Aplicação do método de Monte Carlo na avaliação da incerteza de medição do ensaio de tenacidade à fratura KIC

A determinação de forma confiável das propriedades mecânicas dos materiais é um fator fundamental para a aplicação dos mesmos em engenharia, e a estimativa da incerteza de medição pelos laboratórios de ensaio impacta diretamente na interpretação do resultado. O Guia para a Expressão da Incerteza de Medição (GUM) é um documento que estabelece os critérios para o cálculo e a expressão da incerteza de medição, considerando as diferentes influências de cada parâmetro que compõe o valor da incerteza. Porém, a literatura recente demonstra que o GUM possui limitações, especialmente nos casos em que o modelo matemático de medição possui elevado grau de não linearidade e devido a pressupostos assumidos em relação à distribuição de probabilidade final. Nesses casos, recomenda-se que a incerteza de medição seja determinada através do Método de Monte Carlo (MMC), que considera a propagação da distribuição ao invés da propagação das incertezas. Assim, dadas as vantagens do MMC, e considerando a importância de se estimar a incerteza de medição de ensaios, esse trabalho tem como objetivo principal implementar o cálculo da incerteza para o ensaio de tenacidade à fratura KIC de materiais metálicos através do Método de Monte Carlo. Além disso, uma análise da influência da distribuição de probabilidade nos resultados de incerteza de medição foi realizada, através de um projeto de experimentos. Os resultados do trabalho demonstram a importância do uso do Método de Monte Carlo para a estimativa da incerteza de medição de ensaios e confirmam que a forma da distribuição de probabilidade possui influência significativa nos valores de incerteza de medição obtidos para o ensaio de tenacidade à fratura KIC.A reliable determination of mechanical properties of materials is fundamental for their application in engineering, and an estimation of measurement uncertainty by testing laboratories has a direct impact on the interpretation of the results. The Guide to the Expression of Uncertainty in Measurement (GUM) is a document that establishes the criteria for the calculation and expression of measurement uncertainty, considering how different influences of each parameter make up the uncertainty value. However, recent literature has demonstrated that GUM has limitations, especially in those cases where the mathematical measurement model has a high degree of non-linearity and due to assumptions considered in the final probability distribution. In such cases, it is recommended that measurement uncertainty is estimated using the Monte Carlo Method (MCM), which considers the propagation of distributions approach instead of propagation of uncertainties. Thus, given the advantages of MCM, and considering the importance of the estimation of testing measurement uncertainty, this work aims to implement uncertainty calculation for the KIC fracture toughness testing of metallic materials by the Monte Carlo Method. In addition, an analysis of the influence of the probability distribution in the final uncertainty results was performed through design of experiments. Results have confirmed the importance of the Monte Carlo Method for an appropriate estimation of the measurement uncertainty of mechanical tests and also confirm the probability distribution has a significant influence in the measurement uncertainty values of fracture toughness KIC

Global and regional trends of Aerosol Optical Thickness derived using satellite- and ground-based observations

Atmospheric aerosol plays a critical role for human health, air quality, long range transport of pollution, and the Earth s radiative balance, thereby influencing global climate change. To test our scientific understanding and provide an evidence base for policymakers, long-term temporal changes of local, regional, and global aerosols are needed. Remote sensing from satellite borne and ground based observations offers unique opportunities to provide such data. However, only a few studies have discussed the limitations, associated with unrepresentative sampling originating from large/persistent cloud disturbance and limited/different sampling (limited orbital periods and different sampling times) in the trend analysis. Using a linear weighted model, the long-term trends of global AOTs from various polar orbiting satellites and ground observations: MODIS (aboard Terra), MISR (Terra), SeaWiFS (OrbView-2), MODIS (Aqua), and AERONET have been analyzed. In this manner, the present study attempts to minimize the influence of unrepresentative sampling in the trend analysis. Throughout terrestrial and marine regions, temporal increase of cloud-free AOTs were dominat over the globe (GL), northern (NH), and southern hemisphere (SH) (up to 0.00348±0.00185 for GL, 0.00514±0.00272 for NH, and 0.00232±0.00124 per year for SH). Generally, consistently in all observations, the weighted trends over Eastern US and OECD Europe showed a strong decreasing AOT (up to -0.00376±0.00174 for Eastern US and -0.00530±0.00304 per year for OECD Europe) attributed to the recent environmental legislation and resulting regulation of emissions. A significant increase was observed over Saharan/Arabian deserts, South, and East Asia (up to 0.00618±0.00326, 0.01452±0.00615, and 0.01939±0.00986 per year, respectively). These in part dramatic increases are caused by the enhanced amount of aerosol transported/emitted from industrialization, urbanization, deforestation, desertification, and climate change. Overall large/persistent cloud disturbance all year round and the limited/different sampling of polar orbiting satellites represent a challenge, which has been addressed successfully in this study for the accurate determination of aerosol amount and its trends

Accurate Non-Iterative Modelling and Inference of Longitudinal Neuroimaging Data

In recent years, increasing efforts have been made to collect longitudinal neuroimaging data in order to study how brains change over time. However, the popular methods used to analyse such kind of data may not always be appropriate (e.g., overly sensitive to model misspecifications, difficult to specify adequately or prohibitively slow to compute) and may sometimes lead to erroneous conclusions. Motivated by these shortcomings, in this dissertation, we have proposed and studied the use of an alternative method, referred to as “the Sandwich Estimator method”, and have demonstrated that it is a fast, easy-to-specify and accurate option to analyse longitudinal or repeated-measures neuroimaging data