Sabir, Zulqurnain; Akkurt, Nevzat; Ben Said, Salem

The aim of this work is to provide the numerical solutions of the fluid model by using the stochastic computing paradigms. The linear/exponential stretching sheets on magneto-rotating flow based on the Maxwell nanofluid have been provided using the Buongiorno model with the impacts of uneven heat source/sink, varying thermal conductivity and reactive species. The solutions of this transformed ordinary differential exponential stretching sheet model have been presented using a novel ‘radial basis’ (RB) activation function together with the Bayesian regularization deep neural network (BRDNN), i.e., RB-BRDNN. The deep neural network is presented into two hidden layers, while thirteen and twenty-five numbers of neurons have been used in the first and second layer. A reference dataset is proposed using the Runge-Kutta scheme for the model. The correctness of the stochastic RB-BRDNN procedure is examined through the comparison of proposed and database results, whereas minimal absolute error values provide the accuracy of the scheme. The reliability and competence of the computing RB-BRDNN solver is authenticated using the state transitions, correlation, regression, and error histograms.

AtıfScopus33·WoS30·OpenAlex36

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Naveed Khan, Muhammad; Akkurt, Nevzat; Ameer Ahammad, N.; Ahmad, Shafiq; Amari, Abdelfattah; Eldin, Sayed M; Ali Pasha, Amjad

This paper presents the theoretical and computation irreversibility investigation of the Ellis hybrid nanofluid flow (CuO-Al2O3 nanoparticles) model using both homogeneous and heterogeneous responses toward the horizontal porous stretching cylinder. The Joule heating, thermal radiation, and heat generation/absorption are all factors that affect thermal energy transport, all of which are explored. On the surface of the cylinder, the slip boundary conditions are enforced. Keeping in mind that the nonlinear partial differential equations (PDEs) are converted into nonlinear ordinary differential equations (ODEs), these ODEs are constructed from the mathematical flow model with the aid of proper transformations. With the help of the bvp4c MATLAB built-in method, the nonlinear system equations are tacked numerically. Graphical results are used to discuss the numerical aspects of different parameters. By increasing the curvature parameter, entropy generation increases while fluid velocity decreases. Elasticity and magnetic fields thin boundary layers. Realistically, as liquid porosity increases, momentum boundary layer diminishes.

AtıfScopus36·OpenAlex35

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