9 research outputs found

    Development of a multi-objective optimization algorithm based on lichtenberg figures

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    This doctoral dissertation presents the most important concepts of multi-objective optimization and a systematic review of the most cited articles in the last years of this subject in mechanical engineering. The State of the Art shows a trend towards the use of metaheuristics and the use of a posteriori decision-making techniques to solve engineering problems. This fact increases the demand for algorithms, which compete to deliver the most accurate answers at the lowest possible computational cost. In this context, a new hybrid multi-objective metaheuristic inspired by lightning and Linchtenberg Figures is proposed. The Multi-objective Lichtenberg Algorithm (MOLA) is tested using complex test functions and explicit contrainted engineering problems and compared with other metaheuristics. MOLA outperformed the most used algorithms in the literature: NSGA-II, MOPSO, MOEA/D, MOGWO, and MOGOA. After initial validation, it was applied to two complex and impossible to be analytically evaluated problems. The first was a design case: the multi-objective optimization of CFRP isogrid tubes using the finite element method. The optimizations were made considering two methodologies: i) using a metamodel, and ii) the finite element updating. The last proved to be the best methodology, finding solutions that reduced at least 45.69% of the mass, 18.4% of the instability coefficient, 61.76% of the Tsai-Wu failure index and increased by at least 52.57% the natural frequency. In the second application, MOLA was internally modified and associated with feature selection techniques to become the Multi-objective Sensor Selection and Placement Optimization based on the Lichtenberg Algorithm (MOSSPOLA), an unprecedented Sensor Placement Optimization (SPO) algorithm that maximizes the acquired modal response and minimizes the number of sensors for any structure. Although this is a structural health monitoring principle, it has never been done before. MOSSPOLA was applied to a real helicopter’s main rotor blade using the 7 best-known metrics in SPO. Pareto fronts and sensor configurations were unprecedentedly generated and compared. Better sensor distributions were associated with higher hypervolume and the algorithm found a sensor configuration for each sensor number and metric, including one with 100% accuracy in identifying delamination considering triaxial modal displacements, minimum number of sensors, and noise for all blade sections.Esta tese de doutorado traz os conceitos mais importantes de otimização multi-objetivo e uma revisão sistemática dos artigos mais citados nos últimos anos deste tema em engenharia mecânica. O estado da arte mostra uma tendência no uso de meta-heurísticas e de técnicas de tomada de decisão a posteriori para resolver problemas de engenharia. Este fato aumenta a demanda sobre os algoritmos, que competem para entregar respostas mais precisas com o menor custo computacional possível. Nesse contexto, é proposta uma nova meta-heurística híbrida multi-objetivo inspirada em raios e Figuras de Lichtenberg. O Algoritmo de Lichtenberg Multi-objetivo (MOLA) é testado e comparado com outras metaheurísticas usando funções de teste complexas e problemas restritos e explícitos de engenharia. Ele superou os algoritmos mais utilizados na literatura: NSGA-II, MOPSO, MOEA/D, MOGWO e MOGOA. Após validação, foi aplicado em dois problemas complexos e impossíveis de serem analiticamente otimizados. O primeiro foi um caso de projeto: otimização multi-objetivo de tubos isogrid CFRP usando o método dos elementos finitos. As otimizações foram feitas considerando duas metodologias: i) usando um meta-modelo, e ii) atualização por elementos finitos. A última provou ser a melhor metodologia, encontrando soluções que reduziram pelo menos 45,69% da massa, 18,4% do coeficiente de instabilidade, 61,76% do TW e aumentaram em pelo menos 52,57% a frequência natural. Na segunda aplicação, MOLA foi modificado internamente e associado a técnicas de feature selection para se tornar o Seleção e Alocação ótima de Sensores Multi-objetivo baseado no Algoritmo de Lichtenberg (MOSSPOLA), um algoritmo inédito de Otimização de Posicionamento de Sensores (SPO) que maximiza a resposta modal adquirida e minimiza o número de sensores para qualquer estrutura. Embora isto seja um princípio de Monitoramento da Saúde Estrutural, nunca foi feito antes. O MOSSPOLA foi aplicado na pá do rotor principal de um helicóptero real usando as 7 métricas mais conhecidas em SPO. Frentes de Pareto e configurações de sensores foram ineditamente geradas e comparadas. Melhores distribuições de sensores foram associadas a um alto hipervolume e o algoritmo encontrou uma configuração de sensor para cada número de sensores e métrica, incluindo uma com 100% de precisão na identificação de delaminação considerando deslocamentos modais triaxiais, número mínimo de sensores e ruído para todas as seções da lâmina

    Crashworthiness analysis and design optimization of hybrid energy absorption devices: application to aircraft structures

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    Programa Oficial de Doutoramento en Enxeñaría Civil . 5011V01[Abstract]Amid the main research lines for the enhancement of aircraft and automotive designs, structural optimization and crashworthiness studies are at their pinnacle. Means of transport need to be robust and safe, albeit efficiency and lightness cannot be neglected. While active safety systems have avoided innumerable accidents, passive crashworthiness systems need to protect passengers when they do occur. In the event of a crash, modern structures are designed to collapse progressively, dissipating high amounts of kinetic energy and protecting the passengers against abrupt decelerations. Within this broad field of study, the aim of this thesis is that of bettering traditional crash structures by designing and optimizing thin-walled hybrid energy absorbers, and ultimately proving reduced occupant injury levels during representative impact scenarios. The collapsible energy absorbers studied throughout this research originated by combining square metallic tubes with inner cores made from glass-fiber reinforced polymer (GFRP) and foam structures. Honeycombs are studied in depth, showing their outstanding behavior as load bearing structures and identifying the effects of modifying their cell’s shape. Another composite structure investigated was that of an intertwined four-plate star core, slightly less stiff than honeycombs but promising crushing behavior. Foam extrusions are also used as standalone reinforcements and as filling of the inner core’s voids, always enhancing the energy absorption capabilities of specimens. Specimens are characterized according to different crashworthiness metrics, including their energy absorption value, peak force undergone during its collapse and the mass of the components. Moreover, each initial design is subjected to optimization techniques to achieve the utmost from the aforementioned metrics. For that, finite element simulations of axial dynamic loading are parametrized as to obtain variable core heights, material thicknesses and modifiable honeycomb’s cell size and shape. These are later coupled with sampling and metamodeling algorithms, constructing a surrogate model which performs accordingly with the simulation during any fluctuation in the design variables. Later on, the metamodels are single- and multi-objectively optimized with genetic algorithms, yielding various sets of designs that excel in one or more of the selected responses. As a second goals of this work, the previous energy absorber design and the methodology used are to be applied in a significant impact scenario of a passenger vehicle. A drop-test numerical simulation from a Boeing 737-200 fuselage section is developed and correlated with extensive experimental data, later analyzing the crushing behavior of isolated components and their energy absorption trends. The effect of adding hollow thin-walled tubes as vertical struts is studied, expecting a great enhancement of the conventional design response. Surrogate-based optimization methodologies are also applied to this simulation, monitoring various crashworthiness biometrics and the specimen’s mass. Results show that on a coupon basis, the usage of inner reinforcements can modify the tube’s collapse patterns and increase its specific energy absorption values by up to 30 %, mainly caused by the interaction between the core and the confining structure. Moreover, reducing the core’s height has also shown improved responses, offsetting the triggering loads of each component and yielding peak force values 33 % lower. Topographic optimization of honeycomb cells has revealed that the highest specific energy absorption values for dynamic loads are not achieved with a regular cell but with a pseudo-rectangular one. The usage of foam as cell-filling has also proved superb, increasing energy absorption by another 28 % with minor hindering on the specimen’s mass. As for the validation of the full size aircraft drop-test simulation, numerical and graphical results closely match those of the experimental procedure. It was found that removing the auxiliary fuel tank from the original section increased occupant injury levels due to high structural deformation and low energy absorption by the main structures. In a later phase, hybrid energy absorbers are added to the fuselage section with an empty cargo area, and a new surrogate model is built with 600 full-scale drop test simulation. The surrogate is then single- and multi-objectively optimized, reducing acceleration peak values by 50 % and injury levels from severe to moderate at different occupant locations

    Design Optimization of Composite Deployable Bridge Systems Using Hybrid Meta-heuristic Methods for Rapid Post-disaster Mobility

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    Recent decades have witnessed an increase in the transportation infrastructure damage caused by natural disasters such as earthquakes, high winds, floods, as well as man-made disasters. Such damages result in a disruption to the transportation infrastructure network; hence, limit the post-disaster relief operations. This led to the exigency of developing and using effective deployable bridge systems for rapid post-disaster mobility while minimizing the weight to capacity ratio. Recent researches for assessments of mobile bridging requirements concluded that current deployable metallic bridge systems are prone to their service life, unable to meet the increase in vehicle design loads, and any trials for the structures’ strengthening will sacrifice the ease of mobility. Therefore, this research focuses on developing a lightweight deployable bridge system using composite laminates for lightweight bridging in the aftermath of natural disaster. The research investigates the structural design optimization for composite laminate deployable bridge systems, as well as the design, development and testing of composite sandwich core sections that act as the compression bearing element in a deployable bridge treadway structure. The thesis is organized into two parts. The first part includes a new improved particle swarm meta-heuristic approach capable of effectively optimizing deployable bridge systems. The developed approach is extended to modify the technique for discrete design of composite laminates and maximum strength design of composite sandwich core sections. The second part focuses on developing, experimentally testing and numerically investigating the performance of different sandwich core configurations that will be used as the compression bearing element in a deployable fibre-reinforced polymer (FRP) bridge girder. The first part investigated different optimization algorithms used for structural optimization. The uncertainty in the effectiveness of the available methods to handle complex structural models emphasized the need to develop an enhanced version of Particle Swarm Optimizer (PSO) without performing multiple operations using different techniques. The new technique implements a better emulation for the attraction and repulsion behavior of the swarm. The new algorithm is called Controlled Diversity Particle Swarm Optimizer (CD-PSO). The algorithm improved the performance of the classical PSO in terms of solution stability, quality, convergence rate and computational time. The CD-PSO is then hybridized with the Response Surface Methodology (RSM) to redirect the swarm search for probing feasible solutions in hyperspace using only the design parameters of strong influence on the objective function. This is triggered when the algorithm fails to obtain good solutions using CD-PSO. The performance of CD-PSO is tested on benchmark structures and compared to others in the literature. Consequently, both techniques, CD-, and hybrid CD-PSO are examined for the minimum weight design of large-scale deployable bridge structure. Furthermore, a discrete version of the algorithm is created to handle the discrete nature of the composite laminate sandwich core design. The second part focuses on achieving an effective composite deployable bridge system, this is realized through maximizing shear strength, compression strength, and stiffness designs of light-weight composite sandwich cores of the treadway bridge’s compression deck. Different composite sandwich cores are investigated and their progressive failure is numerically evaluated. The performance of the sandwich cores is experimentally tested in terms of flatwise compressive strength, edgewise compressive strength and shear strength capacities. Further, the cores’ compression strength and shear strength capacities are numerically simulated and the results are validated with the experimental work. Based on the numerical and experimental tests findings, the sandwich cores plate properties are quantified for future implementation in optimized scaled deployable bridge treadway

    Implementación web de una herramienta para el diseño de paneles rigidizados laminados de materiales compuestos

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    The use of composite materials has grown considerably during recent years, most prominently in the aerospace and automobile industries, as well as in the civil and naval industries. Composite materials display outstanding specific strength and stiffness properties, and a broad capacity for customization. Both qualities have contributed to their application in industries sensitive to the weight of materials such as steel or other alloys. However, composites’ behavior is more difficult to predict due to multiple failure modes. Therefore, complex models and powerful software are normally required to design components made of composite materials, both during the creation of the model and in the results post-processing, adding time to the design process. It would be helpful to automate part of this process in order to speed up the pre-design step. The aim of this project is the development of a tool that will ease the design of composite stiffened panels, reducing the time required to create the model while also allowing fast modifications of a given design in order to maximize the design efficiency. The tool will be able to generate the mesh and geometry from a set of predefined parameters that defines a given panel, and it will compute the critical buckling load of the first five modes, along with the visualization of the deformed panel to ensure quick post-processing. In order to render this tool accessible, it will be implemented in a web environment (CADEC) using advanced features such as object-oriented programming, user authentication and databases. After implementing such a tool in CADEC, it was used to predict the buckling load of several real cases of stiffened panels. Since the results obtained with CADEC generally coincided with the experimental results analyzed, the tool and its results can be safely validated.Ingeniería Industria

    Safety and Reliability - Safe Societies in a Changing World

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    The contributions cover a wide range of methodologies and application areas for safety and reliability that contribute to safe societies in a changing world. These methodologies and applications include: - foundations of risk and reliability assessment and management - mathematical methods in reliability and safety - risk assessment - risk management - system reliability - uncertainty analysis - digitalization and big data - prognostics and system health management - occupational safety - accident and incident modeling - maintenance modeling and applications - simulation for safety and reliability analysis - dynamic risk and barrier management - organizational factors and safety culture - human factors and human reliability - resilience engineering - structural reliability - natural hazards - security - economic analysis in risk managemen
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