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During the past decade, design under uncertainty has received significant attention and a wide range of applications from designing simple product components to designing complex and emerging engineered systems. Uncertainty is ubiquitous in engineering design. Therefore, it is crucial to discover an optimized design that satisfies the reliability requirements. A commonly used design optimization methodology for engineering systems comprises deterministic modeling and simulation-based design optimization. However, traditional deterministic design optimization (DDO) is not capable of considering design uncertainty. Taking uncertainties into account could be handled by using a technique called Reliability-based design optimization (RBDO). Reliability analysis, as the fundamental process in RBDO, evaluates the probability of failure as a probability of a function to reach a defined limit state. RBDO requires reliability analysis to estimate probabilistic design constraints iteratively, and it can be very time consuming because the reliability analysis requires huge numbers of input data. Hence, efficient reliability analysis is inevitable especially when computationally expensive simulation such as finite element runs is involved. The ultimate purpose for any reliability analysis problem is to increase the efficiency and accuracy of the calculations. There are still clear deficiencies in terms of efficiency and accuracy of the solution methods which is used depending on the nature of the problem (level of nonlinearity and size). The aim of this research is to propose efficient methods for reliability analysis in the design of energy harvesters and sensor network systems. We apply these techniques to high dimensional RBDO that possibly involves finite element analyses. Dimension reduction (DR) method is implemented in the first part of this dissertation to address design under uncertainty for complex engineering problems. Next, an adaptive improved response surface method (AIRSM) in conjunction with a new regression based model is proposed for solving reliability problems and compared with DR method in terms of efficiency and accuracy. Finally, a high dimensional complex reliability problem is solved using Kriging model and Genetic Algorithm (GA). This research aims to suggest practical and efficient reliability analysis methods to minimize the computational cost while maintaining accuracy to be integrated into the RBDO.
This book is a comprehensive overview of the recently developed methods for assessing and optimizing system reliability and safety. It consists of two main parts, for assessment and optimization methods, respectively. The former covers multi-state system modelling and reliability evaluation, Markov processes, Monte Carlo simulation and uncertainty treatments under poor knowledge. The reviewed methods range from piecewise-deterministic Markov process to belief functions. The latter covers mathematical programs, evolutionary algorithms, multi-objective optimization and optimization under uncertainty. The reviewed methods range from non-dominated sorting genetic algorithm to robust optimization. This book also includes the applications of the assessment and optimization method on real world cases, particularly for the reliability and safety of renewable energy systems. From this point of view, the book bridges the gap between theoretical development and engineering practice.
This book offers a comprehensive overview of recently developed methods for assessing and optimizing system reliability. It consists of two main parts, for treating assessment methods and optimization methods, respectively. The first part covers methods of multi-state system reliability modelling and evaluation, Markov processes, Monte Carlo simulation and uncertainty analysis. The methods considered range from piecewise-deterministic Markov processes to belief function analysis. The second part covers optimization methods of mathematical programming and evolutionary algorithms, and problems of multi-objective optimization and optimization under uncertainty. The methods of this part range from non-dominated sorting genetic algorithm to robust optimization. The book also includes the application of the assessment and optimization methods considered on real case studies, particularly with respect to the reliability assessment and optimization of renewable energy systems, and bridges the gap between theoretical method development and engineering practice.
Abstract : Uncertainty is inherent to real-world engineering systems, and reliability analysis aims at quantitatively measuring the probability that engineering systems successfully perform the intended functionalities under various sources of uncertainties. In this dissertation, heterogeneous uncertainties including input variation, data uncertainty, simulation model uncertainty, and time-dependent uncertainty have been taken into account in reliability analysis and reliability-based design optimization (RBDO). The input variation inherently exists in practical engineering system and can be characterized by statistical modeling methods. Data uncertainty occurs when surrogate models are constructed to replace the simulations or experiments based on a set of training data, while simulation model uncertainty is introduced when high-fidelity simulation models are built through idealizations and simplifications of real physical processes or systems. Time-dependent uncertainty is involved when considering system or component aging and deterioration. Ensuring a high level of system reliability is one of the critical targets for engineering design, and this dissertation studies effective reliability analysis and reliability-based design optimization (RBDO) techniques to address the challenges of heterogeneous uncertainties. First of all, a novel reliability analysis method is proposed to deal with input randomness and time-dependent uncertainty. An ensemble learning framework is designed by integrating the Long short-term memory (LSTM) and feedforward neural network. Time-series data is utilized to construct a surrogate model for capturing the time-dependent responses with respect to input variables and stochastic processes. Moreover, a RBDO framework with Kriging technique is presented to address the time-dependent uncertainty in design optimization. Limit state functions are transformed into time-independent domain by converting the stochastic processes and time parameter to random variables, and Kriging surrogate models are then built and enhanced by a design-driven adaptive sampling scheme to accurately identify potential instantaneous failure events. Secondly, an equivalent reliability index (ERI) method is proposed for handling both input variations and surrogate model uncertainty in RBDO. To account for the surrogate model uncertainty, a Gaussian mixture model is constructed based on Gaussian process model predictions. To propagate both input variations and surrogate model uncertainty into reliability analysis, the statistical moments of the GMM is utilized for calculating an equivalent reliability index. The sensitivity of ERI with respect to design variables is analytically derived to facilitate the surrogate model-based product design process, lead to reliable optimum solutions. Thirdly, different effective methods are developed to handle the simulation model uncertainty as well as the surrogate model uncertainty. An active resource allocation framework is proposed for accurate reliability analysis using both simulation and experimental data, where a two-phase updating strategy is developed for reducing the computational costs. The framework is further extended for RBDO problems, where multi-fidelity design algorithm is presented to ensure accurate optimum designs while minimizing the computational costs. To account for both the bias terms and unknown parameters in the simulation model, Bayesian inference method is adopted for building a validated surrogate model, and a Bayesian-based mixture modeling method is developed to ensure reliable system designs with the consideration of heterogeneous uncertainties.
This book presents original studies describing the latest research and developments in the area of reliability and systems engineering. It helps the reader identifying gaps in the current knowledge and presents fruitful areas for further research in the field. Among others, this book covers reliability measures, reliability assessment of multi-state systems, optimization of multi-state systems, continuous multi-state systems, new computational techniques applied to multi-state systems and probabilistic and non-probabilistic safety assessment.
The transformation of vibrations into electric energy through the use of piezoelectric devices is an exciting and rapidly developing area of research with a widening range of applications constantly materialising. With Piezoelectric Energy Harvesting, world-leading researchers provide a timely and comprehensive coverage of the electromechanical modelling and applications of piezoelectric energy harvesters. They present principal modelling approaches, synthesizing fundamental material related to mechanical, aerospace, civil, electrical and materials engineering disciplines for vibration-based energy harvesting using piezoelectric transduction. Piezoelectric Energy Harvesting provides the first comprehensive treatment of distributed-parameter electromechanical modelling for piezoelectric energy harvesting with extensive case studies including experimental validations, and is the first book to address modelling of various forms of excitation in piezoelectric energy harvesting, ranging from airflow excitation to moving loads, thus ensuring its relevance to engineers in fields as disparate as aerospace engineering and civil engineering. Coverage includes: Analytical and approximate analytical distributed-parameter electromechanical models with illustrative theoretical case studies as well as extensive experimental validations Several problems of piezoelectric energy harvesting ranging from simple harmonic excitation to random vibrations Details of introducing and modelling piezoelectric coupling for various problems Modelling and exploiting nonlinear dynamics for performance enhancement, supported with experimental verifications Applications ranging from moving load excitation of slender bridges to airflow excitation of aeroelastic sections A review of standard nonlinear energy harvesting circuits with modelling aspects.
This book originates from the NATO Advanced Study Institute on Synthesis and Analysis Methods for Safety and Reliability Studies held at Sogesta Conference Centre, Urbino, Italy, 3-14 July 1978. The Institute, co-directed by Prof. E.J. Henley and Dr. G. Volta, was attended by 67 persons from twelve countries. The focus of the Institute was on theoretical and applied aspects of reliability and risk analysis methodologies. The Institute was composed of lectures, workshops and gu~ded discussions. From the large quantity of written material that was used and produced during the Institute, a number of papers introducing the most relevant research results and trends in the field have been selected. The papers have been edited, partly rewritten and rearranged in order to obtain in the end an integrat ed exposition of methods and techniques for reliability analysis and computation of complex systems. The book is divided into four sections which correspond to fairly homogeneous areas from a methodological point of view. Each section is preceded by an introduction prepared by the Editors which aims at helping the readers to put in perspective and appre ciate the contribution of each paper to the subject of the section.
This unique treatment systematically interprets a spectrum of importance measures to provide a comprehensive overview of their applications in the areas of reliability, network, risk, mathematical programming, and optimization. Investigating the precise relationships among various importance measures, it describes how they are modelled and combined with other design tools to allow users to solve readily many real-world, large-scale decision-making problems. Presenting the state-of-the-art in network analysis, multistate systems, and application in modern systems, this book offers a clear and complete introduction to the topic. Through describing the reliability importance and the fundamentals, it covers advanced topics such as signature of coherent systems, multi-linear functions, and new interpretation of the mathematical programming problems. Key highlights: Generalizes the concepts behind importance measures (such as sensitivity and perturbation analysis, uncertainty analysis, mathematical programming, network designs), enabling readers to address large-scale problems within various fields effectively Covers a large range of importance measures, including those in binary coherent systems, binary monotone systems, multistate systems, continuum systems, repairable systems, as well as importance measures of pairs and groups of components Demonstrates numerical and practical applications of importance measures and the related methodologies, including risk analysis in nuclear power plants, cloud computing, software reliability and more Provides thorough comparisons, examples and case studies on relations of different importance measures, with conclusive results based on the authors’ own research Describes reliability design such as redundancy allocation, system upgrading and component assignment. This book will benefit researchers and practitioners interested in systems design, reliability, risk and optimization, statistics, maintenance, prognostics and operations. Readers can develop feasible approaches to solving various open-ended problems in their research and practical work. Software developers, IT analysts and reliability and safety engineers in nuclear, telecommunications, offshore and civil industries will also find the book useful.
The objective of this research is to develop new stochastic methods based on most probable points (MPPs) for general reliability analysis and reliability-based design optimization of complex engineering systems. The current efforts involves: (1) univariate method with simulation for reliability analysis; (2) univariate method with numerical integration for reliability analysis; (3) multi-point univariate for reliability analysis involving multiple MPPs; and (4) univariate method for design sensitivity analysis and reliability-based design optimization.
"Time-dependent uncertainties, such as time-variant stochastic loadings and random deterioration of material properties, are inherent in engineering applications. Not considering these uncertainties in the design process may result in catastrophic failures after the designed products are put into operation. Although significant progress has been made in probabilistic engineering design, quantifying and mitigating the effects of time-dependent uncertainty is still challenging. This dissertation aims to help build high reliability into products under time-dependent uncertainty by addressing two research issues. The first one is to efficiently and accurately predict the time-dependent reliability while the second one is to effectively design the time-dependent reliability into the product. For the first research issue, new time-dependent reliability analysis methodologies are developed, including the joint upcrossing rate method, the surrogate model method, the global efficient optimization, and the random field approach. For the second research issue, a time-dependent sequential optimization and reliability analysis method is proposed. The developed approaches are applied to the reliability analysis of designing a hydrokinetic turbine blade subjected to stochastic river flow loading. Extension of the proposed methods to the reliability analysis with mixture of random and interval variables is also a contribution of this dissertation. The engineering examples tested in in this work demonstrate that the proposed time-dependent reliability methods can improve the efficiency and accuracy more than 100% and that high reliability can be successfully built into products with the proposed method. The research results can benefit a wide range of areas, such as life cycle cost optimization and decision making"--Abstract, page iv.