The boarding process of an aircraft is one of the identified bottlenecks in the turnaround when aircraft arrives to an airport.
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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.
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Estimating the remaining useful life (RUL) of an asset lies at the heart of prognostics and health management (PHM) of many operations-critical industries such as aviation. Mod- ern methods of RUL estimation adopt techniques from deep learning (DL). However, most of these contemporary tech- niques deliver only single-point estimates for the RUL without reporting on the confidence of the prediction. This practice usually provides overly confident predictions that can have severe consequences in operational disruptions or even safety. To address this issue, we propose a technique for uncertainty quantification (UQ) based on Bayesian deep learning (BDL). The hyperparameters of the framework are tuned using a novel bi-objective Bayesian optimization method with objectives the predictive performance and predictive uncertainty. The method also integrates the data pre-processing steps into the hyperparameter optimization (HPO) stage, models the RUL as a Weibull distribution, and returns the survival curves of the monitored assets to allow informed decision-making. We vali- date this method on the widely used C-MAPSS dataset against a single-objective HPO baseline that aggregates the two ob- jectives through the harmonic mean (HM). We demonstrate the existence of trade-offs between the predictive performance and the predictive uncertainty and observe that the bi-objective HPO returns a larger number of hyperparameter configurations compared to the single-objective baseline. Furthermore, we see that with the proposed approach, it is possible to configure models for RUL estimation that exhibit better or comparable performance to the single-objective baseline when validated on the test sets.
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