Browsing by Author "Ali, Mohamed R."

Now showing 1 - 5 of 5

Citation - WoS: 8
Citation - Scopus: 13
Dynamics of multi-point singular fifth-order Lane–Emden system with neuro-evolution heuristics
(Springer Heidelberg, 2022) Sabir, Zulqurnain; Baleanu, Dumitru; Ali, Mohamed R.; Fathurrochman, Irwan; Raja, Muhammad Asif Zahoor; Sadat, R.; Baleanu, Dumitru; 56389; Matematik
The objective of the presented communication is to examine and analyze the solutions of nonlinear multi-singular fifth-order Lane-Emden (LE) system for different scenarios by variation of shape factors settled on the equivalent design of the LE equations. The neuro-evolution based stochastic computing is explored for the numerical measures using the artificial neural networks (ANNs) models for the appropriate continuous mapping, while the learning of decision variables is conducted using the integrated meta-heuristic global search of genetic algorithms (GA) hybrid with the local search efficiency of active-set (AS) i.e., ANN-GA-AS scheme. The numerical approach ANN-GA-AS is applied efficiently for the fifth kind of nonlinear LE model and statistical calculations further validate the accuracy, robustness as well as convergence.
Citation - WoS: 22
Citation - Scopus: 18
Haar wavelets scheme for solving the unsteady gas-flow in 4-D
(Vinca inst Nuclear Sci, 2020) Ali, Mohamed R.; Baleanu, Dumitru; Baleanu, Dumitru; 56389; Matematik
The system of unsteady gas-flow of 4-D is solved successfully by alter the possibility of an algorithm based on collocation points and 4-D Haar wavelet method. Empirical rates of convergence of the Haar wavelet method are calculated which agree with theoretical results. To exhibit the efficiency of the strategy, the numerical solutions which are acquired utilizing the recommended strategy demonstrate that numerical solutions are in a decent fortuitous event with the exact solutions.
Citation - WoS: 7
Citation - Scopus: 7
Investigations Of Non-Linear Induction Motor Model Using The Gudermannıan Neural Networks
(Vinca inst Nuclear Sci, 2022) Sabir, Zulqurnain; Baleanu, Dumitru; Raja, Muhammad Asif Zahoor; Baleanu, Dumitru; Sadat, Rahma; Ali, Mohamed R.; 56389; Matematik
This study aims to solve the non-linear fifth-order induction motor model (FO-IMM) using the Gudermannian neural networks (GNN) along with the optimization procedures of global search as a genetic algorithm together with the quick local search process as active-set technique (GNN-GA-AST). The GNN are executed to discretize the non-linear FO-IMM to prompt the fitness function in the procedure of mean square error. The exactness of the GNN-GA-AST is observed by comparing the obtained results with the reference results. The numerical performances of the stochastic GNN-GA-AST are provided to tackle three different variants based on the non-linear FO-IMM to authenticate the consistency, significance and efficacy of the designed stochastic GNN-GA-AST. Additionally, statistical illustrations are available to authenticate the precision, accuracy and convergence of the designed stochastic GNN-GA-AST.
Citation - WoS: 6
Citation - Scopus: 8
New wavelet method for solving boundary value problems arising from an adiabatic tubular chemical reactor theory
(World Scientific Publ Co Pte Ltd, 2020) Ali, Mohamed R.; Baleanu, Dumitru; Baleanu, Dumitru; 56389; Matematik
This paper displays an efficient numerical technique of realizing mathematical models for an adiabatic tubular chemical reactor which forms an irreversible exothermic chemical reaction. At a steady-state solution for an adiabatic rounded reactor, the model can be diminished to a conventional nonlinear differential equation which converts into a system of the nonlinear equation that can proceed numerically utilizing Newton's iterative method. An operational matrix of coordination is derived and is utilized to decrease the model for an adiabatic tubular chemical reactor to an arrangement of algebraic equations. Simple execution, basic activities, and precise arrangements are the fundamental highlights of the proposed wavelet technique. The numerical solutions attained by the present technique have been contrasted and compared with other techniques.
Citation - WoS: 29
Citation - Scopus: 33
The method of lines for solution of the carbon nanotubes engine oil nanofluid over an unsteady rotating disk
(Springer Heidelberg, 2020) Baleanu, Dumitru; Baleanu, Dumitru; Sadat, R.; Ali, Mohamed R.; 56389; Matematik
The main target of the present model is to find the idea of magneto-hydrodynamics incompressible nanofluid flow past over an infinite rotating disk. The effect of the magnetic field is existed to check the nanofluid flow. The single-walled carbon nanotubes (SWCNTs) and multi-walled carbon nanotubes (MWCNTs) engine oil can be utilized despite carrier fluid for an unsteady rotating disk. Here, we transform the nonlinear system of differential equations to the dimensionless ordinary differential equation by using similarity transformation. Then, we use the numerical method of lines to solve the nonlinear ODE via the Runge-Kutta technique. The resultant of the velocity, Nusselt number and Skin friction is demonstrated under the effect of several factors. We note that when we increase the velocity of the rotating disk, fluid velocity and temperature are safely increased. Finally, we note that the outcomes obtained demonstrate that the SWCNTs nanofluids improved the heat transfer more than the MWCNTs.