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 (R2), 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 (CL/CD) and power coefficient (Cp) 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 (CL/CD) and power coefficient (Cp) 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.

The design and investigation of an intelligent controller for hardware-in-the-loop (HIL) implementation of hybrid electric vehicles (HEVs) are proposed in this article. The proposed intelligent controller is adopted based on the enhancement of a model predictive controller (MPC) by an artificial neural network (ANN) approach. The MPC-based ANN (NNMPC) is proposed to control the speed of HEVs for a simulation system model and experimental HIL test systems. The HIL is established to assess the performance of the NNMPC to control the velocity of HEVs in an experimental environment. The real-time environment of HIL is implemented through a low-cost approach such as the integration of an Arduino Mega 2560 and a host Lenovo PC with a Core i7 @ 3.4 GHz processor. The NNMPC is compared with a proportional–integral (PI) controller, a classical MPC, and two different settings of the ANN methodology to verify the efficiency of the proposed intelligent NNMPC. The obtained results show a distinct behavior of the proposed NNMPC to control the speed of HEVs with good performance based on the distinct transient response, minimum error steady state, and system robustness against parameter perturbation.

In this paper, Internet of Things (IoT) and artificial intelligence (AI) are employed to solve the issue of energy consumption in a case study of an education laboratory. IoT enables deployment of AI approaches to establish smart systems and manage the sensor signals between different equipment based on smart decisions. As a result, this paper introduces the design and investigation of an experimental building management system (BMS)-based IoT approach to monitor status of sensors and control operation of loads to reduce energy consumption. The proposed BMS is built on integration between a programmable logic controller (PLC), a Node MCU ESP8266, and an Arduino Mega 2560 to perform the roles of transferring and processing data as well as decision-making. The system employs a variety of sensors, including a DHT11 sensor, an IR sensor, a smoke sensor, and an ultrasonic sensor. The collected IoT data from temperature sensors are used to build an artificial neural network (ANN) model to forecast the temperature inside the laboratory. The proposed IoT platform is created by the ThingSpeak platform, the Bylink dashboard, and a mobile application. The experimental results show that the experimental BMS can monitor the sensor data and publish the data on different IoT platforms. In addition, the results demonstrate that operation of the air-conditioning, lighting, firefighting, and ventilation systems could be optimally monitored and managed for a smart system with an architectural design. Furthermore, the results prove that the ANN model can perform a distinct temperature forecasting process based on IoT data.

This paper introduces a new intelligent tuning for the model predictive control (MPC) based on an effective intelligent algorithm named the bat-inspired algorithm (BIA) for the aircraft longitudinal flight. The tuning of MPC horizon parameters represents the main challenge to adjust the system performance. So, the BIA algorithm is intended to overcome the tuning issue of MPC parameters due to conventional methods, such as trial and error or designer experience. The BIA is adopted to explore the best parameters of MPC based on the minimization of various time domain objective functions. The suggested aircraft model takes into account the aircraft dynamics and constraints. The nonlinear dynamics of aircraft, gust disturbance, parameters uncertainty and environment variations are considered the main issues against the control of aircraft to provide a good flight performance. The nonlinear autoregressive moving average (NARMA-L2) controller and proportional integral (PI) controller are suggested for aircraft control in order to evaluate the effectiveness of the proposed MPC based on BIA. The proposed MPC based on BIA and suggested controllers are evaluated against various criteria and functions to prove the effectiveness of MPC based on BIA. The results confirm that the accomplishment of the suggested BIA-based MPC is outstanding to the NARMA-L2 and traditional PI controllers according to the cross-correlation criteria, integral time absolute error (ITAE), system overshoot, response settling time, and system robustness.

The significance of cybersecurity and cyber resilience in the aviation sector cannot be ignored, and this fact has been highlighted time and again due to frequent cyberattacks prior and during the COVID-19 pandemic. This paper presents an analysis of the relevant studies that addressed the impact of the COVID-19 pandemic on cybersecurity generally, and in the aviation sector in particular, including identifying the most common increased cyber-attacks associated with the outbreak of COVID-19. Our analysis shows that commonly increased cyber-attacks associated with the outbreak of COVID-19 in aviation are phishing and ransomware. Furthermore, the likelihood of phishing and ransomware attacks is very likely during the pandemic than before it while the impact on flight operations is low, and the subsequent risk level is tolerable during the pandemic. Additionally, we propose a set of possible mitigation measures for those cyber-attacks.

The present study considers the use of a slat near the leading edge of an airfoil to delay or completely cancel separation and thus improves the performance of small wind turbines, in addition to lowering the cost of the blade by reducing the used material. Flow around the S809 airfoil with a slat is numerically simulated using ANSYS 2019 R1 (CFD) program. The present work concentrates on two geometrical parameters namely the location of the slat relative to the base airfoil and the chord size of the slat. Relative to the total chord length of the main airfoil, a slat with a chord length 10% is located at range of distances +3.6% to +10.8% in y direction, slat located at 9% shows the best lift coefficient. To study the effect of slat size, slat sizes range from 5% to 12.5% are numerically investigated, slat size 7.5% shows the best lift coefficient and attached flow over the airfoil for a wide range of angles of attack. The improvement is achieved with the same total chord length with 12% saving of material which corresponds to 6% of the total cost of the turbine.

In Egypt, large quantities of coarse granite wastes are produced annually during the quarrying operations of granite rocks. This waste represents a potentially useful source of material for a variety of applications such as a filler material in epoxy granite composite material. In this work a new eco-friendly composite material studied as a substitute for machine tools traditional materials, like cast iron, to produce better efficiency with lower cost. This study aims to investigate the mechanical properties of granite epoxy composite by using the local epoxy (kemapoxy 150) and the granite residues in the Egyptian quarries. The investigated processing variable was epoxy content, and the mechanical characterization ware carried out by compressive and flexural tests according to the ASTM standard method B. Commercially available, Aswan red granite was procured, crushed, and sieved to three size ranges from 0.150 to 8 mm, respectively. Epoxy ratios of 80:20, 85:15 have been used with granite aggregate size mix with small, medium, and coarse size proportions of 50:25:25 respectively for preparing the specimens with granite granular size range (0.150-8) mm. The results show that Epoxy granite composite with granite to epoxy ratio of 80:20% wt. induced the highest compressive strength (72.15 MPa) while the composite with the ratio of 85:15% wt. induced the highest flexural strength (20.1 MPa). Epoxy granite composite show superior results with respect to cement concrete, polyester concrete, and natural granite.

The study aimed in general to identify the importance of social interaction and its impact on the psychological balance of workers in the field of aviation training and its reflection on their professional performance within the work environment and its reflection on the success of the aviation industry that serves all activities in all different life environments. This study relied on the descriptive and comparative approach, as it revealed the relationship between social interaction, psychological balance and professional performance of the study sample of workers in the field of aviation training, and a comparison between pilots, air traffic controllers and administrators. The study sample consisted of n = (150) divided into 50 pilots, 50 air traffic controllers, '50 administrators working in the field of aviation, and the sample was intentionally chosen by the Egyptian Academy of Aviation Sciences and Egypt Air, and 3 measures were used from the researcher's preparation (social interaction scale, psychological balance scale, performance scale Professional) .In addition to the socio-economic level scale of Dr. Muhammad Saafan 2016, the researcher used the SPSS statistical program to analyze and process the data, and the Pearson coefficient was used to ensure the validity of the internal consistency of the questionnaire, as well as to find the Pearson correlation coefficient between each dimension and the total degree of the questionnaire, the Spearman Brown factor for half segmentation and the Alpha Cronbach coefficient to ensure the stability Study tool. The results of the study resulted in a statistically significant relationship in the social interaction between pilots and air traffic controllers in the direction of pilots, as well as the existence of a statistically significant relationship in the psychological balance between pilots compared to air traffic controllers towards pilots, and there is also a statistically significant relationship in the professional performance between pilots and air traffic controllers. In the direction of pilots - there is a statistically significant relationship on the three variables between air traffic controllers and administrative staff towards air traffic controllers. The recommendations of the study included attention to highlighting the role of the air traffic controller and its importance and attention to training a second row to increase the number of air traffic controllers to reduce the intensity of professional pressure, which leads to increased social interaction with them As well as psychological balance, which is the most important axes of psychological stability and mental health .