Neste trabalho se buscou criar uma base tecnológica para síntese digital e visualização virtual de microestruturas de materiais policristalinos monofásicos, visando disponibilizar software e metodologias de baixo custo aos pesquisadores da área ou de áreas correlatas. Para isso foram levantados e testados métodos, sistemas, bibliotecas e algoritmos computacionais pertinentes ao problema em questão. A técnica de síntese escolhida adotou uma simulação física de empacotamento de partículas seguida por tesselação espacial baseada no diagrama Voronoi. Para testar a abordagem uma amostra de um material real foi reconstituída digitalmente. O modelo reproduziu com grande precisão a distribuição de tamanhos de grão, o número de faces por grão e o número de vizinhos imediatos da referência. Na frente de visualização virtual buscou-se definir um modelo capaz de lidar com grandes quantidades de dados e baseado em princípios cognitivos sólidos, que permitisse maior extração de conhecimento de modelos microestruturais. A técnica de visualização em múltiplas escalas foi considerada a mais apropriada aos modelos cujos objetos e detalhes abrangem diversas escalas espaciais, permitindo ao computador lidar com vastas quantidades de dados ao alternar entre qualidade e quantidade no processo de geração de imagens. Técnicas de visualização tradicionais também foram testadas e a técnica de corte se mostrou fundamental, principalmente para a exploração direta do interior do modelo, mas também para a extração de dados voltados a análise microestrutural estereológica. / The main goal of this work is to create a technological foundation for digital synthesis and virtual visualization of single-phase polycrystalline materials microstrutures, aiming to offer low cost software and methodologies to materials science researchers and alike. Several methods, applications, libraries and algorithms were tested and the most appropriate were selected for further exploration. The chosen microstructural synthesis technique uses newtonian particle packing simulation, followed by a Voronoi-based tesselation. This simple approach were put to test using a real material sample. The sample were digitally built and meaningfull parameters like grain size distribution, edges per face and mean number of neighbours were replicated with acceptable precision. Regarding visualization, the most relevant issue was the specification of a computationally scalable method based on proven cognitive principles, capable to deal with a huge amount of information and to support efficient knowledge extraction from microstructural models. The multiscale approach has proved to be the most suited for models that spans several scales in space, allowing computers to store and display large quantities of data and to manage the tradeoff between quality and quantity in the rendering process. Traditional visualization techniques were tested as well and section visualization has proved to be paramount for internal model visualization, as it is for stereological microstructural analysis.

Neste trabalho se buscou criar uma base tecnológica para síntese digital e visualização virtual de microestruturas de materiais policristalinos monofásicos, visando disponibilizar software e metodologias de baixo custo aos pesquisadores da área ou de áreas correlatas. Para isso foram levantados e testados métodos, sistemas, bibliotecas e algoritmos computacionais pertinentes ao problema em questão. A técnica de síntese escolhida adotou uma simulação física de empacotamento de partículas seguida por tesselação espacial baseada no diagrama Voronoi. Para testar a abordagem uma amostra de um material real foi reconstituída digitalmente. O modelo reproduziu com grande precisão a distribuição de tamanhos de grão, o número de faces por grão e o número de vizinhos imediatos da referência. Na frente de visualização virtual buscou-se definir um modelo capaz de lidar com grandes quantidades de dados e baseado em princípios cognitivos sólidos, que permitisse maior extração de conhecimento de modelos microestruturais. A técnica de visualização em múltiplas escalas foi considerada a mais apropriada aos modelos cujos objetos e detalhes abrangem diversas escalas espaciais, permitindo ao computador lidar com vastas quantidades de dados ao alternar entre qualidade e quantidade no processo de geração de imagens. Técnicas de visualização tradicionais também foram testadas e a técnica de corte se mostrou fundamental, principalmente para a exploração direta do interior do modelo, mas também para a extração de dados voltados a análise microestrutural estereológica. / The main goal of this work is to create a technological foundation for digital synthesis and virtual visualization of single-phase polycrystalline materials microstrutures, aiming to offer low cost software and methodologies to materials science researchers and alike. Several methods, applications, libraries and algorithms were tested and the most appropriate were selected for further exploration. The chosen microstructural synthesis technique uses newtonian particle packing simulation, followed by a Voronoi-based tesselation. This simple approach were put to test using a real material sample. The sample were digitally built and meaningfull parameters like grain size distribution, edges per face and mean number of neighbours were replicated with acceptable precision. Regarding visualization, the most relevant issue was the specification of a computationally scalable method based on proven cognitive principles, capable to deal with a huge amount of information and to support efficient knowledge extraction from microstructural models. The multiscale approach has proved to be the most suited for models that spans several scales in space, allowing computers to store and display large quantities of data and to manage the tradeoff between quality and quantity in the rendering process. Traditional visualization techniques were tested as well and section visualization has proved to be paramount for internal model visualization, as it is for stereological microstructural analysis.

Diffusion of water molecules in biological tissues depends on several microstructural properties. Therefore, diffusion Magnetic Resonance Imaging (dMRI) is a useful tool to infer and study microstructural brain changes in the context of human development, ageing and neuropathology. In this thesis, the state-of-the-art of advanced dMRI techniques is explored and strategies to overcome or reduce its pitfalls are developed and validated. Firstly, it is shown that PCA denoising and Gibbs artefact suppression algorithms provide an optimal compromise between increased precision of diffusion measures and the loss of tissue's diffusion non-Gaussian information. Secondly, the spatial information provided by the diffusion kurtosis imaging (DKI) technique is explored and used to resolve crossing fibres and generalize diffusion measures to cases not limited to well-aligned white matter fibres. Thirdly, as an alternative to diffusion microstructural modelling techniques such as the neurite orientation dispersion and density imaging (NODDI), it is shown that spherical deconvolution techniques can be used to characterize fibre crossing and dispersion simultaneously. Fourthly, free water volume fraction estimates provided by the free water diffusion tensor imaging (fwDTI) are shown to be useful to detect and remove voxels corrupted by cerebrospinal fluid (CSF) partial volume effects. Finally, dMRI techniques are applied to the diffusion data from the large collaborative Cambridge Centre for Ageing and Neuroscience (CamCAN) study. From these data, the inference provided by diffusion anisotropy measures on maturation and degeneration processes is shown to be biased by age-related changes of fibre organization. Inconsistencies of previous NODDI ageing studies are also revealed to be associated with the different age ranges covered. The CamCAN data is also processed using a novel non-Gaussian diffusion characterization technique which is invariant to different fibre configurations. Results show that this technique can provide indices specific to axonal water fraction which can be linked to age-related fibre density changes.

In this thesis, we present an experimental and numerical study of globularization during reheating of thixocast billet having non-dendritic microstructure. The process of reheating is an important step in the semisolid processing and is essential to control its microstructure and hence its mechanical properties. Material chosen for this study is Aluminum alloy, A356. The primary focus of this study is the heat treatment below eutectic temperature i.e. transformation in solid phase. It is found that during short duration heat treatment, globularization of primary α grains and spheroidization of eutectic Si flakes take place which improves the mechanical properties of semisolid cast products significantly. A prolonged heat treatment is found to degrade the properties of castings since it enhances the porosity and coarsening of Si. The study suggests that a precise heat treatment practice can be designed to improve the semisolid microstructure. A computational model based on Phase field approach has been proposed to study this phenomena. Predictions based on this model are qualitatively compared with corresponding experimental observations. Since eutectics form an important step in multiphase solidification, an attempt has been made to develop an enthalpy based explicit micro-scale model for eutectic solidification. In this preliminary study, growth of adjacent α and β phases in a two dimensional Eulerian framework has been simulated. The model is qualitatively validated with Jackson Hunt theory. Results show expected eutectic growth. This methodology promises significant saving in computational time compared to existing numerical models.