Thermophoresis particles deposition is a fundamental procedure of micro-particles transmission through temperature gradient and has major significance in aerosol and electronic technologies. To observe the variation of mass deposition, the phenomenon of thermophoresis is encountered in time-dependent electrically conducting flow of viscous fluid by rotating disk which also oscillates periodically. The swirl flow finds significance in hydraulic pumps and conical diffusers. The fluid model initially based upon the system that comprises on the partial differential equations (PDEs) and then re-framed into the ordinary differential equations (ODEs) by similar transformations. To obtain the numerical results in graphical and tabular formats, we first apply the finite difference scheme on governing equations and then implemented successive over relaxation (SOR) method for faster convergence. Statistical approach is also executed to discuss the Pearson’s correlation and probable error for the emerging physical parameters. Three-dimensional flow graphs, contour plots and scatter plots of shear stresses are also drawn. The oscillatory behavior of radial curves is reduced by increasing the magnetic parameter. Thermophoretic co-efficient and relative temperature difference parameters demonstrated the opposite impact on oscillatory concentration field. A moderate positive correlation is noticed between swirl parameter and radial shear stress.

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The inverse exponential distribution is widely used in the field of reliability. In this article, we present a generalization of the inverse exponential distribution in formation of Topp-Leone odd log-logistic inverse exponential distribution. We provide a comprehensive account of some mathematical properties of the Topp-Leone odd log-logistic inverse exponential distribution. The possible shapes of the corresponding probability density function and hazard function are obtained and graphical demonstration are presented. The distribution is found to be unimodal. The results for moment, moment-generating function, and probability-generating function are computed. The residual and reversed residual functions are also obtained. The proposed method of maximum likelihood is used for the estimation of model parameters. The performance of the parameters is investigated through simulation. The usefulness of the proposed model is illustrated by means of a real data set.

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