Aviation

Tribological properties of thermoplastic elastomer used in 3D printing technology

2024-05-28T18:25:54+03:00

The use of thermoplastic elastomers (TPE) in 3D printing technology enables the use of this technology to produce prototype seals with an unusual shape or design solution. Tribological tests were carried out on a pin-on-disc test stand. The influence of contact pressure and sliding velocity on the friction coefficient of the TPE-steel friction pair under mixed lubrication conditions was analyzed. Based on the obtained tribological test results, it was found that the coefficient of friction of the thermoplastic TPE elastomer on steel in the presence of hydraulic oil (mixed lubrication) at a sliding velocity below 1 m/s does not exceed μ = 0.25. The obtained friction coefficient values are comparable to the results for other elastomeric materials used for technical seals. It was found that the influence of contact pressure on the value of the friction coefficient in the tested friction pairs is varied and depends, for example, on the sliding velocity. It was recommended to carry out research on the assessment of durability (wear intensity) and structure (porosity) of the material in elements manufactured using 3D printing to obtain full knowledge of the possibility of using these materials in the area of technical aircraft seals.

A comprehensive analysis of competency and training perspectives among air traffic controllers

2024-05-28T18:25:54+03:00

The aim of the study was to examine air traffic controllers’ perspectives on competency, performance, and training. Analysing data from 182 participants, the results revealed a strong focus on competency with the controllers actively evaluating their skills. Notably, the experienced controllers (19+ years) displayed a heightened commitment to continuous learning, reflected in their higher participation in individual training and perception of its value for their career goals and adaptation to the evolving aviation industry. Competency-based training programs also received positive feedback across experience levels, highlighting their potential effectiveness. Moreover, training was universally perceived as key to performance throughout careers. These results highlighted the importance of fostering a culture of continuous learning within air traffic control (ATC) communities. Aviation authorities can support this by providing diverse training opportunities, including individual and competency-based programs. Future research should continue investigating the impact of technological advancements and AI/machine learning on ATC competencies, the effectiveness of competency-based training programs, and the role of individual training in competency development, ultimately ensuring the vital role of air traffic controllers in aviation safety.

Machine learning methods as applied to modelling thermal conductivity of epoxy-based composites with different fillers for aircraft

2024-05-31T18:25:57+03:00

The thermal conductivity coefficient of epoxy composites for aircraft, which are reinforced with glass fiber and filled with aerosil, γ-aminopropylaerosil, aluminum oxide, chromium oxide, respectively, was simulated. To this end, various machine learning methods were used, in particular, neural networks and boosted trees. The results obtained were found to be in good agreement with the experimental data. In particular, the correlation coefficient in the test sample was 0.99%. The prediction error of neural networks in the test samples was 0.5; 0.3; 0.2%, while that of boosted trees was 1.5; 0.9%.

Investigation of aerodynamic characteristics of swept C-wing configurations at transonic speed using design of experiments and computational fluid dynamics

2024-06-27T18:26:26+03:00

Authors investigated the aerodynamic characteristics of backward swept (BSCW) and forward swept (FSCW) C-wing configurations at transonic speed using Design of Experiments (DoE) and Computational Fluid Dynamics (CFD), aiming to enhance aircraft performance. Five geometric parameters for C-winglet design were identified from the literature. A quarter fractional factorial approach for the DoE was employed to analyse the effect of these parameters on aerodynamic characteristics at a constant Mach number and angle of attack of 0.8395 and 3.06°, respectively. Numerical results confirm the accuracy of the regression model in predicting aerodynamic coefficients, while normal plot highlight influential geometric parameters. Retrofitting C-winglets at the wingtips increases the aerodynamic performance by approximately 9.38% and 9.74% for BSCW and FSCW configurations respectively, compared to wings without C-winglets. The study demonstrates that utilizing a large cant angle and sweep angle of 60°, along with a low taper ratio of 0.562 for both the vertical and horizontal winglets, as well as a low cant angle of 90° for the horizontal winglet, reduces shockwave interactions on the C-winglet surface, consequently leading to a reduction in drag. It was concluded that the geometric parameters of the C-winglet play an integral role in designing new aircraft aimed at reducing drag.