No description available.

The main focus of this research is to use graph-theoretic formulations to develop an automated algorithm for the generation of sensitivity equations. The idea is to combine the benefits of direct differentiation with that of graph-theoretic formulation. The primary deliverable of this work is the developed software module which can derive the system equations and the sensitivity equations directly from the linear graph of the system. Sensitivity analysis refers to the study of changes in system behaviour brought about by the changes in model parameters. Due to the rapid increase in the sizes and complexities of the models being analyzed, it is important to extend the capabilities of the current tools of sensitivity analysis, and an automated, efficient, and accurate method for the generation of sensitivity equations is highly desirable. In this work, a graph-theoretic algorithm is developed to generate the sensitivity equations. In the current implementation, the proposed algorithm uses direct differentiation to generate sensitivity equations at the component level and graph-theoretic methods to assemble the equation fragments to form the sensitivity equations. This way certain amount of control can be established over the size and complexity of the generated sensitivity equations. The implementation of the algorithm is based on a commercial software package \verb MapleSim[Multibody] and can generate governing and sensitivity equations for multibody models created in MapleSim. In this thesis, the algorithm is tested on various mechanical, hydraulic, electro-chemical, multibody, and multi-domain systems. The generated sensitivity information are used to perform design optimization and parametric importance studies. The sensitivity results are validated using finite difference formulations. The results demonstrate that graph-theoretic sensitivity analysis is an automated, accurate, algorithmic method of generation for sensitivity equations, which enables the user to have some control over the form and complexity of the generated equations. The results show that the graph-theoretic method is more efficient than the finite difference approach. It is also demonstrated that the efficiency of the generated equations are at par or better than the equation obtained by direct differentiation.

Anxiety Sensitivity (AS) consists of beliefs that the experience of anxiety symptoms leads to illness or additional anxiety. The aim of the present study was to examine the factor structure of the Turkish version of Anxiety Sensitivity Index&amp / #8211 / Revised (ASI-R), and to investigate associations among AS, alcohol use and drinking motives in university students and alcohol dependent inpatients. The participants were 411 university students (225 females and 186 males) and 55 (3 females and 52 males) alcohol dependent inpatients. All participants were administered ASI-R, State-Trait Anxiety Inventory-Trait Form, Beck Depression Inventory, Drinking Motives Questionnaire-Revised, and Demographic Information Form. Exploratory factor analyses revealed four lower order factors of the ASI-R: (1) fear of respiratory symptoms / (2) fear of cardiovascular symptoms / (3) fear of cognitive dyscontrol / and (4) fear of publicly observable anxiety symptoms. ANOVA revealed that the frequency and amount of alcohol use were significantly higher in male students than females. Males reported more alcohol use for Coping and Conformity Motives than did females. Regression analyses revealed that only fear of cognitive dyscontrol significantly predicted hazardous alcohol use of students. Coping Motives significantly predicted alcohol use after controlling the effects of demographics, depression and ASI-R lower order factors in students using alcohol. Fear of publicly observable anxiety symptoms significantly predicted frequency of alcohol use in students using alcohol. Students reported using alcohol mostly for Enhancement, Social, Coping, and Conformity Motives, respectively. Students with high AS reported more alcohol use for Coping, Social and Conformity Motives than those with moderate and low AS. Fear of cognitive dyscontrol and fear of publicly observable anxiety symptoms explained a significant variance of drinking motives in students. In alcohol dependent inpatients, only fear of respiratory symptoms had a significant correlation with Coping Motives. Patients reported having used alcohol mostly for Coping, Enhancement, Social, and Conformity Motives, respectively. Coping and Enhancement Motives were significantly correlated with alcohol use. Results were discussed within the findings in the literature.

No description available.

A aplicação de modelos hidrológicos vem sendo bastante utilizada como apoio à tomada de decisão no planejamento dos recursos hídricos. Tendo em vista que os dados que servem de entrada para esses modelos estão sujeitos a erros diversos, o presente estudo teve o intuito de contribuir com o conhecimento do impacto desses erros no desempenho do modelo e na estimativa de seus parâmetros. O modelo analisado foi o IPH II fazendo uso do programa computacional WIN_IPH2. Entendendo que a avaliação da sensibilidade ainda é uma área que requer mais estudos, o presente trabalho é focado na utilização das análises de sensibilidade estática e dinâmica. Para isso foram geradas diversas séries temporais de dados de entradas do modelo hidrológico obtidas pela perturbação da série de dados observados. A perturbação foi representada por erros aleatórios (seguindo uma distribuição normal ou uniforme) ou sistemáticos incorporados ás séries temporais das variáveis: precipitação e evapotranspiração. Posteriormente, as análises de sensibilidade estática e dinâmica foram executadas. Para efetuar o acompanhamento da interferência dos erros, na eficiência do modelo, foi feita a avaliação dos resultados obtidos com a aplicação do modelo WIN_IPH2 para diferentes medidas de desempenho, e verificado o impacto dos erros nos dados de entrada no desempenho do modelo (sensibilidade estática) e no desempenho do modelo e na estimativa dos parâmetros (sensibilidade dinâmica). Na análise de sensibilidade estática verificou-se o decaimento mais acentuado da eficiência do modelo, em comparação com a análise de sensibilidade dinâmica, onde o modelo consegue contornar os erros nos dados de entrada com a alteração dos valores dos parâmetros. Por fim, o presente estudo confirmou as conclusões obtidas em estudos anteriores: Oudin et al. (2006), Andréassian et al. (2004), Kavetski et al. (2003). Além disso, o presente estudo apontou para outros fatores, na medida em que, observa-se junto à tendência do desempenho do modelo cair quando a intensidade do erro gerado é elevada, a importância de avaliar o possível comprometimento de dados em eventos extremos, uma vez que, nessa situação o desempenho do modelo passa a ser afetado de forma mais acentuada. / The hydrologic models had been used to support the decision making in water resources management. Since the input data of those models are subject to several kinds of errors, this study aimed to assess how this errors affect the model performance and the estimate of its parameters. The hydrologic model IPH II was used. Perceiving that the sensitivity analysis is still a field that requires further knowledge, this study was focused in the use of the dynamic and the static sensitivity procedures. In this sense, several time series of input data were obtained through the perturbations of an observed time serie. The perturbation was represented by the addition of random errors (with a normal or uniform distribution) or systematic errors to the observed time series of evapotranspiration and precipitation. Then, the static and dynamic sensibility analysis were performed. The effect of input data errors was assessed for several calibration processes of the IPH II using several performance measures. Thus, modification of the model performance (static sensitivity analysis) and model performance and parameter estimation (dynamic sensitivity analysis) were estimated. In the static sensitivity analysis it was found a most pronounced decay of the model efficiency in comparison with the dynamic sensitivity analysis, where the model can circumvent the errors in the input data with modification of the optimum parameter values. Finally, this study confirmed the conclusions of other previous studies as Oudin et al. (2006), Andréassian et al. (2004), Kavetski et al. (2003). In addition this study found other factors, as was observed that if the intensity of the error is high in an extreme event of precipitation, it reduced the model performance more than when it is low, in spite of the time series of errors have the same statistics.

A aplicação de modelos hidrológicos vem sendo bastante utilizada como apoio à tomada de decisão no planejamento dos recursos hídricos. Tendo em vista que os dados que servem de entrada para esses modelos estão sujeitos a erros diversos, o presente estudo teve o intuito de contribuir com o conhecimento do impacto desses erros no desempenho do modelo e na estimativa de seus parâmetros. O modelo analisado foi o IPH II fazendo uso do programa computacional WIN_IPH2. Entendendo que a avaliação da sensibilidade ainda é uma área que requer mais estudos, o presente trabalho é focado na utilização das análises de sensibilidade estática e dinâmica. Para isso foram geradas diversas séries temporais de dados de entradas do modelo hidrológico obtidas pela perturbação da série de dados observados. A perturbação foi representada por erros aleatórios (seguindo uma distribuição normal ou uniforme) ou sistemáticos incorporados ás séries temporais das variáveis: precipitação e evapotranspiração. Posteriormente, as análises de sensibilidade estática e dinâmica foram executadas. Para efetuar o acompanhamento da interferência dos erros, na eficiência do modelo, foi feita a avaliação dos resultados obtidos com a aplicação do modelo WIN_IPH2 para diferentes medidas de desempenho, e verificado o impacto dos erros nos dados de entrada no desempenho do modelo (sensibilidade estática) e no desempenho do modelo e na estimativa dos parâmetros (sensibilidade dinâmica). Na análise de sensibilidade estática verificou-se o decaimento mais acentuado da eficiência do modelo, em comparação com a análise de sensibilidade dinâmica, onde o modelo consegue contornar os erros nos dados de entrada com a alteração dos valores dos parâmetros. Por fim, o presente estudo confirmou as conclusões obtidas em estudos anteriores: Oudin et al. (2006), Andréassian et al. (2004), Kavetski et al. (2003). Além disso, o presente estudo apontou para outros fatores, na medida em que, observa-se junto à tendência do desempenho do modelo cair quando a intensidade do erro gerado é elevada, a importância de avaliar o possível comprometimento de dados em eventos extremos, uma vez que, nessa situação o desempenho do modelo passa a ser afetado de forma mais acentuada. / The hydrologic models had been used to support the decision making in water resources management. Since the input data of those models are subject to several kinds of errors, this study aimed to assess how this errors affect the model performance and the estimate of its parameters. The hydrologic model IPH II was used. Perceiving that the sensitivity analysis is still a field that requires further knowledge, this study was focused in the use of the dynamic and the static sensitivity procedures. In this sense, several time series of input data were obtained through the perturbations of an observed time serie. The perturbation was represented by the addition of random errors (with a normal or uniform distribution) or systematic errors to the observed time series of evapotranspiration and precipitation. Then, the static and dynamic sensibility analysis were performed. The effect of input data errors was assessed for several calibration processes of the IPH II using several performance measures. Thus, modification of the model performance (static sensitivity analysis) and model performance and parameter estimation (dynamic sensitivity analysis) were estimated. In the static sensitivity analysis it was found a most pronounced decay of the model efficiency in comparison with the dynamic sensitivity analysis, where the model can circumvent the errors in the input data with modification of the optimum parameter values. Finally, this study confirmed the conclusions of other previous studies as Oudin et al. (2006), Andréassian et al. (2004), Kavetski et al. (2003). In addition this study found other factors, as was observed that if the intensity of the error is high in an extreme event of precipitation, it reduced the model performance more than when it is low, in spite of the time series of errors have the same statistics.

A aplicação de modelos hidrológicos vem sendo bastante utilizada como apoio à tomada de decisão no planejamento dos recursos hídricos. Tendo em vista que os dados que servem de entrada para esses modelos estão sujeitos a erros diversos, o presente estudo teve o intuito de contribuir com o conhecimento do impacto desses erros no desempenho do modelo e na estimativa de seus parâmetros. O modelo analisado foi o IPH II fazendo uso do programa computacional WIN_IPH2. Entendendo que a avaliação da sensibilidade ainda é uma área que requer mais estudos, o presente trabalho é focado na utilização das análises de sensibilidade estática e dinâmica. Para isso foram geradas diversas séries temporais de dados de entradas do modelo hidrológico obtidas pela perturbação da série de dados observados. A perturbação foi representada por erros aleatórios (seguindo uma distribuição normal ou uniforme) ou sistemáticos incorporados ás séries temporais das variáveis: precipitação e evapotranspiração. Posteriormente, as análises de sensibilidade estática e dinâmica foram executadas. Para efetuar o acompanhamento da interferência dos erros, na eficiência do modelo, foi feita a avaliação dos resultados obtidos com a aplicação do modelo WIN_IPH2 para diferentes medidas de desempenho, e verificado o impacto dos erros nos dados de entrada no desempenho do modelo (sensibilidade estática) e no desempenho do modelo e na estimativa dos parâmetros (sensibilidade dinâmica). Na análise de sensibilidade estática verificou-se o decaimento mais acentuado da eficiência do modelo, em comparação com a análise de sensibilidade dinâmica, onde o modelo consegue contornar os erros nos dados de entrada com a alteração dos valores dos parâmetros. Por fim, o presente estudo confirmou as conclusões obtidas em estudos anteriores: Oudin et al. (2006), Andréassian et al. (2004), Kavetski et al. (2003). Além disso, o presente estudo apontou para outros fatores, na medida em que, observa-se junto à tendência do desempenho do modelo cair quando a intensidade do erro gerado é elevada, a importância de avaliar o possível comprometimento de dados em eventos extremos, uma vez que, nessa situação o desempenho do modelo passa a ser afetado de forma mais acentuada. / The hydrologic models had been used to support the decision making in water resources management. Since the input data of those models are subject to several kinds of errors, this study aimed to assess how this errors affect the model performance and the estimate of its parameters. The hydrologic model IPH II was used. Perceiving that the sensitivity analysis is still a field that requires further knowledge, this study was focused in the use of the dynamic and the static sensitivity procedures. In this sense, several time series of input data were obtained through the perturbations of an observed time serie. The perturbation was represented by the addition of random errors (with a normal or uniform distribution) or systematic errors to the observed time series of evapotranspiration and precipitation. Then, the static and dynamic sensibility analysis were performed. The effect of input data errors was assessed for several calibration processes of the IPH II using several performance measures. Thus, modification of the model performance (static sensitivity analysis) and model performance and parameter estimation (dynamic sensitivity analysis) were estimated. In the static sensitivity analysis it was found a most pronounced decay of the model efficiency in comparison with the dynamic sensitivity analysis, where the model can circumvent the errors in the input data with modification of the optimum parameter values. Finally, this study confirmed the conclusions of other previous studies as Oudin et al. (2006), Andréassian et al. (2004), Kavetski et al. (2003). In addition this study found other factors, as was observed that if the intensity of the error is high in an extreme event of precipitation, it reduced the model performance more than when it is low, in spite of the time series of errors have the same statistics.

We develop a stochastic parametrization, based on a 'simple' deterministic model for the dynamics of steady longshore currents, that produces ensembles that are statistically consistent with field observations of these currents. Unlike deterministic models, stochastic parameterization incorporates randomness and hence can only match the observations in a statistical sense. Unlike statistical emulators, in which the model is tuned to the statistical structure of the observation, stochastic parametrization are not directly tuned to match the statistics of the observations. Rather, stochastic parameterization combines deterministic, i.e physics based models with stochastic models for the "missing physics" to create hybrid models, that are stochastic, but yet can be used for making predictions, especially in the context of data assimilation. We introduce a novel measure of the utility of stochastic models of complex processes, that we call consistency of sensitivity. A model with poor consistency of sensitivity requires a great deal of tuning of parameters and has a very narrow range of realistic parameters leading to outcomes consistent with a reasonable spectrum of physical outcomes. We apply this metric to our stochastic parametrization and show that, the loss of certainty inherent in model due to its stochastic nature is offset by the model's resulting consistency of sensitivity. In particular, the stochastic model still retains the forward sensitivity of the deterministic model and hence respects important structural/physical constraints, yet has a broader range of parameters capable of producing outcomes consistent with the field data used in evaluating the model. This leads to an expanded range of model applicability. We show, in the context of data assimilation, the stochastic parametrization of longshore currents achieves good results in capturing the statistics of observation that were not used in tuning the model.

No description available.

Understanding the effects of uncertainty on modeling has seen an increased focus as engineering disciplines rely more heavily on computational modeling of complex physical processes to predict system performance and make informed engineering decisions. These computational methods often use simplified models and assumptions with models calibrated using uncertain, averaged experimental data. This commonplace method ignores the effects of uncertainty on the variation of modeling output. Qualitatively, uncertainty is the possibility of error existing from experiment to experiment, from model to model, or from experiment to model. Quantitatively, uncertainty quantification (UQ) methodologies seek to determine the how variable an engineering system is when subjected to variation in the factors that control it. Often performed in conjunction, sensitivity analysis (SA) methods seek to describe what model factor contributes the most to variation in model output. UQ and SA methodologies were employed in the analysis of the Modified Embedded Atom Method (MEAM) model for a pure aluminum, a microstructure sensitive fatigue crack growth model for polycarbonate, and the MultiStage Fatigue (MSF) model for AZ31 magnesium alloy. For the MEAM model, local uncertainty and sensitivity measures were investigated for the purpose of improving model calibrations. In polycarbonate fatigue crack growth, a Monte Carlo method is implemented in code and employed to investigate how variations in model input factors effect fatigue crack growth predictions. Lastly, in the analysis of fatigue life predictions with the MSF model for AZ31, the expected fatigue performance range due to variation in experimental parameters is investigated using both Monte Carlo Simple Random Sampling (MCSRS) methods and the estimation of first order effects indices using the Fourier Amplitude Sensitivity Test (FAST) method.