Literatura académica sobre el tema "Complex mdof structural systems"

Nonlinear response history analysis is accepted as the most accurate analytical tool for seismic response determination. However, accurate estimation of displacement responses using conceptually simple, approximate analysis procedures is preferable, since there are shortcomings in the application of nonlinear response history analysis resulting from its complexity. An equivalent linearization procedure, which utilizes the familiar response spectrum analysis as the analysis tool and benefits from the capacity principles, is developed in this thesis study as an approximate method for predicting the inelastic seismic displacement response of MDOF systems under earthquake excitations. The procedure mainly consists of the construction of an equivalent linear system by reducing the stiffness of structural members which are expected to respond in the inelastic range. Different from similar studies in literature, equivalent damping is not explicitly employed in this study. Instead, predetermined spectral displacement demands are utilized in each mode of the equivalent linear system for the determination of global displacement demands. Response predictions of the equivalent linearization procedure are comparatively evaluated by using the benchmark nonlinear response history analysis results and other approximate methods including conventional pushover analysis and modal pushover analysis (MPA). It is observed that the proposed procedure results in similar accuracy with approximate methods which employ nonlinear analysis. Considering the conceptual simplicity of the procedure and the conventional analysis tools used in its application, presented equivalent linearization procedure can be suggested as a practically applicable method for the prediction of inelastic seismic displacement response parameters with sufficient accuracy.

Thesis (Ph. D.)--University of Akron, Dept. of Chemistry, 2006.<br>"December, 2006." Title from electronic dissertation title page (viewed 04/24/2008). Advisor, Chrys Wesdemiotis; Committee members, Matthew P. Espe, Jun Hu, Wiley J. Youngs, Frank W. Harris; Department Chair, Kim C. Calvo; Dean of the College, Ronald F. Levant; Dean of the Graduate School, George R. Newkome. Includes bibliographical references.

With the rapid development in today&rsquo<br>s technology, reliability and performance requirements on components of various mechanical systems, which tend to be much lighter and work under much more severe working conditions, dramatically increased. In general, analysis techniques based on simplified model of structural components with linearity assumption may provide time saving for solutions with reasonable accuracy. However, since most engineering structures are often very complex and intrinsically nonlinear, in some cases they may behave in a different manner which cannot be fully described by linear mathematical models, or linear treatments may not be applicable at all. In fact, some studies revealed that deviations in the modal properties of dynamic structures gathered from measured data are due to nonlinearities in the structure. Hence, in problems where accuracy is the primary concern, taking the nonlinear effects into account becomes inevitable. In this thesis, it is aimed to analyze the harmonic response characteristics of multi degree of freedom nonlinear structures having different type of nonlinearities. The amplitude dependencies of nonlinearities are modelled by using describing function method. To increase the accuracy of the results, effect of the higher order harmonic terms will be considered by using multi harmonic describing function theory. Mathematical formulations are embedded in a computer program developed in MATLAB&reg<br>with graphical user interface. The program gets the system matricies from the file which is obtained by using substructuring analysis in ANSYS&reg<br>, and nonlinearities in the system can easily be defined through the graphical user interface of the MATLAB&reg<br>program.

This thesis aims at developing new methodologies for the reliability analysis of structural systems with applications to offshore and aeronautical fields. In general, sructures of practical interest are complex redundant systems, in which more than one element is required to fail in order to have catastrophic failure. Moreover, ramdomness inherently exists in both material properties and external loads. As a result, complex structural systems are typically characterised by a huge number of possible failure sequences, of which only some are most likely to occour. Therefore, for an efficient risk analysis, only the dominant failure modes need to be considered, so as to minimise the number of failure paths as well as the computational costs associated to their enumeration and evaluation. However, although several techniques have been developed for the identification of the critical failure sequences, these methods are still either time-demanding or prone to miss potential failure modes. These challenges motivated the first part of the thesis, in which the merits of a risk assessment framework recently developed for truss and frame structures are here investigated in view of its extensive application to the offshore field. To this end, the case study of a jacket-type platform under an extreme sea state is considered. First, the dominant failure modes of the structure are rapidly identified by a multi-point parallel search employing a genetic algorithm. Then, a multi-scale system reliability analysis is performed, in which the statistical dependence among both structural elements and failure modes is fully considered through simple matrix operations. Finally, the accuracy and the efficiency of the proposed approach are successfully validated against crude Monte Carlo simulation. In the second part of the thesis, system reliability theory is applied to the uncertainty quantification of the longitudinal tensile strength of UniDirectional (UD) composites, a structural component very common in aircraft structures. Predictive models for size effects in this class of materials are paramount for scaling small-coupon experimental results to the design of large composite structures. In this respect, a Monte Carlo progressive failure analysis is proposed to calculate the strength distributions of hierarchical fibre bundles, which are formed by grouping a predefined number of smaller-order bundles into a larger-order one. The present approach is firstly validated against a recent analytical model to be later applied to more complex load-sharing configurations. The resulting distributions are finally used to analyse the damage accumulation process and the formation of clusters of broken fibres during progressive failure.<br>Lo scopo principale di questa tesi è lo sviluppo di nuove metodologie per determinare l’affidabilità dei sistemi strutturali con applicazioni sia in campo offshore che aeronautico. In generale, strutture di interesse pratico sono caratterizzate da un elevato grado di ridondanza, per cui il collasso globale richiede la rottura simulatanea e/o progressiva di più elementi. Inoltre, i sistemi fisici sono influenzati da diverse fonti di incertezza, quali le prorietà dei materiali e le condizioni ambientali e operative. Pertanto, il collasso strutturale può avvenire con diverse modalità (modi di guasto), di cui solo alcune possiedono una probabilità di accadimento significativa (modi di guasto dominanti). Per una valutazione efficiente del rischio risulta dunque indispensabile limitare l’analisi ai soli modi dominanti, così da ridurre il costo computazionale associato alle fasi di identificazione e di valutazione dei modi stessi. Tuttavia, nonostante in letteratura vi siano numerose soluzioni per l’analisi del rischio, tali metodi richiedono ancora tempi di calcolo notevoli e sono inclini a tralasciare potenziali modi di guasto. Queste motivazioni conducono alla prima parte delle tesi, in cui si ripropone un metodo recentemente sviluppato per l’analisi del rischio di strutture discrete (reticolari e telai) in previsione di una sua applicazione al campo offshore. A tale scopo si considera il caso di studio di una piattaforma di tipo jacket in condizioni di mare estremo. Dapprima, i modi di guasto dominanti vengono rapidamente identificati per mezzo di un algoritmo genetico. In seguito, l’affidabilità del sistema viene calcolata mediante un approccio multi-scala che fa uso di semplici operazioni matriciali, in cui la dipendenza statistica viene considerata sia tra le componenti strutturali che tra i modi di guasto dominanti. Infine, l’accuratezza e l’efficienza del metodo vengono testate con successo tramite comparazione con Monte Carlo. Nella seconda parte della tesi, la teoria dell’affidabilità dei sistemi viene applicata per la quantificazione dell’incertezza nella resistenza a trazione di compositi UniDirezionali (UD), problema di notevole interesse per l’ambito aeronautico e non solo. Infatti, il comportamento aletorio di questi materiali è fortemente influenzato da effetti di scala, che limitano la progettazione di strutture in composito di grandi dimensioni sulla base dei dati sperimentali ricavati da provini. In quest’ottica, si propone di modellare fasci di fibre secondo una legge di scala gerarchica, ossia raggruppando un numero prestabilito di fasci più piccoli in un fascio di ordine superiore. La distribuzione di resistenza di tali fasci viene quindi simulata attraverso un’analisi di collasso progressivo. Questo approccio, dapprima validato rispetto ad un modello analitico recentemente sviluppato per disposizioni semplici di fasci, viene poi esteso a configurazioni più realistiche. I risultati così ottenuti sono infine processati per l’analisi statistica del danno.

Complex networks is a vibrant research field and has received much attention over the last decade. Central to this area is the question of how networks around us are constructed. The essential notion of network research is that these systems are assembled in a decentralised way, thus no central agent is planning the network beforehand. Despite this lack of central coordination, many networks present intriguing universalities, such as broad degree distributions, in the form of power-laws. The subject of study in this thesis is a class of networks that are constructed by a node intrinsic variable, called fitness. The way these networks grow could be called a rich-get-richer mechanism. The fitter a node is, the more likely it is to acquire new connections inside the network. Several aspects that are directly connected to these networks are explored in this thesis. In the first part, the properties of growing networks that are driven by fitness are investigated and it is shown that the introduction of growth leads to a topological structure that is different from its static counterpart. In the subsequent chapter, percolation on fitness driven networks is studied. The results give insights into possible mechanisms that can stabilise systems. Furthermore, the theory can be used to identify vulnerable structures around us. In the following chapter, the world trade network is discussed. This numerical investigation highlights possible improvements to the methodology to make statistical analysis more robust. That chapter is followed by an analysis of time-varying networks. Time-varying networks represent an interesting construct that allows a formulation of stochastic processes on the same time-scale as the evolution of the network itself. This possibility is highly relevant to the investigation of epidemics, for instance. In the last chapter, a study of a system of clusters and their self-organised formation is presented.

In educational and organisational settings it has become common practice to use computer-based complex problems that represent dynamic systems for assessment and training purposes. In the interpretation of performance scores and the design of training programs, it is often assumed that the capacity to effectively control the outcomes of a dynamic system depends on the acquisition of structural knowledge. Control performance scores are generally interpreted as evidence of individual differences in the capacity to acquire and utilise structural knowledge and training programs typically try to improve learners‘ mental models of the system of interest. However, a causal relationship between the acquisition of structural knowledge and successful system control has not been established, and some findings suggest that it may be possible to control dynamic systems in the absence of structural knowledge. Therefore, the goals of this project were to determine the conditions that are required to learn how to control dynamic systems and the psychological processes that separate successful from less successful problem solvers in the performance of this task. The main emphasis of this investigation was to clarify the role of structural knowledge in the control of dynamic systems and to identify sources of individual differences in problem solvers‘ capacity to acquire such knowledge and apply it in a goal-orientated application. In a series of studies, a combined experimental and differential approach was adopted to address these goals. This consisted of the experimental manipulation of the task and structural characteristics of complex problems combined with the use of process indicators and external psychometric tests. Study 1 examined whether problem solvers need to directly interact with a dynamic system in order to acquire structural knowledge that is useful for system control. Study 2 examined whether increments in structural knowledge lead to improvements in control performance and whether dynamic systems can be successfully controlled without structural knowledge. Study 3 examined whether the relationship between structural knowledge and control performance is moderated by system complexity. Each of these studies also investigated the role of fluid intelligence in the acquisition and application of knowledge. Additional methodological contributions include the application of Cognitive Load Theory to the design of the instructions used to manipulate structural knowledge, the use of randomly generated control performance scores to evaluate the success of performance and the development of a theoretically driven operationalisation of system complexity. Across the studies, it was found that structural knowledge was a necessary condition of better than random performance and that there was a causal relationship between structural knowledge and control performance. However, the likelihood that structural knowledge would be acquired and utilised was found to be dependent on the complexity of the system. Small increments in system complexity resulted in floor effects on performance. Fluid intelligence was found to play a crucial role in the acquisition and subsequent application of knowledge. Overall, the results indicate that the complexity of the system determines the amount of knowledge that is acquired by the problem solver, which in turn, combined with their intelligence, determines the quality of their control performance.