Characteristics of melting heat transport of blood with time-dependent cross-nanofluid model using Keller–Box and BVP4C method

Ayub, Assad; Sabir, Zulqurnain; Altamirano, Gilder Cieza; Sadat, R.; Ali, Mohamed R.

doi:10.1007/s00366-021-01406-7

Cited by 68 publications

(17 citation statements)

References 29 publications

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“…The stochastic computing processes have been executed to solve a large variety of nonlinear systems, few of them are fractional singular systems (Sabir et al 2021c , d , e ), like higher order singular systems (Ayub et al 2021 ; Sabir et al 2021a ), dengue fever system (Umar et al 2020b , c , d , e ), SITR based COVID-19 models (Umar et al 2020b , c , d , e , 2021a , b ), delay singular function system (Khan et al 2021 ; Sabir et al 2021b ), SIR system for spreading infection and treatment (Umar et al 2021a , b ), mosquito release system in the heterogeneous environment (Umar et al 2020a ), doubly singular nonlinear models (Raja et al 2019 ; Sabir et al 2020a , b , c ), rank-constrained spectral clustering (Li et al 2018a , b ), zero-shot event detection system (Li et al 2019 ), fuzzy K-means clustering associated discriminative embedding scheme (Li et al 2018a , b ), multiclass classification systems (Yan et al 2020 ). dynamic affinity graph construction strategy for spectral clustering (Nie et al 2020 ), enhanced multilayer piezoelectric transducer design (Naz et al 2021 ), performance investigation of the heat sink of functionally graded material of the porous fin (Ahmad et al 2021 ), impact of heat transfer in a Bodewadt flow model (Awais et al 2021 ), thin film flow model over a stretched surface (Uddin et al 2021 ) and state estimation problems arising in underwater Markov chain maneuvering targets (Ali et al 2021 ).…”

Section: Introductionmentioning

confidence: 99%

Gudermannian neural networks using the optimization procedures of genetic algorithm and active set approach for the three-species food chain nonlinear model

Sabir

Ali

Sadat

2022

J Ambient Intell Human Comput

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The present study is to investigate the Gudermannian neural networks (GNNs) using the optimization procedures of genetic algorithm and active-set approach (GA-ASA) to solve the three-species food chain nonlinear model. The three-species food chain nonlinear model is dependent upon the prey populations, top-predator, and specialist predator. The design of an error-based fitness function is presented using the sense of the three-species food chain nonlinear model and its initial conditions. The numerical results of the model have been obtained by exploiting the GNN-GA-ASA. The obtained results through the GNN-GA-ASA have been compared with the Runge–Kutta method to substantiate the correctness of the designed approach. The reliability, efficacy and authenticity of the proposed GNN-GA-ASA are examined through different statistical measures based on single and multiple neurons for solving the three-species food chain nonlinear model.

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Section: Introductionmentioning

confidence: 99%

Gudermannian neural networks using the optimization procedures of genetic algorithm and active set approach for the three-species food chain nonlinear model

Sabir

Ali

Sadat

2022

J Ambient Intell Human Comput

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“…A shooting technique connecting with R-K 4th order mechanism is utilized to solve Equations ( 7)-( 9) with boundary conditions (10) in MATLAB. During the procedure, we have taken the following notations.…”

Section: Solution Proceduresmentioning

confidence: 99%

Significance of heat source/sink on the radiative flow of Cross nanofluid across an exponentially stretching surface towards a stagnation point with chemical reaction

Babu

Parandhama²,

Vijaya

2021

Heat Trans

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A numerical review on magnetohydrodynamics radiative motion of Cross nanofluid across an exponentially stretchable surface near stagnation point with varying heat source/sink is addressed. Brownian movement and thermophoretic impacts are assumed. The governing equations for this study are first altered as a system of ordinary differential equations by similarity transformation. With an aid of the Runge–Kutta 4th order mechanism together with the shooting procedure, the impacts of several pertinent parameters including chemical reaction on regular profiles (velocity, temperature, and concentration) are explicated. The consequences of the same parameters on surface drag force, transfer rates of heat, and mass are visualized in tables. From the analysis, it was noticed that the magnetic field parameter enhances the temperature and decreases the velocity of the Cross nanofluid. Also, fluid temperature is an increasing function with thermal radiation and nonuniform heat source/sink. The rate of heat transfer is increased with thermophoresis and diminished with Brownian motion. Sherwood's number is diminished with Brownian motion but it was boosted up with thermophoresis. The present results are compared with published results and those are in agreement.

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“…The designed measures of GNN-GA-AST have never been implemented nor applied to solve the nonlinear FO-IMM. Few current proposals of the stochastic solvers are doubly singular models [17], biological prey-predator network [18], mosquito release system [19], singular Thomas-Fermi system [20], a nonlinear functional form of the singular systems [21][22], COVID-19 SITR system [23], SIR nonlinear dengue fever model [24], three-point differential model [25], heat conduction model [26], periodic boundary value singular problems [27] and infection HIV model [28]. The motive of the current work is to solve numerically the nonlinear FO-IMM using the stochastic procedures of GNN-GA-AST.…”

Section: Introductionmentioning

confidence: 99%

Investigations of non-linear induction motor model using the Gudermannian neural networks

et al. 2022

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This study aims to solve the nonlinear fifth-order induction motor model (FO-IMM) using the Gudermannian neural networks (GNNs) 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). GNNs are executed to discretize the nonlinear 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 nonlinear 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.

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Characteristics of melting heat transport of blood with time-dependent cross-nanofluid model using Keller–Box and BVP4C method

Cited by 68 publications

References 29 publications

Gudermannian neural networks using the optimization procedures of genetic algorithm and active set approach for the three-species food chain nonlinear model

Gudermannian neural networks using the optimization procedures of genetic algorithm and active set approach for the three-species food chain nonlinear model

Significance of heat source/sink on the radiative flow of Cross nanofluid across an exponentially stretching surface towards a stagnation point with chemical reaction

Investigations of non-linear induction motor model using the Gudermannian neural networks

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