242 resultados para Calculo tensorial
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Mode of access: Internet.
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Mode of access: Internet.
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Contiene además en h. [4-5]:
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Mode of access: Internet.
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Mode of access: Internet.
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With this are bound: [Venette, Nicolas] Traité du scorbut. La Rochelle, 1671. - Copy 3 of Brunacci, Gaudenzio. De cina cina. Venetiis, 1661. - Menon, Dominique. L'ecole de Salerne. La Haye, 1695.
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Cruz, Ângela Maria Paiva. Os paradoxos de Prior e o cálculo proposicional deôntico relevante. Princípios, Natal, v. 4, p. 05-18, 1996.
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Cruz, Ângela Maria Paiva. Os paradoxos de Prior e o cálculo proposicional deôntico relevante. Princípios, Natal, v. 4, p. 05-18, 1996.
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El análisis tensorial tiene aplicaciones en el campo de la geometría diferencial de curvas y superficies en un espacio ordinario, así como la generalización a espacios de mayor dimensión o regularmente llamada geometría Rimaniana; otra aplicación es en la física matemática, en la cual, el análisis tensorial permite la formulación de las leyes naturales en términos de tensores los cuales son independientes de cualquier sistema coordenado en particular. Como los vectores son uno de los muchos tipos de tensores que hay, se dan conceptos básicos del análisis vectorial tales como: coordenadas cartesianas rectangulares, producto escalar y vectorial, diferenciación con respecto a una variable escalar y diferenciación parcial haciendo un desarrollo suciente del operador nabla. Se dan los principios fundamentales de los tensores los cuales son conceptos matemáticos o físicos que tienen ciertas leyes especícas que están relacionados con los cambios en el sistema coordenado y se llega hasta la ecuación de movimiento escrita en forma tensorial.
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The purpose of this paper is to develop a second-moment closure with a near-wall turbulent pressure diffusion model for three-dimensional complex flows, and to evaluate the influence of the turbulent diffusion term on the prediction of detached and secondary flows. A complete turbulent diffusion model including a near-wall turbulent pressure diffusion closure for the slow part was developed based on the tensorial form of Lumley and included in a re-calibrated wall-normal-free Reynolds-stress model developed by Gerolymos and Vallet. The proposed model was validated against several one-, two, and three-dimensional complex flows.
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This paper is concerned with recent advances in the development of near wall-normal-free Reynolds-stress models, whose single point closure formulation, based on the inhomogeneity direction concept, is completely independent of the distance from the wall, and of the normal to the wall direction. In the present approach the direction of the inhomogeneity unit vector is decoupled from the coefficient functions of the inhomogeneous terms. A study of the relative influence of the particular closures used for the rapid redistribution terms and for the turbulent diffusion is undertaken, through comparison with measurements, and with a baseline Reynolds-stress model (RSM) using geometric wall normals. It is shown that wall-normal-free rsms can be reformulated as a projection on a tensorial basis that includes the inhomogeneity direction unit vector, suggesting that the theory of the redistribution tensor closure should be revised by taking into account inhomogeneity effects in the tensorial integrity basis used for its representation.
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Identifying unusual or anomalous patterns in an underlying dataset is an important but challenging task in many applications. The focus of the unsupervised anomaly detection literature has mostly been on vectorised data. However, many applications are more naturally described using higher-order tensor representations. Approaches that vectorise tensorial data can destroy the structural information encoded in the high-dimensional space, and lead to the problem of the curse of dimensionality. In this paper we present the first unsupervised tensorial anomaly detection method, along with a randomised version of our method. Our anomaly detection method, the One-class Support Tensor Machine (1STM), is a generalisation of conventional one-class Support Vector Machines to higher-order spaces. 1STM preserves the multiway structure of tensor data, while achieving significant improvement in accuracy and efficiency over conventional vectorised methods. We then leverage the theory of nonlinear random projections to propose the Randomised 1STM (R1STM). Our empirical analysis on several real and synthetic datasets shows that our R1STM algorithm delivers comparable or better accuracy to a state-of-the-art deep learning method and traditional kernelised approaches for anomaly detection, while being approximately 100 times faster in training and testing.
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Several constitutive inequalities have been proposed in the literature to quantify the notion that ‘stress increases with strain’ in an elastic material. Due to some inherent shortcomings in them, which we discuss, we propose a new tensorial criterion for isotropic materials. We also present necessary conditions in terms of elasticity tensors for the onset of elastic instabilities.
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Based on the internal variable theory, a viscoelastic constitutive model of a highly deformable continuous medium is proposed. A set of second rank tensorial internal state variables corresponding to Biot's strain is introduced, and a nonlinear evolution