Yu-Ming Chu

Bio: Yu-Ming Chu is an academic researcher from Changsha University of Science and Technology. The author has contributed to research in topics: Computational intelligence & Mean squared error. The author has an hindex of 2, co-authored 2 publications receiving 19 citations.

Design of neural network based intelligent computing for neumerical treatment of unsteady 3D flow of Eyring-Powell magneto-nanofluidic model

Abstract: In the presented study, a novel application of intelligent numerical computing solver based on neural networks backpropagated with the Levenberg-Marquard scheme (NN-BLMS) is presented to interpret the chemical reactions and activation energy in unsteady 3D flow of Eyring-Powell magneto-nanofluidic system for convective heat and mass flux scenarios. The original nonlinear coupled PDEs representing the Eyring-Powell magneto-nanofluidic model (EPMNM) is transformed to an equivalent nonlinear ODEs system by exploiting similarity variables. A dataset for the proposed NN-BLMS is generated for different scenarios of EPMNM by variation of radiation, temperature ratio parameter, heat generation, Brownian motion and thermophoresis parameters by using Adam numerical method. The training, testing, and validation processes of NN-BLMS are performed to determine the approximate solution of EPMNM for different cases and comparison with reference results to verify the correctness of the proposed NN-BLMS. The performance of the proposed NN-BLMS to effectively solve the EPMNM is endorsed through mean squared error, histogram studies and regression analysis. The close matching of the proposed and reference results based on error analysis form level 10−05 to 10-07 validates the correctness of the proposed methodology.

Computational intelligence of Levenberg-Marquardt backpropagation neural networks to study the dynamics of expanding/contracting cylinder for Cross magneto-nanofluid flow model

Abstract: In the present investigation, design of integrated numerical computing through Levenberg-Marquardt backpropagation neural network (LMBNN) is presented to examine the fluid mechanics problems governing the dynamics of expanding and contracting cylinder for Cross magneto-nanofluid flow (ECCCMNF) model in the presence of time dependent non-uniform magnetic force and permeability of the cylinder. The original system model ECCCMNF in terms of PDEs is converted to nonlinear ODEs by introducing the similarity transformations. Reference dataset of the designed LMBNN methodology is formulated with Adam numerical technique for scenarios of ECCCMNF by variation of thermophoresis temperature ratio parameter, Brownian motion, suction parameters as well as Schmidt, Prandtl, local Weissenberg and Biot numbers. To calculate the approximate solution for ECCCMNF for different scenarios, the training, testing, and validation processes are conducted in parallel to adapt neural network by reducing the mean square error (MSE) function through Levenberg-Marquardt backpropagation. The comparative studies and performance analyses based on outcomes of MSE, error histograms, correlation and regression demonstrate the effectiveness of designed LMBNN technique.

Effects of Variable Transport Properties on Heat and Mass Transfer in MHD Bioconvective Nanofluid Rheology with Gyrotactic Microorganisms: Numerical Approach

Abstract: Rheology of MHD bioconvective nanofluid containing motile microorganisms is inspected numerically in order to analyze heat and mass transfer characteristics. Bioconvection is implemented by combined effects of magnetic field and buoyancy force. Gyrotactic microorganisms enhance the heat and transfer as well as perk up the nanomaterials’ stability. Variable transport properties along with assisting and opposing flow situations are taken into account. The significant influences of thermophoresis and Brownian motion have also been taken by employing Buongiorno’s model of nanofluid. Lie group analysis approach is utilized in order to compute the absolute invariants for the system of differential equations, which are solved numerically using Adams-Bashforth technique. Validity of results is confirmed by performing error analysis. Graphical and numerical illustrations are prepared in order to get the physical insight of the considered analysis. It is observed that for controlling parameters corresponding to variable transport properties c2, c4, c6, and c8, the velocity, temperature, concentration, and bioconvection density distributions accelerates, respectively. While heat and mass transfer rates increases for convection parameter and bioconvection Rayleigh number, respectively.

Design of Morlet wavelet neural network for solving the higher order singular nonlinear differential equations

Abstract: The aim of this study is to present the numerical solutions of the higher order singular nonlinear differential equations using an advanced intelligent computational approach by manipulating the Morlet wavelet (MW) neural networks (NNs), global approach as genetic algorithm (GA) and quick local search approach as interior-point method (IPM), i.e., GA-IPM. MWNNs is applied to discretize the higher order singular nonlinear differential equations to express the activation function using the mean square error. The performance of the designed MWNNs using the GA-IPM is observed to solve three different variants based on the higher order singular nonlinear differential model to check the significance, efficacy and consistency of the designed MWNNs using the GA-IPM. Furthermore, statistical performances are provided to check the precision, accuracy and convergence of the present approach.

Intelligent computing for the dynamics of entropy optimized nanofluidic system under impacts of MHD along thick surface

Abstract: This article examines entropy production (EP) of magneto-hydrodynamics viscous fluid flow model (MHD-VFFM) subject to a variable thickness surface with heat sink/source effect by utilizing the inte...