Mathematics & Statistics

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    A non-monotonic convergence analysis of population clusters of random numbers.
    (NUST, 2012) Obabueki, B. E.; Reju, S. A.
    The standard deviation of a population (of size N ) is a measure of the spread of the population observations about the mean. A population may be clustered and the standard deviation of each cluster calculated. This paper looked at how the mean of the standard deviations of the clusters of a population of random numbers relate to the standard deviation of the population as the size of the clusters increased. We assumed that all clusters have the same size.
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    Entropy generation analysis in a variable viscosity MHD channel flow with permeable walls and convective heating.
    (Hindawi Publishing Corporation, 2013) Eegunjobi, A. S.; Makinde, O. D.
    This paper examines the effects of the thermodynamic second law on steady flow of an incompressible variable viscosity electrically conducting fluid in a channel with permeable walls and convective surface boundary conditions.The nonlinear model governing equations are solved numerically using shooting quadrature. Numerical results of the velocity and temperature profiles are utilised to compute the entropy generation number and the Bejan number.The results revealed that entropy generation minimization can be achieved by appropriate combination of the regulated values of thermophysical parameters controlling the flow systems.
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    A collocation multistep methods for integrating ordinary differential equations on manifolds.
    (Academic Journals, 2009) Fatokun, J. O; Ajibola, I. K. O.
    This paper concerns a family of generalized collocation multistep methods that evolves the numerical solution of ordinary differential equations on configuration spaces formulated as homogeneous manifolds. Collocating the general linear method at x x for k s n k = = 0,1,... + , we obtain the discrete scheme which can be adapted to homogeneous spaces. Varying the values of k in the collocation process, the standard Munthe-Kass (k = 1) and the linear multistep methods (k = s) are recovered. Any classical multistep methods may be employed as an invariant method and the order of the invariant method is as high as in the classical setting. In this paper an implicit algorithm was formulated and two approaches presented for its implementation.