Uncertainties in engineering design exist due to the random nature of loads and materials, lack of knowledge, and imperfect modelling of design parameters. Conventional design methods based on deterministic procedures do not always yield designs having consistent safety. In recent years considerable research has been conducted in the use of probability theory for modelling uncertainties in engineering designs and several probabilistic design formats have been developed. Probability based design methods provide a unified procedure applicable to all construction materials, all loads, and all types of uncertainties. Code committees are currently working on the development of the new design codes for various construction materials such as steel, concrete, and wood based on probabilistic concepts. The objective of this study is to study a probability based design format for wood members. Reliability analysis of wood structural elements such as beams, columns, and beam-columns is conducted, and the risk level is measured by the reliability or safety index, β. Wood members subjected to dead plus live load and dead plus snow load combinations are considered. After conducting a reliability analysis of current designs, a target reliability index is selected. The reliability index is then used in conjunction with the predetermined load factors and load combinations to determine resistance factors. Finally, a design format is proposed for Load and Resistance Factor Design for wood structures. / M.S.

Reliability prediction of components operating in complex systems has historically been conducted in a statistically isolated manner. Current physics-based, i.e. mechanistic, component reliability approaches focus more on component-specific attributes and mathematical algorithms and not enough on the influence of the system. The result is that significant error can be introduced into the component reliability assessment process. The objective of this study is the development of a framework that infuses the influence of the system into the process of conducting mechanistic-based component reliability assessments. The formulated framework consists of six primary steps. The first three steps, identification, decomposition, and synthesis, are qualitative in nature and employ system reliability and safety engineering principles for an appropriate starting point for the component reliability assessment. The most unique steps of the framework are the steps used to quantify the system-driven local parameter space and a subsequent step using this information to guide the reduction of the component parameter space. The local statistical space quantification step is accomplished using two newly developed multivariate probability tools: Multi-Response First Order Second Moment and Taylor-Based Inverse Transformation. Where existing joint probability models require preliminary statistical information of the responses, these models combine statistical information of the input parameters with an efficient sampling of the response analyses to produce the multi-response joint probability distribution. Parameter space reduction is accomplished using Approximate Canonical Correlation Analysis (ACCA) employed as a multi-response screening technique. The novelty of this approach is that each individual local parameter and even subsets of parameters representing entire contributing analyses can now be rank ordered with respect to their contribution to not just one response, but the entire vector of component responses simultaneously. The final step of the framework is the actual probabilistic assessment of the component. Variations of this final step are given to allow for the utilization of existing probabilistic methods such as response surface Monte Carlo and Fast Probability Integration. The framework developed in this study is implemented to conduct the finite-element based reliability prediction of a gas turbine airfoil involving several failure responses. The framework, as implemented resulted in a considerable improvement to the accuracy of the part reliability assessment and an increased statistical understanding of the component failure behavior.

University of Macau / Faculty of Science and Technology / Department of Civil and Environmental Engineering

Not available / text

Orientador: Gilberto De Martino Jannuzzi / Dissertação (mestrado) - Universidade Estadual de Campinas, Faculdade de Engenharia Mecanica / Made available in DSpace on 2018-08-09T12:30:01Z (GMT). No. of bitstreams: 1 Santos_HeriveltoTiagoMarcondesdos_M.pdf: 831089 bytes, checksum: 0c9db7d62f8e506686cc6b9059f1eb4f (MD5) Previous issue date: 2006 / Resumo: Este trabalho apresenta uma avaliação dos investimentos feitos pelas empresas brasileiras de eletricidade em programas de P&D e em eficiência energética, segundo a imposição da lei federal 9.991/00. Conforme esta lei, as empresas de eletricidade são obrigadas a investirem nesses programas, no mínimo, 1% de suas receitas operacionais líquidas anuais. Com isso, essas empresas foram avaliadas segundo as suas participações no mercado de eletricidade sobre os investimentos em tipos de projetos (eficiência energética) ou tópicos tecnológicos (P&D). A avaliação foi feita usando um índice de concentração de mercado e, nesse caso, utilizou-se o índice Herfindahl ¿ Hirschman, como também por meio de estatísticas descritivas dos programas desenvolvidos. Como parte das conclusões observou-se que as empresas de eletricidade apresentaram alguns padrões de investimentos os quais podem ser utilizados como referência aos futuros programas de P&D e de eficiência energética / Abstract: This work presents one assessment of investments to carry out on research and technological development (R&D) and energy efficiency programs according to impose of federal act 9.991/00. It is to determine that the electricity utilities to invest 1% of annual utilities revenues on those programs. Thus electricity utilities were evaluated according to its market quota on the investments in project type (energy efficiency) or technological topic (R&D). The assessment was realized through of concentration market index and it was utilized the Herfindahl ¿ Hirschman index and the Descriptives Statistics of programs. As part of conclusions was to verify that electricity utilities is to present some investment standard that to be reference to the futures programs in R&D and energy efficiency / Mestrado / Mestre em Planejamento de Sistemas Energéticos

Orientador: Robson Pederiva / Tese (doutorado) - Universidade Estadual de Campinas, Faculdade de Engenharia Mecanica / Made available in DSpace on 2018-08-04T03:26:01Z (GMT). No. of bitstreams: 1 Bezerra_RobertodeAraujo_D.pdf: 3842898 bytes, checksum: 23a6bdd65c87be47fe4cfa856795bab0 (MD5) Previous issue date: 2004 / Resumo: Neste trabalho, é feito um estudo comparativo entre diversas técnicas de detecção de falhas em rolamentos por análise de vibração. Inicialmente, as técnicas foram aplicadas a modelos matemáticos de falhas nas pistas interna, externa e nas esferas dos rolamentos; sendo, em seguida, feito um estudo comparativo entre as técnicas. As técnicas foram aplicadas também a rolamentos com falhas induzidas nas pistas e esfera com diferentes tamanhos de falhas e submetidos a diferentes velocidades, para uma melhor compreensão das técnicas. Finalmente, as técnicas foram usadas para o monitoramento da evolução das falhas em um conjunto de doze rolamentos que foram submetidos a condições próximas as de trabalho, o que possibilitou um estudo mais detalhado do processo de evolução dessas falhas. Os resultados obtidos mostraram que, de todas as técnicas utilizadas, o envelope com filtro adaptativo foi a mais eficiente, sendo capaz de detectar pequenos amassamentos e o surgimento de falhas na gaiola. O estudo em condições próximas a realidade possibilitou uma melhor compreensão do processo de evolução das falhas em rolamentos / Abstract: In this thesis, it is made a comparative study among several vibration analysis techniques of fault detection. Initially, the techniques were applied to inner and outer race and rolling element mathematical models of faults, and so, it was possible to compare the techniques. It was used the same techniques on the races and rolling elements with different size of induced faults, submitting to different speeds. With those studies it was possible to understand how to use the technique in a better way. Finally, we monitored a group of twelve bearings to analyse the evolution of faults, close to real conditions. The results showed that the most efficient techniques is the envelope with adaptive filter, it detects small dentings and the begging of cage failure. It was possible to get a better understanding of the failure evolution process in bearings, studying it in close conditions to the reality / Doutorado / Mecanica dos Sólidos e Projeto Mecanico / Doutor em Engenharia Mecânica

An efficient reliability technique has been developed based on Response Surface Methodology (RSM) in conjunction with the First Order Second Moment (FOSM) reliability method. The technique is applied when the limit state function cannot be obtained explicitly in terms of the design variables, i.e., when the analysis is performed using numerical techniques such as finite elements. The technique has proven to be efficient because it can handle problems with large numbers of design variables and correlated as well as nonnormal random variables. When compared with analytical results, the method has shown excellent agreement. The technique contains a sensitivity analysis scheme which can be used to reduce the computation time resulting in nearly the same accuracy. This technique allows the extension of most finite element codes to account for probabilistic analysis, where statistical variations can be added to the design variables. An explicit solution for rocket motors consisting of propellant and steel case under environmental temperature variations is compared to the RSM technique. The method is then used for the analysis of rocket motors subjected to mechanical loads for which the stress analysis is performed using the finite element method. The technique is also applied to study the reliability of a laminated composite plate with geometric nonlinearity subjected to static and time dependent loadings. Different failure modes were considered as well as different meshes. Results have shown that when the relative size of the element is introduced into the probabilistic model, the same reliability value is obtained regardless of the number of elements in the mesh. This is good because it allows the technique to be used for problems where the failure region is unknown. / Ph. D.

Roof and rib failures in underground coal mines are one of the major problems facing the industry today. In addition to safety considerations, the resulting economic impact of such failures is staggering. Uncovering and replacing buried and damaged equipment and clearing entries can account for a large expenditure in lost man-hours and machinery. Yet, because of the complex nature of their formation, geological variability, and structural characteristics, coal mine roof strata are one of the least controllable of all mine design parameters. This is especially true along the leading (southeastern) edge of the Appalachian coalfields where considerable faulting and movement have contributed to hazardous coal mining roof conditions. For this research, a detailed study of several mines, in the southern Appalachian coalfields, was undertaken to determine the most prominent geomechanical factors affecting roof stability and to evaluate their influence in promoting unstable ground conditions. In order to accomplish this task, the major geological and geomechanical features found to be detrimental to the coal mine roof within the Appalachian basin were identified and mapped in four Virginia mines. Statistical processing by chi-square and linear regression analysis as well as analytical analysis by the finite element method were used to determine the influence of geology, mine-layout, and support methods on roof stability. It was found that some easily determined parameters can be successfully used to predict potentially unstable areas. A simplified roof classification system was developed based on the geomechanical parameters, which can be used to assess the stability of a particular roof type. A Roof Rating Index was also devised capable of expressing the probability of failure under a given set of geomechanical conditions. / Ph. D.

The objective of the proposed research is to develop statistical algorithms for controlling failure trends through targeted maintenance of at-risk components. The at-risk components are identified via chronological history and diagnostic data, if available. Utility systems include many thousands (possibly millions) of components with many of them having already exceeded their design lives. Unfortunately, neither the budget nor manufacturing resources exist to allow for the immediate replacement of all these components. On the other hand, the utility cannot tolerate a decrease in reliability or the associated increased costs. To combat this problem, an overall maintenance model has been developed that utilizes all the available historical information (failure rates and population sizes) and diagnostic tools (real-time conditions of each component) to generate a maintenance plan. This plan must be capable of delivering the needed reliability improvements while remaining economical. It consists of three facets each of which addresses one of the critical asset management issues: * Failure Prediction Facet - Statistical algorithm for predicting future failure trends and estimating required numbers of corrective actions to alter these failure trends to desirable levels. Provides planning guidance and expected future performance of the system. * Diagnostic Facet - Development of diagnostic data and techniques for assessing the accuracy and validity of that data. Provides the true effectiveness of the different diagnostic tools that are available. * Economics Facet - Stochastic model of economic benefits that may be obtained from diagnostic directed maintenance programs. Provides the cost model that may be used for budgeting purposes. These facets function together to generate a diagnostic directed maintenance plan whose goal is to provide the best available guidance for maximizing the gains in reliability for the budgetary limits utility engineers must operate within.

The main objective of the topology optimization is to fulfill the objective function with the minimum amount of material. This reduces the overall cost of the structure and at the same time reduces the assembly, manufacturing and maintenance costs because of the reduced number of parts in the final structure. The concept of reliability analysis can be incorporated into the deterministic topology optimization method; this incorporated scheme is referred to as Reliability-based Topology Optimization (RBTO). In RBTO, the statistical nature of constraints and design problems are defined in the objective function and probabilistic constraint. The probabilistic constraint can specify the required reliability level of the system. In practical applications, however, finding global optimum in the presence of uncertainty is a difficult and computationally intensive task, since for every possible design a full stochastic analysis has to be performed for estimating various statistical parameters. Efficient methodologies are therefore required for the solution of the stochastic part and the optimization part of the design process. This research will explore a reliability-based synthesis method which estimates all the statistical parameters and finds the optimum while being less computationally intensive. The efficiency of the proposed method is achieved with the combination of topology optimization and stochastic approximation which utilizes a sampling technique such as Latin Hypercube Sampling (LHS) and surrogate modeling techniques such as Local Regression and Classification using Artificial Neural Networks (ANN). Local regression is comparatively less computationally intensive and produces good results in case of low probability of failures whereas Classification is particularly useful in cases where the reliability of failure has to be estimated with disjoint failure domains. Because classification using ANN is comparatively more computationally demanding than Local regression, classification is only used when local regression fails to give the desired level of goodness of fit. Nevertheless, classification is an indispensible tool in estimating the probability of failure when the failure domain is discontinuous. Representative examples will be demonstrated where the method is used to design customized meso-scale truss structures and a macro-scale hydrogen storage tank. The final deliverable from this research will be a less computationally intensive and robust RBTO procedure that can be used for design of truss structures with variable design parameters and force and boundary conditions.