7,127 research outputs found

    Subdivision Directional Fields

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    We present a novel linear subdivision scheme for face-based tangent directional fields on triangle meshes. Our subdivision scheme is based on a novel coordinate-free representation of directional fields as halfedge-based scalar quantities, bridging the finite-element representation with discrete exterior calculus. By commuting with differential operators, our subdivision is structure-preserving: it reproduces curl-free fields precisely, and reproduces divergence-free fields in the weak sense. Moreover, our subdivision scheme directly extends to directional fields with several vectors per face by working on the branched covering space. Finally, we demonstrate how our scheme can be applied to directional-field design, advection, and robust earth mover's distance computation, for efficient and robust computation

    Modeling, Analysis, and Optimization Issues for Large Space Structures

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    Topics concerning the modeling, analysis, and optimization of large space structures are discussed including structure-control interaction, structural and structural dynamics modeling, thermal analysis, testing, and design

    Forward uncertainty quantification and sensitivity analysis in models of systemic circulation

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    The intricate nature of the heart and blood circulation is intensively studied in the search for answers and insights capable of maturing the understanding of the cardiovascular system’s physiological and pathophysiological phenomena. Cardiovascular computational models are useful tools for this purpose. They are already widely used by the medical-scientific community, simulating important phenomena such as the dynamics of the systemic circulation and providing valuable information, such as hemodynamic parameters and biomarkers, of common clinical use. However, the clinical application of these models is not straightforward, and for them to be used ubiquitously for decision-making, there is still much to be improved. An important step in this direction is to search for more accurate and reliable models, where the understanding of the relationship between the uncertainties in the input parameters of a model and the precision of its results must be taken into account. In the present work, we verify the effect of the propagation of uncertainties on the input parameters of lumped parameter models and a multi-scale finite element model that simulates the systemic circulation dynamics. For this, we perform forward uncertainty quantification and sensitivity analysis based on the polynomial chaos expansion. The results obtained point to the most influential parameters in the prediction of quantities of interest of clinical relevance. Thus, it is expected that the knowledge acquired on the parameters that must be measured with greater precision and the least influential ones, which can be measured from population-based values or the literature, can help in the calibration and development of more accurate and consistent models.A intrincada natureza do coração e da circulação sanguínea é intensamente estudada na busca de respostas e insights capazes de amadurecer a compreensão dos fenômenos fisiológicos e patofisiológicos do sistema cardiovascular. Modelos computacionais cardiovasculares são ferramentas úteis para este fim e já são amplamente utilizados pela comunidade médico-científica, sendo capazes de simular fenômenos importantes como as dinâmicas da circulação sistêmica e fornecer informações valiosas, como parâmetros hemodinâmicos e biomarcadores, de habitual uso clínico. Entretanto, a aplicação destes modelos em cenários clínicos não se dá facilmente, e para que sejam usados de forma ubíqua para a tomada de decisão ainda há muito o que se aprimorar. Um importante passo neste sentido se dá na busca por modelos mais precisos e confiáveis, onde deve-se tomar em conta o entendimento da relação entre as incertezas nos parâmetros de entrada de um modelo e a precisão de seus resultados. No presente trabalho, verificamos o efeito da propagação de incertezas nos parâmetros de entradas de modelos de parâmetros condensados e um modelo de elementos finitos multi-escala que simulam as dinâmicas da circulação sistêmica. Para isto, realizamos a quantificação de incertezas direta e análise de sensibilidade baseadas na expansão por caos polinomial e os resultados obtidos apontam para os parâmetros mais influentes na predição de quantidades de interesse de relevância clínica. Desta forma, espera-se que os conhecimentos adquiridos sobre os parâmetros que devem ser medidos com maior precisão, bem como os menos influentes, que podem ser medidos a partir de valores de base populacional ou da literatura, possam ajudar na calibragem e desenvolvimento de modelos mais precisos e consistentes.CAPES - Coordenação de Aperfeiçoamento de Pessoal de Nível Superio

    Modern control concepts in hydrology

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    Two approaches to an identification problem in hydrology are presented based upon concepts from modern control and estimation theory. The first approach treats the identification of unknown parameters in a hydrologic system subject to noisy inputs as an adaptive linear stochastic control problem; the second approach alters the model equation to account for the random part in the inputs, and then uses a nonlinear estimation scheme to estimate the unknown parameters. Both approaches use state-space concepts. The identification schemes are sequential and adaptive and can handle either time invariant or time dependent parameters. They are used to identify parameters in the Prasad model of rainfall-runoff. The results obtained are encouraging and conform with results from two previous studies; the first using numerical integration of the model equation along with a trial-and-error procedure, and the second, by using a quasi-linearization technique. The proposed approaches offer a systematic way of analyzing the rainfall-runoff process when the input data are imbedded in noise

    Application of Lumping Analysis inModelling the Living Systems –A Trade-off Between Simplicity and Model Quality

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    General chemical engineering modelling principles are valuable tools to represent both stationary and dynamic characteristics of complex cell processes. Elaboration of reduced (lumped) dynamic models uses all types of information ‘translated’ from the ‘language’ of molecular biology to that of mechanistical chemistry, by preserving cell structural hierarchy and component functions. A combination of non-/conventional estimation methods reported significant model quality improvements by accounting for qualitative/ quantitative data and global properties of the living system. Derivation of a satisfactory model is closely related to the ability of selecting the suitable lumping rules, key-parameters, and influential terms that better realize a trade-off between model simplicity and its predictive quality. Several examples, on the modular modelling of protein synthesis regulation, genetic regulatory networks, and on the successive drug-ligand release in human plasma from a complex multivalent support, illustrate the advantages but also the over-simplifications introduced by various lumping rules
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