Distortion (undesired dimension and shape changes) is one of the most important problems of through hardened steel components. During quenching, anisotropic dimensional changes are inevitable due to classical plasticity and transformation induced plasticity. Moreover / various distortion potential carriers are brought into material during production chain. This study consists of analytical, numerical and experimental investigations of quench distortion. In numerical and analytical part, sensitivity analysis of the quenching model, and dimensional analysis of distortion were conducted by utilizing experimentally verified simulations. In sensitivity analysis, effect of uncertainties in input data on simulation results were determined, whereas / in dimensional analysis, the influence of various dimensionless numbers that govern quench distortion were investigated. Throughout the study, gas-nozzle-field quenching of SAE52100 long shafts were simulated. Simulations were performed by commercial finite element analysis software, SYSWELD&reg / . Conceptual results indicate that the most important material properties and dimensionless numbers are the ones that govern volume change. Moreover, those that determine plasticity of austenite significantly affect isotropy of the dimensional changes. When unimportant dimensionless numbers are eliminated, there remain 14 dimensionless combinations that govern the problem. In experimental part of the study / effect of microstructure on distortion behavior of SAE52100 long cylinders with various diameters was investigated. In addition to gas-nozzle-field quenching, salt bath and high speed quenching experiments were performed. In regards to experimental findings, there is a correlation between distortions of long cylinders and machining position with respect to billet.

An ecosystem always involves some implicit relations between habitat environment and inhabitants, whose reciprocal links can not be identified easily. Three sets of ecological monitoring data were analyzed in this study, including coral reef, algae (Thalassia hemprichii Aschers) in Kenting National Park, and Formosan landlocked salmon (Oncorhynchus masou formosanus) in the basin of Chichiawan Stream. Two data-oriented analysis techniques, which are Habitat Evaluation Procedure (HEP) and Group Method of Data Handling (GMDH), were applied to retrieve the embedded patterns from these data sets. Eventually, for each data set, a forecasting model based on the technique of combined forecasting were developed, which is to integrate the results from HEP and GMDH, for improving the overall modeling precision. The results of this study show that the data-oriented analyses, such as HEP and GMDH, are useful for finding valid information from the ecological data. Furthermore, the combined forecasting technique can really improve the performance of model prediction even for the ecological research. In order to acquire the most important habitat environmental factors affecting the inhabitants, this study also performed sensitivity analysis of the models. The contributions of this study are to identify effective knowledge for future ecological research and to provide reasonable suggestions for formulating conservation strategy.

<p>This thesis concerns optimization of structures considering various uncertainties. The overall objective is to find methods to create solutions that are optimal both in the sense of handling the typical load case and minimising the variability of the response, i.e. robust optimal designs.</p><p>Traditionally optimized structures may show a tendency of being sensitive to small perturbations in the design or loading conditions, which of course are inevitable. To create robust designs, it is necessary to account for all conceivable variations (or at least the influencing ones) in the design process.</p><p>The thesis is divided in two parts. The first part serves as a theoretical background to the second part, the two appended articles. This first part includes the concept of robust design, basic statistics, optimization theory and meta modelling.</p><p>The first appended paper is an application of existing methods on a large industrial example problem. A sensitivity analysis is performed on a Scania truck cab subjected to impact loading in order to identify the most influencing variables on the crash responses.</p><p>The second paper presents a new method that may be used in robust optimizations, that is, optimizations that account for variations and uncertainties. The method is demonstrated on both an analytical example and a Finite Element example of an aluminium extrusion subjected to axial crushing.</p> / ROBDES

For any given reason, every year many countries spend a lot of money purchasing at least one weapon. Due to the secret character of the military, the decision process for specific weapon procurement is shrouded. Moreover, there are several funds loss cases due to mistakes in weapon contractions. Weapon procurement requires very large amounts of money which comes from tax payers. Therefore, an effort to reduce a possible monetary loss is needed. A decision process based on an analytic model can present a better chance to decision makers for better weapon decisions. In general, weapon procurement decision is a multi criteria environment. Decision making in such environments is defined as Multi-Criteria Decision Making (MCDM). MCDM is broadly classified into two areas: Multi-Attribute Decision Making (MADM) and Multi-Objective Decision Making (MODM). MADM methods are used for selecting an alternative from a small explicit list of alternatives. MODM methods are used for designing problems involving an infinite number of alternatives implicitly defined by mathematical constraints. This research is intended to be used by the South Korean Navy when there is a need to select one weapon type among several candidate types. Therefore, MADM methods are used in this research.Many researches for developing an analytical model for better decision-making have been done. However, there is no research for a generalized weapon procurement decision model that is easy to implement. For this reason, whenever there is a need for weapon procurement decision, the Navy has to spend a lot of effort in determining the best weapon. These efforts can be reduced with a generalized model that is proposed in this research for naval weapon procurement. MADM methods determine alternatives ranking orders and the highest ranked alternative is the best one. Various MADM methods are used in computing the alternatives ranking scores.

Most modern systems are equipped with very complex, expensive, and high technology components whose maintenance costs have become an increasingly large portion of the total operating cost of these systems. Thus, the efficacy of the maintenance policy for these and related systems has become a major concern to both manufacturing and design engineers. Different kinds of maintenance strategies have been proposed to solve the problem. While some of these have proven effective, there is yet no definitive approach that has been found that support the maintainability requirements of transient systems or systems that exhibit transient behavior. Transient behavior is the notion of non-steady state operation, which is the characteristic of system operation during its useful life. For designing convenience most of the maintenance strategies have assumed negligible maintenance or repair time which is not practical.In this research an opportunistic maintenance (OM) approach is implemented on a multi-unit system that exhibits transient behavior. Under OM policy, if a maintenance event has been scheduled for certain components and in the process of implementing the scheduled maintenance of these targeted components, the maintenance of other components whose maintenance times are in close proximity is also implemented at the same time. As a result, the time and cost of marshalling and staging maintenance resources are reduced. As part of the system effectiveness measure, the instantaneous system availability based on the transient nature of the system, is estimated using the renewal theory approach.

The Adequate Yearly Progress (AYP) Model has been used to make many high-stakes decisions concerning schools, though it does not provide a complete assessment of student academic achievement and school effectiveness. To provide a clearer perspective, many states have implemented various Growth and Value Added Models, in addition to AYP. The purpose of this study was to examine two Value Added Model specifications, the Gain Score Model and the Layered Effects Model, to understand similarities and differences in school effect results. Specifically, this study correlated value added school effect estimates, which were derived from two model specifications and two outcome measures (mathematics and reading test scores). Existing data were obtained from a moderately large and rural school district in Florida. The outcome measures of 7,899 unique students were examined using the Gain Score Model and the Layered Effects Model to estimate school effects. Those school effect estimates were then used to calculate and examine the relationship between school rankings. Overall, the findings in this study indicated that the school effect estimates and school rankings were more sensitive to outcome measures than they were to model specifications. The mathematics and reading correlations from the Gain Score Model for school effects and school rankings were low (indicating high sensitivity), when advancing from Grades 4 to 5, and were moderate in other grades. The mathematics and reading correlations from the Layered Effects Model were low at Grade 5 for school effects and school rankings, as were the correlations at Grade 7 for the school rankings. In the other grades, correlations were moderate to high (indicating lower sensitivity). Correlations between the Gain Score Model and the Layered Effects Model from mathematics were high in each grade for both school effects and school rankings. Reading correlations were also high for each of the grades. These results were similar to the findings of previous school effects research and added to the limited body of literature. Depending upon the outcome measure used, school effects and rankings can vary significantly when using Value Added Models. These models have become a popular component in educational accountability systems, yet there is no one perfect model. If used, these models should be used cautiously, in addition to other accountability approaches.

The main objective of this thesis is to understand the mechanics of a grand piano soundboard and to investigate the influence of several parameters on its modal and vibrational behaviors. Various analysis techniques are made possible by a development of a fully parameterized Finite Element model of the soundboard which allows the user to modify most geometric and material parameters. In addition, two crucial features, namely crowning and downbearing, are included in the model and their individual and combined effects are observed. This study also accounts for the influence of geometric nonlinearities due to downbearing. The piano soundboard includes a large number of unknown factors stemming from its construction process and the material properties of the wood. This thesis uses sensitivity techniques to investigate the influence of various factors on the variability of the modal outputs (i.e., natural frequencies and mode shapes). The different aspects of the model are described in details and various sensitivity methods are tested in this context.

Malaria is an infectious disease, transmitted between humans through mosquito bites, that kills about two million people a year. We derive and analyze a mathematical model to better understand the transmission and spread of this disease. Our main goal is to use this model to compare intervention strategies for malaria control for two representative areas of high and low transmission. We model malaria using ordinary differential equations. We analyze the existence and stability of disease-free and endemic (malaria persisting in the population) equilibria. Key to our analysis is the definition of a reproductive number, R₀ (the number of new infections caused by one individual in an otherwise fully susceptible population through the duration of the infectious period). We prove the loss of stability of the disease-free equilibrium as R0 increases through R₀ = 1. Using global bifurcation theory developed by Rabinowitz, we show the bifurcation of endemic equilibria at R₀ = 1. This bifurcation can be either supercritical (leading to stable endemic equilibria for R₀ > 1) or subcritical (leading to stable endemic equilibria for R₀ < 1 in the presence of hysteresis). We compile two reasonable sets of values for the parameters in the model: for areas of high and low transmission. We compute sensitivity indices of R₀ and the endemic equilibrium to the parameters around the baseline values. R₀ is most sensitive to the mosquito biting rate in both high and low transmission areas. The fraction of infectious humans at the endemic equilibrium is most sensitive to the mosquito biting rate in low transmission areas, and to the human recovery rate in high transmission areas. This sensitivity analysis allows us to compare the effectiveness of different control strategies. According to our model, the most effective methods for malaria control are the use of insecticide-treated bed nets and the prompt diagnosis and treatment of infected individuals.

The recent trend of mass customization has redefined the way companies do business. Each individual customer is now their own market, requiring products specific to their wants and needs at mass production prices. This need for ever-increasing variety is a significant challenge for industry that many times leads to ballooning manufacturing costs and lower product performance. One approach that has received widespread attention and implementation is to develop families of products from standardized product platforms. While, many methods have been developed to address different challenges within product platform design, they are not without their limitations/tradeoffs and therefore leave much room for development and improvement. The Product Platform Constructal Theory Method (PPCTM), developed by Dr. Gabriel Hernandez, is a novel approach for developing product platforms that enable customizable products. Rooted in the tenants of hierarchic systems theory and constructal theory, the PPCTM solves for the product platform as a problem of optimization of access in a geometric space. The result is a hierarchical organization of the modes for managing variety and the specification of their commonality across the product platform. Overall, the PPCTM offers an extremely comprehensive product platform design method, with the ability to accommodate multi-platform design, multiple design specifications, non-uniform demand modeling, and multi-objective decision-making. One limitation of this method is that the selection of platform variables and the modes for managing product variety must be pre-specified or determined ad hoc by the designer. This thesis seeks to address this limitation through the integration of a sensitivity-based analysis method to determine the effect of platform variable variation on the family performance. The result of this work is a Sensitivity-based PPCTM that facilitates the selection of common platform variables, such that modes for managing variety can be ranked and applied to the space element hierarchy. The proposed method is illustrated with three examples: the design of a line of customizable pressure vessels, universal electric motors, and finger pumps.

For scientists, the accuracy of their results is a constant concern. As the programs they write to support their research grow in complexity, there is a greater need to understand what causes the inaccuracies in their outputs, and how they can be mitigated. This problem is difficult because the inaccuracies in the outputs come from a variety of sources in both the scientific and computing domains. Furthermore, as most programs lack a testing oracle, there is no simple way to validate the results. We define a model for the analysis of error propagation in software. Its novel combination of interval arithmetic and automatic differentiation allows for the error accumulated in an output to be measurable at runtime, attributable to individual inputs and functions, and identifiable as either input error, round-off error, or error from a different source. This allows for the identification of the subset of inputs and functions that are most responsible for the error seen in an output and how it can be best mitigated. We demonstrate the effectiveness of our model by analyzing a small case study from the field of nuclear engineering, where we are able to attribute the contribution of over 99% of the error to 3 functions out of 15, and identify the causes for the observed error. / Thesis (Master, Computing) -- Queen's University, 2012-09-24 14:12:25.659