to construct the turbine geometry. The computational model was constructed by ANSYS FLUENT and the k-w SST turbulence model was used. The computational domain was solved for optimum blade shape with and without trailing edge flap. A various flap deflections were studied for different wind speed and constant rotational speed. The results show that at certain wind speed the turbine performance characteristics were enhancement with increasing the deflection angle of flap until reach to the maximum improvement and then start to slightly drop. The optimum flap deflection angle changes according to the wind speed values and the optimum values are 5, 10 and 15 degrees at wind speed of 4, 7 and 10m/s respectively. The percentage improvement in lift force is about of 9%, 17.06%, 42.25% and in lift-to-drag ratio is about of 3%, 5%, 23.7% and in power coefficient of about 4.5% %, 17.5%, 17.7% for each wind speed respectively. This is mainly due to improving the stall characteristics over the blade especially near the tip region.

The aviation industry continues to innovate onboard systems to enhance efficiency. This research examines the role of engine intake performance, specifically pressure recovery (PR), in improving the performance of a three-spool turbofan engine. Variations in an aircraft's flight path have a direct impact on pressure recovery. A computational fluid dynamics (CFD) model was first developed to assess pressure recovery at various angles of attack (0°, 10°, 20°, 30°, and 40°) at a Mach number of 0.82 and an altitude of 12,000 meters. Using ANSYS FLUENT® 19 with the Spalart-Allmaras turbulence model, the simulations revealed that the pressure recovery is highest at lower angles and gradually decreases at higher angles, with values between 0.92 and 0.98. MATLAB was then employed to analyze how intake pressure recovery affects the performance of a three-spool turbofan engine under similar conditions, with a Mach number range from 0.2 to 1. The results showed that improved pressure recovery led to a 4% enhancement in specific thrust, a 3% reduction in thrust-specific fuel consumption, and overall gains in engine efficiency. The study suggests that maintaining high pressure recovery is crucial to optimizing engine performance.