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.
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.
According to the International Civil Aviation Organization, the world aviation air traffic has grown by an average yearly rate of 5% over the last thirty years, until the devastating downturn brought on by the COVID crisis of 2020. Regardless of the current situation, there are still a number of issues and challenges that the industry is confronted with, not the least of which are related to sustainability, the conversion to electrical usage, the challenge of increasing propulsion efficiency in conventional propulsion, the digital transformation of the entire ecosystem, etc. In response, system developers and researchers in the field are working on a number of key technologies and methodologies to solve some of these issues. The Sustainable Aviation Research Society (SARES), a global organization that seeks to encourage research in this area and helps disseminate knowledge via conferences and symposia, has been organizing meetings to promote sustainable aviation over the five years. Three of these are the International Symposium on Sustainable Aviation (ISSA), International Symposium on Electric Aviation and Autonomous Systems (ISEAS), and the International Symposium on Aircraft Technology, MRO, and Operations (ISATECH).
