Análise numérica via MEC e experimental via imagens térmicas para predição da condutividade térmica efetiva

Monografia (graduação)—Universidade de Brasília, Faculdade de Tecnologia, Departamento de Engenharia Mecânica, 2013.Este trabalho apresenta um estudo sobre a condutividade térmica efetiva de materiais de composição heterogênea em duas dimensões. O Método dos Elementos de Contorno (MEC) é empregado para resolver as equações diferenciais que regem os problemas potenciais em regime permanente. A técnica de subregiões foi utilizada no modelamento para considerar o efeito destas inclusões dentro da matriz. Na implementação numérica, as inclusões são geradas aleatoriamente no domínio de um Elemento de Volume Representativo (EVR). O método do EVR aplica a Teoria de Campos Médios para encontrar as propriedades efetivas (macroscópicas) deste material de composição heterogênea. O material é caracterizado por uma fração de volume pré-determinada, assim como os diâmetros das inclusões. Cada conjunto de amostras é submetido à análise um número suficiente de vezes, a fim de garantir estabilidade estatística dos resultados. São analisados EVR’s para diversas frações de volume cujas propriedades efetivas são obtidas e analisadas. A metodologia desenvolvida empregando o MEC mostrou-se bastante eficiente, principalmente para casos com um grande número de inclusões, sugerindo uma alternativa aos métodos tradicionais de solução numéricos, como elementos finitos e volumes finitos. Para a parte experimental foi desenvolvido um aparato para reproduzir o problema estudado numericamente. O experimento permitiu avaliar o campo de temperatura utilizando-se imagens termográficas. Por fim, os resultados experimentais foram comparados com os obtidos numericamente. Neste sentido a metodologia proposta mostrou-se satisfatória para predizer a condutividade térmica efetiva de materiais de composição heterogênea. _____________________________________________________________________________ ABSTRACTThis work presents a study on the effective thermal conductivity in material with heterogeneous composition in two dimensions. The Boundary Elements Method is used to solve the steady state potential equations. The sub regions technique was implemented in order to take into account the effects of these inclusions inside the domain. In the numerical implementation, the inclusions are randomly generated in a Representative Volume Element (RVE) domain. The Average Field Theory is used to predict the effective properties (macroscopic) of the material with heterogeneous composition. The material is characterized by a specified volume fraction as well as the inclusion’s size. Each set of samples is analyzed several times in order to guarantee statistical stability of the results. RVE’s for several cases of volume fraction are analyzed and discussed. The developed methodology is very efficient, particularly for samples containing a large number of inclusions, suggesting an alternative solution to the traditional numerical methods, such as finite element and finite volume method. Regarding to the experimental part it was developed an apparatus in order to reproduce the same problem studied numerically. This experiment allowed evaluating the temperature field due to the employment of thermographic imaging. Finally both results obtained via numerical and experimental methods were compared showing good agreement. In this sense the purposed methodology showed feasible for predicting the effective thermal conductive for material with heterogeneous composition

Uma abordagem via CNC para geração de padrões de pontos para análises por CDI

A correlação digital de imagens(CDI) é uma técnica de metrologia óptica capaz de fornecer informações acerca da deformação de superfícies carregadas mecanicamente a partir da análise de imagens digitais desta superfície. Isto se dá por meio da análise comparativa entre imagens digitais tomadas antes e depois do carregamento.Esta técnica requer que um padrão de pontos de distribuição aleatória seja marcado sobre a superfície a ser estudada. Este padrão de pontos fornece uma referência para o procedimento de correlação, permitindo que o CDI obtenha a deformação da superfície com base na deformação do padrão. Diversos estudos recentes afirmam que a qualidade do padrão de pontos utilizados tem direta influência na qualidade dos resultados das análises por CDI.Apesar de muitos estudos numéricos apontarem parâmetros ótimos para os padrões de pontos, a carência de técnicas adequadas para a sua reprodução sobre superfícies impossibilita a sua utilização prática.Diante disto, este trabalho apresenta uma metodologia numérica para geração dos padrões de pontos e também o desenvolvimento de um equipamento de marcação por comando numérico computadorizado capaz de reproduzi-los sobre superfícies reais com precisão.Digital image correlation(DIC) is an optical metrology technique used to acquire deformational and displacement data of mechanically loaded surfaces based on digital images taken before and after the loading. To achieve this, this technique requires the studied surface to have a randomly distributed speckle pattern marked over it. This speckle pattern provides a reference for the image correlation procedure, allowing the DIC system to obtain the surface deformation based on the speckle pattern deformation. In this sense, as already stated in many recent studies, the quality of the used speckle pattern has direct influenceon the quality of the DIC results. Although many numerical studies have already defined near optimum parameters for a speckle pattern, the lack of proper techniques to reproduce it on surfaces rendersit impractical.In sight of this, this work presents anumerical methodology for the generation of speckle patterns and the development of a computadorized numerical control marking equipment capable of accurately reproducing it on real surfaces.La correlación digital de imagen (CDI) es una técnica de metrología óptica capaz de brindarinformaciones acerca de la deformación de superficies cargadas mecánicamente a partir del análisis de imágenes digitales de esa superficie,anteriores y posteriores a la carga. Para eso, esta técnica requiere que un patrón de puntos, de distribución aleatoria, sea diseñado sobre la superficie a ser estudiada. Este patrón de puntos sirve de referencia para el análisis de correlación de imagen, de manera que las informaciones de deformación de la superficie son obtenidas con base en la deformación experimentada por este patrón de puntos después de la aplicación de la carga. De esta forma, como muestran diferentes estudios recientes, la calidad del patrón de puntos utilizados tiene una influencia directa en la calidad de los resultados de los análisis hechos por CDI. A pesar de que muchos estudios numéricos muestran buenos parámetros para los patrones de puntos, la falta de técnicas adecuadas para su reproducción sobre superficies imposibilita su utilización práctica. Con lo anterior, este trabajo presenta una metodología numérica para la generación de los patrones de puntos y también el desarrollo de un equipamientode impresión de puntos por comando numérico computarizado capaz de reproducirlos sobre superficies reales con precisión

A novel to perform a thermoelastic analysis using digital image correlation and the boundary element method

This work aims for a novel thermoelastic analysis methodology based on experimental steady-state temperature data and numerical displacement evaluation. The temperature data was acquired using thermal imaging and used as the input for a boundary element method (BEM) routine to evaluate its consequent thermoelastic displacement. The thermoelastic contribution to the resultant displacement arises in the BEM formulation as a domain integral, which compromises the main benefits of the BEM. To avoid the necessity of domain discretization, the radial integration method (RIM) was applied to convert the thermoelastic domain integral into an equivalent boundary integral. Due to its mathematical development, the resultant formulation from RIM requires the temperature difference to be input as a function. The efficacy of the proposed methodology was verified based on experimental displacement fields obtained via digital image correlation (DIC) analysis. For this purpose, a CNC (computer numerical control) marker was developed to print the speckle pattern instead of preparing the specimen by using manual spray paint or using commercially available pre-painted adhesives. The good agreement observed in the comparison between the numerical and experimental displacements indicates the viability of the proposed methodology

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear un derstanding of how ecological communities respond to environmental change across time and space.3,4 While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes,5–7 vast areas of the tropics remain understudied.8–11 In the American tropics, Amazonia stands out as the world’s most diverse rainforest and the primary source of Neotropical biodiversity,12 but it remains among the least known forests in America and is often underrepre sented in biodiversity databases.13–15 To worsen this situation, human-induced modifications16,17 may elim inate pieces of the Amazon’s biodiversity puzzle before we can use them to understand how ecological com munities are responding. To increase generalization and applicability of biodiversity knowledge,18,19 it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple or ganism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region’s vulnerability to environmental change. 15%–18% of the most ne glected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lostinfo:eu-repo/semantics/publishedVersio

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear understanding of how ecological communities respond to environmental change across time and space.3,4 While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes,5,6,7 vast areas of the tropics remain understudied.8,9,10,11 In the American tropics, Amazonia stands out as the world's most diverse rainforest and the primary source of Neotropical biodiversity,12 but it remains among the least known forests in America and is often underrepresented in biodiversity databases.13,14,15 To worsen this situation, human-induced modifications16,17 may eliminate pieces of the Amazon's biodiversity puzzle before we can use them to understand how ecological communities are responding. To increase generalization and applicability of biodiversity knowledge,18,19 it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple organism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region's vulnerability to environmental change. 15%–18% of the most neglected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lost