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vol. 31 num. 3 lang. en<![CDATA[SciELO Logo]]>http://www.scielo.br/img/en/fbpelogp.gif
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<![CDATA[<b>Bézier control points method to solve constrained quadratic optimal control of time varying linear systems </b>]]>
http://www.scielo.br/scielo.php?script=sci_arttext&pid=S1807-03022012000300001&lng=en&nrm=iso&tlng=en
A computational method based on Bézier control points is presented to solve optimal control problems governed by time varying linear dynamical systems subject to terminal state equality constraints and state inequality constraints. The method approximates each of the system state variables and each of the control variables by a Bézier curve of unknown control points. The new approximated problems converted to a quadratic programming problem which can be solved more easily than the original problem. Some examples are given to verify the efficiency and reliability of the proposed method. Mathematical subject classification: 49N10.<![CDATA[<b>Ostrowski type inequalities for interval-valued functions using generalized Hukuhara derivative</b>]]>
http://www.scielo.br/scielo.php?script=sci_arttext&pid=S1807-03022012000300002&lng=en&nrm=iso&tlng=en
The present paper is devoted to obtaining some Ostrowski type inequalities for interval-valued functions. In this context we use the generalized Hukuhara derivative for interval-valued functions. Also some examples and consequences are presented. Mathematical subject classification: Primary: 26E25; Secondary: 35A23.<![CDATA[<b>Numerical analysis of the nonlinear subgrid scale method </b>]]>
http://www.scielo.br/scielo.php?script=sci_arttext&pid=S1807-03022012000300003&lng=en&nrm=iso&tlng=en
This paper presents the numerical analysis of the Nonlinear Subgrid Scale (NSGS) model for approximating singularly perturbed transport models. The NSGS is a free parameter subgrid stabilizing method that introduces an extra stability only onto the subgrid scales. Thisnew feature comes from the local control yielded by decomposing the velocity field into the resolved and unresolved scales. Such decomposition is determined by requiring the minimum of the kinetic energy associated to the unresolved scales and the satisfaction of the resolved scale model problem at element level. The developed method is robust for a wide scope of singularly perturbed problems. Here, we establish the existence and uniqueness of the solution, and provide an a priori error estimate. Convergence tests on two-dimensional examples are reported. Mathematical subject classification: Primary: 65N12; Secondary: 74S05.<![CDATA[<b>A weighted mass explicit scheme for convection-diffusion equations </b>]]>
http://www.scielo.br/scielo.php?script=sci_arttext&pid=S1807-03022012000300004&lng=en&nrm=iso&tlng=en
An explicit scheme based on a weighted mass matrix, for solving time-dependent convection-diffusion problems was recently proposed by the author and collaborators. Convenient bounds for the time step, in terms of both the method's weights and the mesh step size, ensure its stability in space and time, for piecewise linear finite element discretisations in any space dimension. In this work we study some techniques for choosing the weights that guarantee the convergence of the scheme with optimal order in the space-time maximum norm, as both discretisation parameters tend to zero. Mathematical subject classification: Primary: 65M60; Secondary: 76Rxx.<![CDATA[<b>How good are MatLab, Octave and Scilab for computational modelling?</b>]]>
http://www.scielo.br/scielo.php?script=sci_arttext&pid=S1807-03022012000300005&lng=en&nrm=iso&tlng=en
In this article we test the accuracy of three platforms used in computational modelling: MatLab, Octave and Scilab, running on i386 architecture and three operating systems (Windows, Ubuntu and Mac OS). We submitted them to numerical tests using standard data sets and using the functions provided by each platform. A Monte Carlo study was conducted in some of the datasets in order to verify the stability of the results with respect to small departures from the original input. We propose a set of operations which include the computation of matrix determinants and eigenvalues, whose results are known. We also used data provided by NIST (National Institute of Standards and Technology), a protocol which includes the computation of basic univariate statistics (mean, standard deviation and first-lag correlation), linear regression and extremes of probability distributions. The assessment was made comparing the results computed by the platforms with certified values, that is, known results, computing the number of correct significant digits. Mathematical subject classification: Primary: 06B10; Secondary: 06D05.<![CDATA[<b>Numerical solution of coupled mass and energy balances during osmotic microwave dehydration</b>]]>
http://www.scielo.br/scielo.php?script=sci_arttext&pid=S1807-03022012000300006&lng=en&nrm=iso&tlng=en
The mass and energy transfer during osmotic microwave drying (OD-MWD) process was studied theoretically by modeling and numerical simulation. With the aim to describe the transport phenomena that occurs during the combined dehydration process, the mass and energy microscopic balances were solved. An osmotic-diffusional model was used for osmotic dehydration (OD). On the other hand, the microwave drying (MWD) was modeled solving the mass and heat balances, using properties as function of temperature, moisture and soluble solids content. The obtained balances form highly coupled non-linear differential equations that were solved applying numerical methods. For osmotic dehydration, the mass balances formed coupled ordinary differential equations that were solved using the Fourth-order Runge Kutta method. In the case of microwave drying, the balances constituted partial differential equations, which were solved through Crank-Nicolson implicit finite differences method. The numerical methods were coded in Matlab 7.2 (Mathworks, Natick, MA). The developed mathematical model allows predict the temperature and moisture evolution through the combined dehydration process. Mathematical subject classification: Primary: 06B10; Secondary: 06D05.<![CDATA[<b>New conservation laws for inviscid Burgers equation</b>]]>
http://www.scielo.br/scielo.php?script=sci_arttext&pid=S1807-03022012000300007&lng=en&nrm=iso&tlng=en
In this paper it is shown that the inviscid Burgers equation is nonlinearly self-adjoint. Then, from Ibragimov's theorem on conservation laws, local conserved quantities are obtained. Mathematical subject classification: Primary: 76M60; Secondary: 58J70.<![CDATA[<b>An overview of flexibility and generalized uncertainty in optimization</b>]]>
http://www.scielo.br/scielo.php?script=sci_arttext&pid=S1807-03022012000300008&lng=en&nrm=iso&tlng=en
Two new powerful mathematical languages, fuzzy set theory and possibility theory, have led to two optimization types that explicitly incorporate data whose values are not real-valued nor probabilistic: 1) flexible optimization and 2) optimization under generalized uncertainty. Our aim is to make clear what these two types are, make distinctions, and show how they can be applied. Flexible optimization arises when it is necessary to relax the meaning of the mathematical relation of belonging to a set (a constraint set in the context of optimization). The mathematical language of relaxed set belonging is fuzzy set theory. Optimization under generalized uncertainty arises when it is necessary to represent parameters of a model whose values are only known partially or incompletely. A natural mathematical language for the representation of partial or incomplete information about the value of a parameter is possibility theory. Flexible optimization, as delineated here, includes much of what has been called fuzzy optimization whereas optimization under generalized uncertainty includes what has been called possibilistic optimization. We explore why flexible optimization and optimization under generalized uncertainty are distinct and important types of optimization problems. Possibility theory in the context of optimization leads to two distinct types of optimization under generalized uncertainty, single distribution and dual distribution optimization. Dual (possibility/necessity pairs) distribution optimization is new. Mathematical subject classification: 90C70, 65G40.<![CDATA[<b>Simulation results and applications of an advection bounded scheme to practical flows</b>]]>
http://www.scielo.br/scielo.php?script=sci_arttext&pid=S1807-03022012000300009&lng=en&nrm=iso&tlng=en
This paper reports experiments on the use of a recently introduced advection bounded upwinding scheme, namely TOPUS (Computers & Fluids 57 (2012) 208-224), for flows of practical interest. The numerical results are compared against analytical, numerical and experimental data and show good agreement with them. It is concluded that the TOPUS scheme is a competent, powerful and generic scheme for complex flow phenomena. Mathematical subject classification: Primary: 06B10; Secondary: 06D05.<![CDATA[<b>The Poisson-exponential regression model under different latent activation schemes</b>]]>
http://www.scielo.br/scielo.php?script=sci_arttext&pid=S1807-03022012000300010&lng=en&nrm=iso&tlng=en
In this paper, a new family of survival distributions is presented. It is derived by considering that the latent number of failure causes follows a Poisson distribution and the time for these causes to be activated follows an exponential distribution. Three different activationschemes are also considered. Moreover, we propose the inclusion of covariates in the model formulation in order to study their effect on the expected value of the number of causes and on the failure rate function. Inferential procedure based on the maximum likelihood method is discussed and evaluated via simulation. The developed methodology is illustrated on a real data set on ovarian cancer. Mathematical subject classification: 62N01, 62N99.