Relevance of various cavity aspect ratios to the thermal behavior of natural convection heat transfer for water-based hybrid nanofluid within a U-shaped enclosure

The investigation of the effect of the U-shaped cavity aspect ratio on the natural convection heat transfer of copper-alumina/pure water hybrid nanofluid is presented in the manuscript. Various aspect ratio of the enclosure is examined with fixed parameters to observe their effect on the overall thermal performance within the cavity. The dimensionless governing equations are formed, together with the thermophysical properties of hybrid nanofluid which are coupled and solved using the Galerkin weighted residuals finite element method bounded by the boundary conditions. A damped Newton-Raphson algorithm is employed as the convergence criterion to the hon-linear governing equations. Numerical and experimental comparisons between the present numerical results and previously published findings are conducted to ensure the solution's validity. An independent mesh test is done to ascertain the solution's independence from the meshing formation. The results are presented in the form of graphs, streamlines, and isotherms to see the effect of various parameters such as nanoparticle volume fraction and its ratios, Rayleigh number, and enclosure aspect ratios, and the discussion on the results is extensively presented. The present investigation is novel in terms of one heating wall instead of three heating walls in the previously published papers. It is hoped the findings in this manuscript will benefit heat transfer management in industries such as laptop and smartphone manufacturing

Escape Criteria Using Hybrid Picard S-Iteration Leading to a Comparative Analysis of Fractal Mandelbrot Sets Generated with S-Iteration

Fractals are a common characteristic of many artificial and natural networks having topological patterns of a self-similar nature. For example, the Mandelbrot set has been investigated and extended in several ways since it was first introduced, whereas some authors characterized it using various complex functions or polynomials, others generalized it using iterations from fixed-point theory. In this paper, we generate Mandelbrot sets using the hybrid Picard S-iterations. Therefore, an escape criterion involving complex functions is proved and used to provide numerical and graphical examples. We produce a wide range of intriguing fractal patterns with the suggested method, and we compare our findings with the classical S-iteration. It became evident that the newly proposed iteration method produces novel images that are more spontaneous and fascinating than those produced by the S-iteration. Therefore, the generated sets behave differently based on the parameters involved in different iteration schemes

Heat transfer over a steady stretching surface in the presence of suction

The purpose of this paper is to present the Cattaneo–Christov heat flux model for Maxwell fluid past a stretching surface where the presence of suction/injection is taken into account. The governing system of equations is reduced to the ordinary differential equations with the boundary conditions by similarity transformation. These equations are then solved numerically by two approaches, Haar wavelet quasilinearization method (HWQM) and Runge–Kutta–Gill method (RK Gill). The behavior of various pertinent parameters on velocity and temperature distributions is analyzed and discussed. Comparison of the obtained numerical results is made between both methods and with the existing numerical solutions found in the literature, and reasonable agreement is noted

Forecasting sunspot numbers with Feedforward Neural Networks (FNN) using 'sunspot neural forecaster' system

This paper presents the investigations of forecasting performance of different type of Feedforward Neural Networks (FNN) in forecasting the sunspot numbers. Feedforward Neural Network will be used in this investigation by using different learning algorithms, sunspot data models and FNN transfer functions. Simulations are done using Matlab 7 where customized Graphic User Interface (GUI) called `Sunspot Neural Forecaster' have been developed for analysis. A complete analysis for different learning algorithms, sunspot data models and FNN transfer functions are examined in terms of Mean Square Error (MSE) and correlation analysis. Finally, the best optimized FNN parameters will be used to forecast the sunspot numbers

Soret and Dufour effects on doubly diffusive convection of nanofluid over a wedge in the presence of thermal radiation and suction

This paper investigates the effects of thermal radiation, Dufour, and Soret effects on doubly diffusive convective heat transfer of nanoliquid over a wedge in the presence of wall suction. The governing equations are transformed to nonlinear ordinary differential equations using similarity transformation. The resulting system is solved numerically by the fourth-order Runge-Kutta-Gill method with a shooting technique and a Newton-Raphson method. The solutions are expressed in terms of velocity, temperature, solutal concentration, and volume fraction profiles. The effects of pertinent parameters involved in the problem such as wedge angle, thermal radiation, Brownian motion, thermophoresis, Soret number, and Dufour number on the skin friction coefficient, local Nusselt number, and local Sherwood number are discussed in detail. © 2019 Sharif University of Technology. All rights reserved