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The purpose of this paper is to formulate a specialized artificial neural network algorithm utilizing radial basis function (RBF) for modeling of time to failure of aircraft engine turbines.

The model uses training failure data collected from operators of turboprop aircraft working in harsh desert conditions where sand erosion is a detrimental factor in reducing turbine life. Accordingly, the model is more suited to accurate prediction of life of critical components of such engines. The used RBF employs a closest neighbor type of classifier and the hidden unit’s activation is based on the displacement between the early prototype and the input vector.

The results of the algorithm are compared to earlier work utilizing Weibull regression modeling, as well as Feed Forward Back Propagation NN. The results show that the failure rates estimated by RBF more closely match actual failure data than the estimations by both other models. The trained model showed reasonable accuracy in predicting future failure events. Moreover, the technique is shown to have comparatively higher efficiency even with reduced number of neurons in each layer of ANN. This significantly decreases computation time with minimum effect on the accuracy of results.

Using RBF technique significantly decreases the computational time with minimum effect on the accuracy of results.

Aeroplanes are repairable systems consisting of several non‐repairable parts; tyres are one of these. Proper record of failure data is valuable in interpreting the failure pattern, for comparative evaluation of the quality of tyres of various manufacturers, and for prediction of the future needs in a specified planning horizon or for specified operational hours. Analyses failure data of several aeroplane tyres in an aviation set‐up, and interprets these in a reliability framework. Three parameters Weibull model is found to be quite appropriate for reliability characterization of tyres. These reliability models can be effectively integrated into a computerized material requirement planning system of the aviation facility to forecast the number of tyres needed for a given planning horizon.

States that aircraft tyres are critical for safety and they must have high reliability. Compares two policies for tyre replacement and their reliability and cost which are for Saudi aviation and the international standards.

The purpose of this paper is to achieve high‐performance aerofoils that enable delayed stall conditions and achieve high lift to drag ratios.

The unsteady Reynolds averaged Navier‐Stokes equations are employed in conjunction with a shear stress transport (κ‐ω) turbulence model. A control equation is designed and implemented to determine the temporal response of the actuator. A rotating element, in the form of an actuator disc, is embedded on the leading edge of NACA 0012 aerofoil, to inject momentum into the wake region. The actuator disc is rotated at different angular speeds, for angles of attack (α) between 00 and 240.

Phenomena such as flow separation, wake vortices, delayed stall, wake control, etc. are numerically investigated by means of streamlines, streaklines, isobars, etc. Streamwise and cross‐stream forces on the aerofoil are obtained. The influence of momentum injection parameter (ξ) on the fluid flow patterns, and hence on the forces acting on the streamlined body are determined. A synchronization‐based coupling scheme is designed and implemented to achieve annihilation of wake vortices. A delayed stall angle resulted with an attendant increase in maximum lift coefficient. Due to delay and/or prevention of separation, drag coefficient is also reduced considerably, resulting in a high‐performance lifting surface.

The practicality of momentum injection principle requires both wide ranging and intensive further studies to move forward beyond the proof of concept stage.

Determination of forces and moments on an aerofoil is of vital interest in aero‐dynamic design. Perhaps, runways of the future can be shorter and/or more pay load can be carried by an aircraft, for the same stall speed.

The paper describes how a synchronization‐based coupling scheme is designed and implemented along with the RANS solver. Furthermore, it is tested to verify the dynamic adaptability of the wake vortex annihilation for NACA 0012 aerofoils.

The purpose of this paper is to estimate different air–fuel ratio motor shaft speed and fuel flow rates under the performance parameters depending on the indices of combustion efficiency and exhaust emission of the engine, a turboprop multilayer feed forward artificial neural network model. For this purpose, emissions data obtained experimentally from a T56-A-15 turboprop engine under various loads were used.

The designed multilayer feed forward neural network models consist of two hidden layers. 75% of the experimental data used was allocated as training, 25% as test data and cross-referenced by the k-fold four value. Fuel flow, rotate per minute and air–fuel ratio data were used for the training of emission index input values on the designed models and EI_CO, EI_CO2, EI_NO2 and EI_UHC data were used on the output. In the system trained for combustion efficiency, EI_CO and EI_UHC data were used at the input and fuel combustion efficiency data at the output.

Mean square error, normalized mean square error, absolute mean error functions were used to evaluate the error obtained from the system as a result of the test. As a result of modeling the system, absolute mean error values were 0.1473 for CO, 0.0442 for CO₂, 0.0369 for UHC, 0.0028 for NO₂, success for all exhaust emission data was 0.0266 and 7.6165e-10 for combustion efficiency, respectively.

This study has been added to the literature T56-A-15 turboprop engine for the current machine learning methods to multilayer feed forward neural network methods, exhaust emission and combustion efficiency index value calculation.

Flow separation over an aircraft’s wing beyond a specific angle of attack is challenging. Flow boundary layer manipulation has been investigated to improve aerofoil lift and mitigate flow separation difficulties including stall and drag. This is solved via active or passive flow control. Active flow control method moving surface boundary (MSB) enhances shear flow momentum, making it effective. MSB is easier than suction and blowing. Asymmetrical airfoils, which generate lift in aircraft wings, have received less MSB research than symmetrical ones. The purpose of this study is to asses the design efficacy of MSB’s NACA 2412 aerofoil.

To observe the performance of MSB in NACA 2412, a computational model has been created, and aerodynamical performance has been analyzed.

The study results show that the NACA 2412 with MSB has better aerodynamic efficiency than the NACA 2412 base design. It works best when it reaches its optimal speed and the delay in flow separation works well.

Limitations may include specific aerofoil applicability, external factors and simulation constraints. Implications guide future research for broader insights and applications.

Improving asymmetrical aerofoil performance, mitigating stall effects, reducing drag and optimizing designs with moving surface boundary. Insights gained can enhance overall aircraft efficiency and flow control techniques.

The MSB flow control in a chambered aerofoil is less explored and not explored enough in wing-based aerofoils, and the optimal cylinder speed ratio trend has been discussed at each angle of attack studied.

The purpose of this paper is to consider the corrosion properties of laser nitrided Ti‐6Al‐4V alloys that have been reported previously by several researchers.

Different kinds of surface nitriding methods of titanium alloys, such as plasma nitriding, ion nitriding, gas and laser nitriding, are introduced. Microstructure changes, such as phase formation and the influence of laser processing parameters in laser nitriding layers of Ti‐6Al‐4V alloys, were investigated using scanning electron microscope, transmission electron microscope, X‐ray photo‐electron spectroscopy, and X‐ray diffraction. Based on investigations presented in the literature, the effect of laser nitriding on the corrosion behavior of Ti‐6Al‐4V alloy was reviewed.

By regulating the laser processing parameter, the microstructure of the nitrided layer can be controlled to optimize corrosion properties. This layer improves corrosion behavior in most environments, due to the formation of a continuous TiNxOy passive film, which can retard the ingress of corrosive ions into the substrate and can maintain a constant value of a current density. Therefore, the laser gas nitrided specimens have a relatively noble corrosion potential and a very small corrosion current, as compared to untreated specimens.

This paper comprises a critical review, and its collection of references is useful. It summarizes current knowledge in laser surface treatment research.

The purpose of this paper is to investigate mechanical and metallurgical variations at interfaces of commercial austenitic‐stainless steel and copper materials welded by friction welding.

In this paper, austenitic‐stainless commercial steel and copper materials are welded using the friction welding method. The optimum parameters are obtained for the joints. The joints are applied to the tensile and micro‐hardness tests. Then, micro‐ and macro‐photos of the joints are examined.

It is found that some of the welds show poor strength depending on some accumulation of alloying elements at the interface result of temperature rise and the existence of intermetallic layers.

It would be interesting to search about the toughness values and fatigue behaviour of the joints. It could be a good idea for future work to concentrate on the friction welding of these materials.

Friction welding can be achieved at high‐production rates and therefore is economical in operation. In applications where friction welding has replaced other joining processes, the production rate has been increased substantially.

The main value of this paper is to contribute to the literature on friction welding of dissimilar materials.

This study aims to investigate the cybersecurity awareness manifested as protective behavior to explain self-disclosure in social networking sites. The disclosure of information about oneself is associated with benefits as well as privacy risks. The individuals self-disclose to gain social capital and display protective behaviors to evade privacy risks by careful cost-benefit calculation of disclosing information.

This study explores the role of cyber protection behavior in predicting self-disclosure along with demographics (age and gender) and digital divide (frequency of Internet access) variables by conducting a face-to-face survey. Data were collected from 284 participants. The model is validated by using multiple hierarchal regression along with the artificial intelligence approach.

The results revealed that cyber protection behavior significantly explains the variance in self-disclosure behavior. The complementary use of five machine learning (ML) algorithms further validated the model. The ML algorithms predicted self-disclosure with an area under the curve of 0.74 and an F1 measure of 0.70.

The findings suggest that costs associated with self-disclosure can be mitigated by educating the individuals to heighten their cybersecurity awareness through cybersecurity training programs.

This study uses a hybrid approach to assess the influence of cyber protection behavior on self-disclosure using expectant valence theory (EVT).