Advances in geometrical parametrization and reduced order models and methods for computational fluid dynamics problems in applied sciences and engineering: overview and perspectives

Several problems in applied sciences and engineering require reduction techniques in order to allow computational tools to be employed in the daily practice, especially in iterative procedures such as optimization or sensitivity analysis. Reduced order methods need to face increasingly complex problems in computational mechanics, especially into a multiphysics setting. Several issues should be faced: stability of the approximation, efficient treatment of nonlinearities, uniqueness or possible bifurcations of the state solutions, proper coupling between fields, as well as offline-online computing, computational savings and certification of errors as measure of accuracy. Moreover, efficient geometrical parametrization techniques should be devised to efficiently face shape optimization problems, as well as shape reconstruction and shape assimilation problems. A related aspect deals with the management of parametrized interfaces in multiphysics problems, such as fluid-structure interaction problems, and also a domain decomposition based approach for complex parametrized networks. We present some illustrative industrial and biomedical problems as examples of recent advances on methodological developments. \ua9 The author

Economic Impact Assessment of Structural Health Monitoring Systems on the Lifecycle of a Helicopter Blade

Structural Health Monitoring Systems (SHMS) are widely investigated in the literature, however, their application in the aerospace industry is still limited. One of the reasons lies in the lack of methods evaluating their economic impact on the lifecycle of the structures. In this work, the economic impact of a Fibre Bragg Grating (FBG)-based SHMS is assessed on a composite helicopter tail rotor blade, considering two perspectives: Beginning Of Life (BOL), and Middle Of Life (MOL). Two scenarios are compared according to the Life Cycle Costing methodology: the current one, and the one including the SHMS. In the BOL, the FBG's replace the thermocouples, adopted for the development of the curing cycle; while during the blade certification tests, the FBG's replace the strain gauges. In the MOL the SHMS performs automatic scheduled inspections of the tail rotor blade. The lifecycle of the helicopter is implemented in the Probabilistic Damage Tolerance Analysis to compare two scenarios having the same blade Probability Of Failure. The detection performance and false alarms of the SHMS are considered. Results show that an economic benefit may be achieved using the SHMS in the development of a new blade, potentially reducing the number of blade tested and the number of autoclave curing cycles. Moreover, the scheduled detailed inspection interval can be extended if automatic SHMS inspections are performed in addition to it, maintaining the same Probability Of Failure. However, due to the frequent impacts with foreign objects, the repair complexity of a sensorized structure, and its higher cost compared to a standard blade, the economic impact of the SHMS is evaluated as negative on the lifecycle of the blade

Economic Impact Assessment of Structural Health Monitoring Systems on Helicopter Blade Beginning of Life

Te economic impact of Structural Health Monitoring Systems based on optical fbre sensors is assessed in the development ofcomposite helicopter rotor blades. Hence, the focus of this analysis is on the helicopter’s Beginning Of Life stage. Two applicationsof the Structural Health Monitoring System are considered in the development of composite blades: curing cycle development andaccomplishment of laboratory and fight certifcation tests. Optical fbre sensors measure the temperature feld during the curingcycle and strain feld during the laboratory tests and allow load identifcation during the load survey activity. It was found thatStructural Health Monitoring Systems can potentially lead to economic benefts during the development of the blade provide thata reduction in the number of curing cycles and number of blades tested is achieved as a consequence of the improvement of thetemperature and strain feld quality. Moreover, an economic beneft could be achieved during the load survey activity, needed tocomplete the certifcation of the composite blade, avoiding the periodical maintenance of the applied strain gauges acquiring thestrains during the fight

Application of Artificial Neural Networks to a Model of a Helicopter Rotor Blade for Damage Identification in Realistic Load Conditions

Monitoring the integrity of aeronautical structures is fundamental for safety. Structural Health Monitoring Systems (SHMSs) perform real-time monitoring functions, but their performance must be carefully assessed. This is typically done by introducing artificial damages to the components; however, such a procedure requires the production and testing of a large number of structural elements. In this work, the damage detection performance of a strain-based SHMS was evaluated on a composite helicopter rotor blade root, exploiting a Finite Element (FE) model of the component. The SHMS monitored the bonding between the central core and the surrounding antitorsional layer. A damage detection algorithm was trained through FE analyses. The effects of the load’s variability and of the damage were decoupled by including a load recognition step in the algorithm, which was accomplished either with an Artificial Neural Network (ANN) or a calibration matrix. Anomaly detection, damage assessment, and localization were performed by using an ANN. The results showed a higher load identification and anomaly detection accuracy using an ANN for the load recognition, and the load set was recognized with a satisfactory accuracy, even in damaged blades. This case study was focused on a real-world subcomponent with complex geometrical features and realistic load conditions, which was not investigated in the literature and provided a promising approach to estimate the performance of a strain-based SHMS

Trabajo Fin de Máster

Este trabajo de fin de máster es una memoria de los aspectos más significativos del curso en relación a la programación de Unidades Didácticas y Proyectos Educativos. Asimismo recoge las impresiones y el trabajo realizado en periodo de prácticas en el centro Escuelas Pías y las propuestas de mejora personales para el máster y la propia profesión docente

Mathematical modelling and computational reduction of molten glass fluid flow in a furnace melting basin

In this work, we present the modelling and numerical simulation of a molten glass fluid flow in a furnace melting basin. We first derive a model for a molten glass fluid flow and present numerical simulations based on the Finite Element Method (FEM). We further discuss and validate the results obtained from the simulations by comparing them with experimental results. Finally, we also present a non-intrusive Proper Orthogonal Decomposition (POD) based on Artificial Neural Networks (ANN) to efficiently handle scenarios which require multiple simulations of the fluid flow upon changing parameters of relevant industrial interest. This approach lets us obtain solutions of a complex 3D model, with good accuracy with respect to the FEM solution, yet with negligible associated computational times

Fast simulations of patient-specific haemodynamics of coronary artery bypass grafts based on a POD-Galerkin method and a vascular shape parametrization

In this work a reduced-order computational framework for the study of haemodynamics in three-dimensional patient-specific configurations of coronary artery bypass grafts dealing with a wide range of scenarios is proposed. We combine several efficient algorithms to face at the same time both the geometrical complexity involved in the description of the vascular network and the huge computational cost entailed by time dependent patient-specific flow simulations. Medical imaging procedures allow to reconstruct patient-specific configurations from clinical data. A centerlines-based parametrization is proposed to efficiently handle geometrical variations. POD-Galerkin reduced-order models are employed to cut down large computational costs. This computational framework allows to characterize blood flows for different physical and geometrical variations relevant in the clinical practice, such as stenosis factors and anastomosis variations, in a rapid and reliable way. Several numerical results are discussed, highlighting the computational performance of the proposed framework, as well as its capability to carry out sensitivity analysis studies, so far out of reach. In particular, a reduced-order simulation takes only a few minutes to run, resulting in computational savings of 99% of CPU time with respect to the full-order discretization. Moreover, the error between full-order and reduced-order solutions is also studied, and it is numerically found to be less than 1% for reduced-order solutions obtained with just O(100) online degrees of freedom. (C) 2016 Elsevier Inc. All rights reserved

Expression of the Atypical Chemokine Receptor D6 in Human Alveolar Macrophages in COPD

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