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The reduction in the inner diameter of the artery due to the creation of atheromatic plaque on the artery lumen, known as artery stenosis, disrupts the blood flow, leading to medical complications, which can be fatal. The angioplasty procedure aims to reopen the artery and uses a stent to keep it open. In this study, an effort is made to determine the point of the stent, the plaque and the artery during the expansion phase of the angioplasty using the in silico Finite Element Analysis method. A literature-based design was chosen for the stent geometry, whereas simplified shapes of the balloon and the two artery layers were used. Additionally, two plaque designs were the benchmark for the eight distinct artery stenosis models within the Abaqus environment. In the context of stent angioplasty simulations, failure patterns were investigated. An inverse relationship was observed between artery stenosis and pressure at the artery failure point, while an increased danger of interlaminar failure was detected in models with larger artery stenosis. This study verifies the necessity for the inclusion of interlaminar failure in future angioplasty research. [ABSTRACT FROM AUTHOR]

Runway Safety Assistant Foreseeing Excursions (RUNSAFE) is a complete embedded system solution, that predicts a potential runway overrun of a civil aviation aircraft during takeoff and landing. This work examines the feasibility of such a system, through the algorithms and computations that predict the overruns. The system executes both static and dynamic calculations, with the former being dependent on and the latter independent to the user's inputs. Their outcomes and the runway's length are compared in real time to assess if the process will end up in an overrun. All inputs are specifically selected to either be available to the pilots or be retrieved from the existing avionics systems of the cockpit. A performance evaluation is conducted on both static and dynamic calculations, and metrics unveil the accuracy of the predictions and the time needed to converge to a reliable result. The solution is adapted for a Boeing 737-800 aircraft with CFM56-7B engines, but the calculations also apply for similar aircraft equipped with tricycle landing gear and turbofan engines, namely the whole Boeing 737 family, the Airbus A320 family, etc. The system is aligned with current standards and certification specifications, where applicable. [ABSTRACT FROM AUTHOR]

This study aimed to optimize the geometry of composite stepped repair patches, using a parametric algorithm to automate the process due to the complexity of the optimization problem and various factors affecting efficiency. More specifically, the algorithm initially calculates the equivalent strengths of the repaired laminate plate according to a max stress criterion, then calculates the dimensions of several elliptical repair patches, taking into account several design methods extracted from the literature. Next, it creates their finite element models and finally, the code conducts an assessment of the examined patch geometries, given specific user-defined criteria. In the end, the algorithm reaches a conclusion about the optimum patch among the designed ones. The algorithm has the potential to run for many different patch geometries. In the current research, five patch geometries were designed and modeled under uniaxial compressive loading at 0°, 45° and 90°. Overall, the code greatly facilitated the design and optimization process and constitutes a useful tool for future research. The results revealed that elliptical stepped patches can offer a near-optimum solution much more efficient than that of the conservative option of the circular patch, in terms of both strength and volume of healthy removed material. [ABSTRACT FROM AUTHOR]

The pursuit of more efficient transport has led engineers to develop a wide variety of aircraft configurations with the aim of reducing fuel consumption and emissions. However, these innovative designs introduce significant aeroelastic couplings that can potentially lead to structural failure. Consequently, aeroelastic analysis and optimization have become an integral part of modern aircraft design. In addition, aeroelastic testing of scaled models is a critical phase in aircraft development, requiring the accurate prediction of aeroelastic behavior during scaled model construction to reduce costs and mitigate the risks associated with full-scale flight testing. Achieving a high degree of similarity between the stiffness, mass distribution and flow field characteristics of scaled models and their full-scale counterparts is of paramount importance. However, achieving similarity is not always straightforward due to the variety of configurations of modern lightweight aircraft, as identical geometry cannot always be directly scaled down. This configuration diversity has a direct impact on the aeroelastic response, necessitating the use of computational aeroelasticity tools and optimization algorithms. This paper presents the development of an aeroelastic scaling framework using multidisciplinary optimization. Specifically, a parametric Finite Element Model (FEM) of the wing is created, incorporating the parameterization of both thickness and geometry, primarily using shell elements. Aerodynamic loads are calculated using the Doublet Lattice Method (DLM) employing twist and camber correction factors, and aeroelastic coupling is established using infinite plate splines. The aeroelastic model is then integrated within an Ant Colony Optimization (ACO) algorithm to achieve static and dynamic similarity between the scaled model and the reference wing. A notable contribution of this work is the incorporation of internal geometry parameterization into the framework, increasing its versatility and effectiveness. [ABSTRACT FROM AUTHOR]

In this study, tests were carried out on repaired 300 × 250 mm composite stiffened panels. The initial damage was assumed to be extended in a depth of the first six plies and a repair with scarf ratio of 1:50 was employed. A circular repair took place, utilizing laser-assisted ablation and the wet patches approach. The panels were tested in compression and Digital Image Correlation (DIC) and Acoustic Emission (AE) were used in order to monitor the damage initiation and propagation of the panels. In addition, Phased Array (PA) was used in order to monitor the patch failure. Detailed Finite Elements (FE) models were validated against the test findings and their performance as a prediction tool for the performance of repairs on composite aeronautical structures was assessed. [ABSTRACT FROM AUTHOR]

Aviation authorities require, from aircraft seat manufacturers, specific performance metrics that maximize the occupants' chances of survival in the case of an emergency landing and allow for the safe evacuation of the aircraft cabin. Therefore, aircraft seats must comply with specific requirements with respect to their structural integrity and potential occupant injuries, which are certified through the conduction of costly, full-scale tests. To reduce certification costs, computer-aided methods such as finite element analysis can simulate and predict the responses of different seat configuration concepts and potentially save time and development costs. This work presents one of the major steps of an aircraft seat development, which is the design and study of preliminary design concepts, whose structural and biomechanical response will determine whether the concept seat model is approved for the next steps of development. More specifically, a three-occupant aircraft seat configuration is studied for crash landing load cases and is subjected to modification iterations from a baseline design to a composite one for its structural performance, its weight reduction and the reduction of forces transmitted to the passengers. [ABSTRACT FROM AUTHOR]

This paper addresses the problem of singularity avoidance for a 4-Control Moment Gyroscope (CMG) pyramid cluster, as used for the attitude control of a satellite using machine learning (ML) techniques. A data-set, generated using a heuristic algorithm, relates the initial gimbal configuration and the desired maneuver—inputs—to a number of null space motions the gimbals have to execute—output. Two ML techniques—Deep Neural Network (DNN) and Random Forest Classifier (RFC)—are utilized to predict the required null motion for trajectories that are not included in the training set. The principal advantage of this approach is the exploitation of global information gathered from the whole maneuver compared to conventional steering laws that consider only some local information, near the current gimbal configuration for optimization and are prone to local extrema. The data-set generation and the predictions of the ML systems can be made offline, so no further calculations are needed on board, providing the possibility to inspect the way the system responds to any commanded maneuver before its execution. The RFC technique demonstrates enhanced accuracy for the test data compared to the DNN, validating that it is possible to correctly predict the null motion even for maneuvers that are not included in the training data. [ABSTRACT FROM AUTHOR]