This paper aims to present a comprehensive investigation to obtain the structural calculations needed to design a rigid panel of aluminum alloy for the wing box beam of an ATR 72–500 aircraft. For this design process, several types of materials, including composites like CFRP, are considered so it is possible to compare the actual existing part made of aluminum to them, thus checking the advantages these new materials offer. The research presents an introduction to structural design and provides a study of the relevant literature. The aircraft's principal characteristics and performance abilities were collected so that structural loads can be computed. Research used several methods, a design using conventional methods, applying the theory of elasticity is performed using the Theory of Farrar, allowing us to obtain an analytical solution to the problem, followed by checking the obtained results using Ansys FEM software combined with the parts being designed with CATIA. Furthermore, this same panel is calculated using composite materials instead of conventional aluminum, allowing us to compare both solutions. This research shed light on the intricate process of aircraft structural design, materials selection, and calculation methodologies, highlighting the ongoing pursuit of new and advanced materials. This paper makes clear that using composite materials presents several advantages over traditional ones, allowing for lighter, safer, more fuel-efficient, and more sustainable aircraft. The use of composite materials in the construction of airplane structures is driven by many factors. The results show that the chosen composite materials reduce weight, are durable, have low maintenance requirements, reduce noise, enhance fuel economy, and are resistant to corrosion.
Problems of energy security, diversification of energy sources, and improvement of technologies (including alternatives) for obtaining motor fuels have become a priority of science and practice today. Many scientists devote their scientific research to the problems of obtaining effective brands of alternative (reformulated) motor fuels. Our scientific school also deals with the problems of the rational use of traditional and alternative motor fuels.This article focused on advances in motor fuel synthesis using natural, associated, or biogas. Different raw materials are used for GTL technology: biomass, natural and associated petroleum gases. Modern approaches to feed gas purification, development of Gas-to-Liquid-technology based on Fischer–Tropsch synthesis, and liquid hydrocarbon mixture reforming are considered.Biological gas is produced in the process of decomposition of waste (manure, straw, grain, sawdust waste), sludge, and organic household waste by cellulosic anaerobic organisms with the participation of methane fermentation bacteria. When 1 tonne of organic matter decomposes, 250 to 500–600 cubic meters of biogas is produced. Experts of the Bioenergy Association of Ukraine estimate the volume of its production at 7.8 billion cubic meters per year. This is 25% of the total consumption of natural gas in Ukraine. This is a significant raw material potential for obtaining liquid hydrocarbons for components of motor fuels.We believe that the potential for gas-to-liquid synthetic motor fuels is associated with shale and coalfield gases (e.g. mine methane), methane hydrate, and biogas from biomass and household waste gases.
Machine learning models have proven to be reliable methods in classification tasks. However, little research has been conducted on the classification of dwelling characteristics based on smart meter and weather data before. Gaining insights into dwelling characteristics, which comprise of the type of heating system used, the number of inhabitants, and the number of solar panels installed, can be helpful in creating or improving the policies to create new dwellings at nearly zero-energy standard. This paper compares different supervised machine learning algorithms, namely Logistic Regression, Support Vector Machine, K-Nearest Neighbor, and Long-short term memory, and methods used to correctly implement these algorithms. These methods include data pre-processing, model validation, and evaluation. Smart meter data, which was used to train several machine learning algorithms, was provided by Groene Mient. The models that were generated by the algorithms were compared on their performance. The results showed that the Long-short term memory performed the best with 96% accuracy. Cross Validation was used to validate the models, where 80% of the data was used for training purposes and 20% was used for testing purposes. Evaluation metrics were used to produce classification reports, which indicates that the Long-short term memory outperforms the compared models on the evaluation metrics for this specific problem.
